diff --git "a/test_model.json" "b/test_model.json"
new file mode 100644--- /dev/null
+++ "b/test_model.json"
@@ -0,0 +1,33217 @@
+[
+  {
+    "Query": "Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Display the list of all unique providers.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "How many claim types are available?",
+    "pandas_code": "df['Claim_Type'].nunique()"
+  },
+  {
+    "Query": "List all products covered in this dataset.",
+    "pandas_code": "df['Product'].unique()"
+  },
+  {
+    "Query": "Show number of claims per status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "List all unique schemes available.",
+    "pandas_code": "df['Scheme'].unique()"
+  },
+  {
+    "Query": "Show count of claims by each provider.",
+    "pandas_code": "df['Provider_Name'].value_counts()"
+  },
+  {
+    "Query": "Show total Approved_Amount per Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Average Claim_Amount for each Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Highest Approved_Amount among all claims.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "Minimum Deduction_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Sum of Deduction_Amount by Status.",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum()"
+  },
+  {
+    "Query": "Show average Approved_Amount by Scheme.",
+    "pandas_code": "df.groupby('Scheme')['Approved_Amount'].mean()"
+  },
+  {
+    "Query": "Calculate how many days between Loss_Date and Claim_Created_Date for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Find the claim with the longest duration between loss and claim creation.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Show average delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Which claim has the shortest duration between loss and creation?",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmin()]"
+  },
+  {
+    "Query": "Which claims have missing Approved_Amount?",
+    "pandas_code": "df[df['Approved_Amount'].isnull()]"
+  },
+  {
+    "Query": "Count of records missing Loss_Date.",
+    "pandas_code": "df['Loss_Date'].isnull().sum()"
+  },
+  {
+    "Query": "List columns with missing values.",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Which column has the most missing values?",
+    "pandas_code": "df.isnull().sum().idxmax()"
+  },
+  {
+    "Query": "Show total missing values in dataset.",
+    "pandas_code": "df.isnull().sum().sum()"
+  },
+  {
+    "Query": "Calculate claim balance as Claim_Amount minus Approved_Amount.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Show claims where Deduction_Amount is more than 10 percent of Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Find average difference between Claim_Amount and Approved_Amount by Product.",
+    "pandas_code": "(df['Claim_Amount'] - df['Approved_Amount']).groupby(df['Product']).mean()"
+  },
+  {
+    "Query": "List claims where Approved_Amount equals Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Which product has higher total Approved_Amount?",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().idxmax()"
+  },
+  {
+    "Query": "Which provider processed the fewest claims?",
+    "pandas_code": "df['Provider_Name'].value_counts().idxmin()"
+  },
+  {
+    "Query": "Which status type has the most claims?",
+    "pandas_code": "df['Status'].value_counts().idxmax()"
+  },
+  {
+    "Query": "Which product has the lowest average Approved_Amount?",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().idxmin()"
+  },
+  {
+    "Query": "What is the median Claim_Amount?",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Find standard deviation of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Is there a correlation between Claim_Amount and Approved_Amount?",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Show variance of Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].var()"
+  },
+  {
+    "Query": "Find 90th percentile of Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].quantile(0.9)"
+  },
+  {
+    "Query": "List all unique patient names.",
+    "pandas_code": "df['Patient_Name'].unique()"
+  },
+  {
+    "Query": "How many claims belong to Policy_Number 12345?",
+    "pandas_code": "df[df['Policy_Number'] == '12345'].shape[0]"
+  },
+  {
+    "Query": "Show claims for Policy_Number 54321.",
+    "pandas_code": "df[df['Policy_Number'] == '54321']"
+  },
+  {
+    "Query": "Show details of Claim_Number C1234.",
+    "pandas_code": "df[df['Claim_Number'] == 'C1234']"
+  },
+  {
+    "Query": "Get all claims for Provider_Code P5678.",
+    "pandas_code": "df[df['Provider_Code'] == 'P5678']"
+  },
+  {
+    "Query": "Find claim details where Claim_Id equals 10.",
+    "pandas_code": "df[df['Claim_Id'] == 10]"
+  },
+  {
+    "Query": "What columns are available in this dataset?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "How many records does the dataset have?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Describe the dataset structure.",
+    "pandas_code": "df.info()"
+  },
+  {
+    "Query": "Show first five records of dataset.",
+    "pandas_code": "df.head()"
+  },
+  {
+    "Query": "Show last ten records.",
+    "pandas_code": "df.tail(10)"
+  },
+  {
+    "Query": "Get total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Retrieve the list of all unique providers.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Get the list of all unique providers.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Display many claim types are available?",
+    "pandas_code": "df['Claim_Type'].nunique()"
+  },
+  {
+    "Query": "Fetch many claim types are available?",
+    "pandas_code": "df['Claim_Type'].nunique()"
+  },
+  {
+    "Query": "Retrieve all products covered in this dataset.",
+    "pandas_code": "df['Product'].unique()"
+  },
+  {
+    "Query": "Provide number of claims per status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Retrieve all unique schemes available.",
+    "pandas_code": "df['Scheme'].unique()"
+  },
+  {
+    "Query": "Provide all unique schemes available.",
+    "pandas_code": "df['Scheme'].unique()"
+  },
+  {
+    "Query": "Provide count of claims by each provider.",
+    "pandas_code": "df['Provider_Name'].value_counts()"
+  },
+  {
+    "Query": "Provide total Approved_Amount per Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Retrieve total Approved_Amount per Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Provide Claim_Amount for each Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Retrieve Approved_Amount among all claims.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "Fetch Deduction_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Display Deduction_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Retrieve of Deduction_Amount by Status.",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum()"
+  },
+  {
+    "Query": "Get of Deduction_Amount by Status.",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum()"
+  },
+  {
+    "Query": "Fetch average Approved_Amount by Scheme.",
+    "pandas_code": "df.groupby('Scheme')['Approved_Amount'].mean()"
+  },
+  {
+    "Query": "Retrieve average Approved_Amount by Scheme.",
+    "pandas_code": "df.groupby('Scheme')['Approved_Amount'].mean()"
+  },
+  {
+    "Query": "List how many days between Loss_Date and Claim_Created_Date for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Retrieve how many days between Loss_Date and Claim_Created_Date for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Show the claim with the longest duration between loss and claim creation.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Retrieve the claim with the longest duration between loss and claim creation.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Fetch average delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "List claim has the shortest duration between loss and creation?",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmin()]"
+  },
+  {
+    "Query": "Provide claim has the shortest duration between loss and creation?",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmin()]"
+  },
+  {
+    "Query": "Display claims have missing Approved_Amount?",
+    "pandas_code": "df[df['Approved_Amount'].isnull()]"
+  },
+  {
+    "Query": "Get claims have missing Approved_Amount?",
+    "pandas_code": "df[df['Approved_Amount'].isnull()]"
+  },
+  {
+    "Query": "Show of records missing Loss_Date.",
+    "pandas_code": "df['Loss_Date'].isnull().sum()"
+  },
+  {
+    "Query": "Display of records missing Loss_Date.",
+    "pandas_code": "df['Loss_Date'].isnull().sum()"
+  },
+  {
+    "Query": "Show columns with missing values.",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Retrieve column has the most missing values?",
+    "pandas_code": "df.isnull().sum().idxmax()"
+  },
+  {
+    "Query": "Show column has the most missing values?",
+    "pandas_code": "df.isnull().sum().idxmax()"
+  },
+  {
+    "Query": "List total missing values in dataset.",
+    "pandas_code": "df.isnull().sum().sum()"
+  },
+  {
+    "Query": "Get total missing values in dataset.",
+    "pandas_code": "df.isnull().sum().sum()"
+  },
+  {
+    "Query": "Fetch claim balance as Claim_Amount minus Approved_Amount.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Provide claim balance as Claim_Amount minus Approved_Amount.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "List claims where Deduction_Amount is more than 10 percent of Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Fetch claims where Deduction_Amount is more than 10 percent of Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Fetch average difference between Claim_Amount and Approved_Amount by Product.",
+    "pandas_code": "(df['Claim_Amount'] - df['Approved_Amount']).groupby(df['Product']).mean()"
+  },
+  {
+    "Query": "Display average difference between Claim_Amount and Approved_Amount by Product.",
+    "pandas_code": "(df['Claim_Amount'] - df['Approved_Amount']).groupby(df['Product']).mean()"
+  },
+  {
+    "Query": "Provide claims where Approved_Amount equals Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Fetch claims where Approved_Amount equals Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Provide product has higher total Approved_Amount?",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().idxmax()"
+  },
+  {
+    "Query": "Provide provider processed the fewest claims?",
+    "pandas_code": "df['Provider_Name'].value_counts().idxmin()"
+  },
+  {
+    "Query": "Get provider processed the fewest claims?",
+    "pandas_code": "df['Provider_Name'].value_counts().idxmin()"
+  },
+  {
+    "Query": "Display status type has the most claims?",
+    "pandas_code": "df['Status'].value_counts().idxmax()"
+  },
+  {
+    "Query": "Show status type has the most claims?",
+    "pandas_code": "df['Status'].value_counts().idxmax()"
+  },
+  {
+    "Query": "Show product has the lowest average Approved_Amount?",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().idxmin()"
+  },
+  {
+    "Query": "Provide product has the lowest average Approved_Amount?",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().idxmin()"
+  },
+  {
+    "Query": "Show is the median Claim_Amount?",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "List is the median Claim_Amount?",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Fetch standard deviation of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Show standard deviation of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Fetch there a correlation between Claim_Amount and Approved_Amount?",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Display there a correlation between Claim_Amount and Approved_Amount?",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Get variance of Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].var()"
+  },
+  {
+    "Query": "Retrieve variance of Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].var()"
+  },
+  {
+    "Query": "Show 90th percentile of Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].quantile(0.9)"
+  },
+  {
+    "Query": "Retrieve 90th percentile of Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].quantile(0.9)"
+  },
+  {
+    "Query": "Retrieve all unique patient names.",
+    "pandas_code": "df['Patient_Name'].unique()"
+  },
+  {
+    "Query": "Fetch all unique patient names.",
+    "pandas_code": "df['Patient_Name'].unique()"
+  },
+  {
+    "Query": "Retrieve many claims belong to Policy_Number 12345?",
+    "pandas_code": "df[df['Policy_Number'] == '12345'].shape[0]"
+  },
+  {
+    "Query": "Display many claims belong to Policy_Number 12345?",
+    "pandas_code": "df[df['Policy_Number'] == '12345'].shape[0]"
+  },
+  {
+    "Query": "Display claims for Policy_Number 54321.",
+    "pandas_code": "df[df['Policy_Number'] == '54321']"
+  },
+  {
+    "Query": "Fetch claims for Policy_Number 54321.",
+    "pandas_code": "df[df['Policy_Number'] == '54321']"
+  },
+  {
+    "Query": "Display details of Claim_Number C1234.",
+    "pandas_code": "df[df['Claim_Number'] == 'C1234']"
+  },
+  {
+    "Query": "List details of Claim_Number C1234.",
+    "pandas_code": "df[df['Claim_Number'] == 'C1234']"
+  },
+  {
+    "Query": "Fetch all claims for Provider_Code P5678.",
+    "pandas_code": "df[df['Provider_Code'] == 'P5678']"
+  },
+  {
+    "Query": "Get claim details where Claim_Id equals 10.",
+    "pandas_code": "df[df['Claim_Id'] == 10]"
+  },
+  {
+    "Query": "List claim details where Claim_Id equals 10.",
+    "pandas_code": "df[df['Claim_Id'] == 10]"
+  },
+  {
+    "Query": "List columns are available in this dataset?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Provide columns are available in this dataset?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Retrieve many records does the dataset have?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Get many records does the dataset have?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Retrieve the dataset structure.",
+    "pandas_code": "df.info()"
+  },
+  {
+    "Query": "Provide the dataset structure.",
+    "pandas_code": "df.info()"
+  },
+  {
+    "Query": "List first five records of dataset.",
+    "pandas_code": "df.head()"
+  },
+  {
+    "Query": "Get first five records of dataset.",
+    "pandas_code": "df.head()"
+  },
+  {
+    "Query": "Get last ten records.",
+    "pandas_code": "df.tail(10)"
+  },
+
+  
+  {
+    "Query": "Tell me list Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Tell me get Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Fetch Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Retrieve Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "I want to know display Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Is there find Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "I want to know provide Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Is there give me Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Tell me Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Can you Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Please Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Could you could you Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Could you show How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "List How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Please get How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Could you fetch How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Retrieve How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Do we have display How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Is there find How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Provide How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Can you give me How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Could you tell me How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Tell me can you How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Please How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Do we have could you How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Do we have tell me How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Show What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "List What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Get What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Fetch What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Retrieve What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Display What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Find What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Provide What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Give me What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Please tell me What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "I want to know can you What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Please What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Could you What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Tell me What columns are available?",
+    "pandas_code": "list(df.columns)"
+  },
+  {
+    "Query": "Could you show List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Get List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Fetch List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Retrieve List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Display List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Find List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Provide List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Give me List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Tell me List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Is there can you List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Please List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "I want to know could you List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Could you show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "List Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Can you get Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Fetch Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Do we have retrieve Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Do we have display Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Find Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Provide Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Give me Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Tell me Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Can you Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Could you please Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Could you Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Can you tell me Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Show Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "List Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Get Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Fetch Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Retrieve Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Display Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Could you find Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Do we have provide Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Give me Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Could you tell me Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Can you Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Please please Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Could you Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Tell me Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Show Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "List Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Get Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "I want to know fetch Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Retrieve Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Display Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Find Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Provide Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Give me Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Could you tell me Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Tell me can you Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Is there please Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Could you Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Tell me Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Is there show Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "List Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Please get Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Fetch Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Can you retrieve Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Display Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Can you find Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Do we have provide Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Give me Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Please tell me Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Can you Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Please Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Could you Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Tell me Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Show Max Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "List Max Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "I want to know get Max Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "Fetch Max Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "Retrieve Max Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "Display Max Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "Find Max Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "Is there provide Max Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "Give me Max Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "Tell me Max Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "Is there can you Max Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "Please Max Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "Could you Max Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].max()"
+  },
+  {
+    "Query": "Show Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "List Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Could you get Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Fetch Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "I want to know retrieve Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Display Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Can you find Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Provide Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Could you give me Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Tell me tell me Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Can you Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Do we have please Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Could you Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Tell me Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Is there show Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "List Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Get Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Tell me fetch Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Retrieve Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Display Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Could you find Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Provide Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Give me Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Can you tell me Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Can you Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Please please Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Could you Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Is there tell me Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Show Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Is there list Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Get Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Do we have fetch Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Retrieve Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Display Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Tell me find Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Please provide Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Give me Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Tell me Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Can you Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Please Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Could you Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "Please tell me Average days between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.mean()"
+  },
+  {
+    "Query": "I want to know show Claim with maximum delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "List Claim with maximum delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Get Claim with maximum delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Fetch Claim with maximum delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Retrieve Claim with maximum delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Display Claim with maximum delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Tell me find Claim with maximum delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Please provide Claim with maximum delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Could you give me Claim with maximum delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Tell me Claim with maximum delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Can you Claim with maximum delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Do we have please Claim with maximum delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Could you Claim with maximum delay between Loss_Date and Claim_Created_Date.",
+    "pandas_code": "df.loc[(df['Claim_Created_Date'] - df['Loss_Date']).dt.days.idxmax()]"
+  },
+  {
+    "Query": "Show Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "I want to know list Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Get Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Do we have fetch Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Tell me retrieve Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Tell me display Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Find Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "I want to know provide Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Give me Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Tell me tell me Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Can you Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Is there please Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "I want to know could you Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Can you tell me Days difference column for each claim.",
+    "pandas_code": "(df['Claim_Created_Date'] - df['Loss_Date']).dt.days"
+  },
+  {
+    "Query": "Show Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Is there list Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Tell me get Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Fetch Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "I want to know retrieve Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "I want to know display Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Find Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Provide Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Tell me give me Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Tell me Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Could you can you Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Please Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Do we have could you Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Tell me tell me Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Show List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Tell me get List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Fetch List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Retrieve List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Display List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Find List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Please provide List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Give me List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Tell me List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Can you List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Can you please List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Could you List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Could you tell me List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Show List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Get List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Is there fetch List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Retrieve List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Display List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Find List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "I want to know provide List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Tell me give me List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Could you tell me List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Can you List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Please please List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Could you List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Tell me List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Please show Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "List Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Please get Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Fetch Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Can you retrieve Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Display Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Find Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Do we have provide Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Give me Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Is there tell me Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Can you can you Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Please Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Could you Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Tell me Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Show Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "I want to know list Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Get Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Fetch Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Can you retrieve Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Display Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Find Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Tell me provide Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Please give me Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Tell me Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Can you Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Please Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Can you could you Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Can you tell me Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Show Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "List Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "I want to know get Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Fetch Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Retrieve Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Display Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Find Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Provide Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Give me Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Tell me tell me Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Can you can you Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Please Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Could you Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Can you tell me Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Show details for Claim_Number CBGDC23058949-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058949-00']"
+  },
+  {
+    "Query": "Tell me list Show details for Claim_Number CBGDC23058949-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058949-00']"
+  },
+  {
+    "Query": "Can you get Show details for Claim_Number CBGDC23058949-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058949-00']"
+  },
+  {
+    "Query": "Fetch Show details for Claim_Number CBGDC23058949-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058949-00']"
+  },
+  {
+    "Query": "Retrieve Show details for Claim_Number CBGDC23058949-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058949-00']"
+  },
+  {
+    "Query": "Tell me display Show details for Claim_Number CBGDC23058949-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058949-00']"
+  },
+  {
+    "Query": "Could you find Show details for Claim_Number CBGDC23058949-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058949-00']"
+  },
+  {
+    "Query": "Provide Show details for Claim_Number CBGDC23058949-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058949-00']"
+  },
+  {
+    "Query": "Please give me Show details for Claim_Number CBGDC23058949-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058949-00']"
+  },
+  {
+    "Query": "Tell me Show details for Claim_Number CBGDC23058949-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058949-00']"
+  },
+  {
+    "Query": "Can you Show details for Claim_Number CBGDC23058949-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058949-00']"
+  },
+  {
+    "Query": "Please Show details for Claim_Number CBGDC23058949-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058949-00']"
+  },
+  {
+    "Query": "Could you Show details for Claim_Number CBGDC23058949-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058949-00']"
+  },
+  {
+    "Query": "Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "I want to know list Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "Get Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "Fetch Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "Retrieve Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "Is there display Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "Find Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "Provide Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "Do we have give me Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "Tell me Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "Can you Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "Please Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "I want to know could you Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "Please tell me Show details for Claim_Number CBGDC23058968-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "I want to know show details for Claim_Number CBGDC23058986-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058986-00']"
+  },
+  {
+    "Query": "List Show details for Claim_Number CBGDC23058986-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058986-00']"
+  },
+  {
+    "Query": "Get Show details for Claim_Number CBGDC23058986-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058986-00']"
+  },
+  {
+    "Query": "Fetch Show details for Claim_Number CBGDC23058986-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058986-00']"
+  },
+  {
+    "Query": "Retrieve Show details for Claim_Number CBGDC23058986-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058986-00']"
+  },
+  {
+    "Query": "Display Show details for Claim_Number CBGDC23058986-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058986-00']"
+  },
+  {
+    "Query": "Find Show details for Claim_Number CBGDC23058986-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058986-00']"
+  },
+  {
+    "Query": "Do we have provide Show details for Claim_Number CBGDC23058986-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058986-00']"
+  },
+  {
+    "Query": "Can you give me Show details for Claim_Number CBGDC23058986-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058986-00']"
+  },
+  {
+    "Query": "Tell me Show details for Claim_Number CBGDC23058986-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058986-00']"
+  },
+  {
+    "Query": "Can you Show details for Claim_Number CBGDC23058986-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058986-00']"
+  },
+  {
+    "Query": "Please Show details for Claim_Number CBGDC23058986-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058986-00']"
+  },
+  {
+    "Query": "Could you Show details for Claim_Number CBGDC23058986-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058986-00']"
+  },
+  {
+    "Query": "Please show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "List Show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "Get Show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "I want to know fetch Show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "I want to know retrieve Show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "Tell me display Show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "Find Show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "Provide Show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "Give me Show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "Tell me tell me Show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "Can you Show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "Please Show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "Is there could you Show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "Tell me Show details for Claim_Number CBGDC23058987-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058987-00']"
+  },
+  {
+    "Query": "Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "List Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "Get Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "Fetch Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "Retrieve Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "Display Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "I want to know find Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "Is there provide Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "Give me Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "Could you tell me Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "Can you Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "Please Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "Could you Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "Tell me Show details for Claim_Number CBGDC23058990-00.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23058990-00']"
+  },
+  {
+    "Query": "Show Get claims for Provider_Code PC100469.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "I want to know list Get claims for Provider_Code PC100469.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Get claims for Provider_Code PC100469.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Do we have fetch Get claims for Provider_Code PC100469.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Retrieve Get claims for Provider_Code PC100469.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Could you display Get claims for Provider_Code PC100469.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Find Get claims for Provider_Code PC100469.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Provide Get claims for Provider_Code PC100469.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Give me Get claims for Provider_Code PC100469.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Tell me Get claims for Provider_Code PC100469.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Can you Get claims for Provider_Code PC100469.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Do we have please Get claims for Provider_Code PC100469.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Could you Get claims for Provider_Code PC100469.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Is there show Get claims for Provider_Code OTH0000001.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "List Get claims for Provider_Code OTH0000001.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "Get claims for Provider_Code OTH0000001.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "Fetch Get claims for Provider_Code OTH0000001.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "Retrieve Get claims for Provider_Code OTH0000001.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "Display Get claims for Provider_Code OTH0000001.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "I want to know find Get claims for Provider_Code OTH0000001.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "Do we have provide Get claims for Provider_Code OTH0000001.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "Give me Get claims for Provider_Code OTH0000001.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "Tell me Get claims for Provider_Code OTH0000001.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "Tell me can you Get claims for Provider_Code OTH0000001.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "I want to know please Get claims for Provider_Code OTH0000001.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "Could you Get claims for Provider_Code OTH0000001.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "Could you show Get claims for Provider_Code PC103465.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103465']"
+  },
+  {
+    "Query": "List Get claims for Provider_Code PC103465.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103465']"
+  },
+  {
+    "Query": "Get claims for Provider_Code PC103465.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103465']"
+  },
+  {
+    "Query": "Please fetch Get claims for Provider_Code PC103465.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103465']"
+  },
+  {
+    "Query": "Can you retrieve Get claims for Provider_Code PC103465.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103465']"
+  },
+  {
+    "Query": "Could you display Get claims for Provider_Code PC103465.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103465']"
+  },
+  {
+    "Query": "Find Get claims for Provider_Code PC103465.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103465']"
+  },
+  {
+    "Query": "Provide Get claims for Provider_Code PC103465.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103465']"
+  },
+  {
+    "Query": "Give me Get claims for Provider_Code PC103465.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103465']"
+  },
+  {
+    "Query": "Tell me Get claims for Provider_Code PC103465.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103465']"
+  },
+  {
+    "Query": "Can you Get claims for Provider_Code PC103465.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103465']"
+  },
+  {
+    "Query": "Can you please Get claims for Provider_Code PC103465.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103465']"
+  },
+  {
+    "Query": "Could you Get claims for Provider_Code PC103465.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103465']"
+  },
+  {
+    "Query": "Show Get claims for Provider_Code PC101350.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "List Get claims for Provider_Code PC101350.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Get claims for Provider_Code PC101350.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Fetch Get claims for Provider_Code PC101350.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Can you retrieve Get claims for Provider_Code PC101350.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Display Get claims for Provider_Code PC101350.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Find Get claims for Provider_Code PC101350.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Provide Get claims for Provider_Code PC101350.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Is there give me Get claims for Provider_Code PC101350.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Tell me Get claims for Provider_Code PC101350.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Please can you Get claims for Provider_Code PC101350.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Please please Get claims for Provider_Code PC101350.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Could you Get claims for Provider_Code PC101350.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Could you show Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "List Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "I want to know fetch Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "Retrieve Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "Do we have display Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "Find Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "Provide Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "Give me Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "Please tell me Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "Can you Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "Please Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "Can you could you Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "I want to know tell me Get claims for Provider_Code PC100001.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "Fetch Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Tell me claims where Claim Created Date on 2023-03-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-24')]"
+  },
+  {
+    "Query": "Give me claims where Policy Number is 'BGDC211000706-01-000' and Loss Date after 2023-04-03.",
+    "pandas_code": "df['Policy_Number'] == 'BGDC211000706-01-000' & 'Loss_Date'] > pd.to_datetime('2023-04-03')]"
+  },
+  {
+    "Query": "List claims where Deduction Amount between 4 and 7 and Claim Id at least 271546.",
+    "pandas_code": "df[('Deduction_Amount'] >= 4.61) & ('Deduction_Amount'] <= 7.02) & 'Claim_Id'] >= 271546]"
+  },
+  {
+    "Query": "Count how many me claims where Claim Id greater than 275571",
+    "pandas_code": "df[df['Claim_Id'] > 275571].shape[0]"
+  },
+  {
+    "Query": "List claims where Provider Name is 'DENTAL FOCUS JUNCTION 10 CLINIC'.",
+    "pandas_code": "df[df['Provider_Name'] == 'DENTAL FOCUS JUNCTION 10 CLINIC']"
+  },
+  {
+    "Query": "Provide claims where Loss Type is 'GP Treatment' and Claim Number contains 'CBGDP2' How many are there?",
+    "pandas_code": "df['Loss_Type'] == 'GP Treatment' & 'Claim_Number'].str.contains(r'CBGDP2', na=False)].shape[0]"
+  },
+  {
+    "Query": "Retrieve claims where Deduction Amount equal to 9.65 and Status Code is 'PY3'.",
+    "pandas_code": "df['Deduction_Amount'] == 9.65 & 'Status_Code'] == 'PY3']"
+  },
+  {
+    "Query": "Provide claims where Patient Name contains 'Mr Kim' and Approved Amount between 386 and 1088 How many are there?",
+    "pandas_code": "df['Patient_Name'].str.contains(r'Mr Kim', na=False) & ('Approved_Amount'] >= 386.64) & ('Approved_Amount'] <= 1088.45)].shape[0]"
+  },
+  {
+    "Query": "Please display claims where Claim Number is 'CBGDC23060462-00' and Status is 'Rejected'.",
+    "pandas_code": "df['Claim_Number'] == 'CBGDC23060462-00' & 'Status'] == 'Rejected']"
+  },
+  {
+    "Query": "Show claims where Claim Id at most 272152.",
+    "pandas_code": "df[df['Claim_Id'] <= 272152]"
+  },
+  {
+    "Query": "Retrieve claims where Product is 'Group Specialist' and Claim Id greater than 272765.71.",
+    "pandas_code": "df['Product'] == 'Group Specialist' & 'Claim_Id'] > 272765.71]"
+  },
+  {
+    "Query": "Please find Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Tell me claims where Product contains 'Group ' and Provider Name contains 'HEALTH'.",
+    "pandas_code": "df['Product'].str.contains(r'Group ', na=False) & 'Provider_Name'].str.contains(r'HEALTH', na=False)]"
+  },
+  {
+    "Query": "Tell me find claims where Product is 'Group Specialist' and Patient Name is 'Ms Yi Yun Joyce Chua'.",
+    "pandas_code": "df['Product'] == 'Group Specialist' & 'Patient_Name'] == 'Ms Yi Yun Joyce Chua']"
+  },
+  {
+    "Query": "Get claims where Claim Id greater than 276176.",
+    "pandas_code": "df[df['Claim_Id'] > 276176]"
+  },
+  {
+    "Query": "Give me Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Tell me claims where Approved Amount less than 628.",
+    "pandas_code": "df[df['Approved_Amount'] < 628]"
+  },
+  {
+    "Query": "Tell me claims where Policy Number is 'BGDC211000733-01-000' and Provider Name is 'CASA DENTAL ADMIRALTY'.",
+    "pandas_code": "df['Policy_Number'] == 'BGDC211000733-01-000' & 'Provider_Name'] == 'CASA DENTAL ADMIRALTY']"
+  },
+  {
+    "Query": "List List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Give me claims where Policy Number is 'BGDC211000067-02-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000067-02-000']"
+  },
+  {
+    "Query": "Get claims where Claim Id greater than 279086 and Claim Number contains 'CBGDC2'.",
+    "pandas_code": "df['Claim_Id'] > 279086 & 'Claim_Number'].str.contains(r'CBGDC2', na=False)]"
+  },
+  {
+    "Query": "Get claims where Approved Amount less than 266.",
+    "pandas_code": "df[df['Approved_Amount'] < 266]"
+  },
+  {
+    "Query": "Show claims where Product contains 'Group ' and Approved Amount less than 30.",
+    "pandas_code": "df['Product'].str.contains(r'Group ', na=False) & 'Approved_Amount'] < 30]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 269921 and 276134.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269921.41) & (df['Claim_Id'] <= 276134.64)]"
+  },
+  {
+    "Query": "Show claims where Claim Amount equal to 702.",
+    "pandas_code": "df[df['Claim_Amount'] == 702]"
+  },
+  {
+    "Query": "How many me claims where Loss Date on 2023-04-17",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-17')].shape[0]"
+  },
+  {
+    "Query": "Find claims where Scheme contains 'SME 2-' and Claim Id at least 262255.11.",
+    "pandas_code": "df['Scheme'].str.contains(r'SME 2-', na=False) & 'Claim_Id'] >= 262255.11]"
+  },
+  {
+    "Query": "Display claims where Claim Amount less than 1306.",
+    "pandas_code": "df[df['Claim_Amount'] < 1306]"
+  },
+  {
+    "Query": "Tell me claims where Loss Date before 2023-01-10 and Deduction Amount less than 8.81.",
+    "pandas_code": "df['Loss_Date'] < pd.to_datetime('2023-01-10') & 'Deduction_Amount'] < 8.81]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount at least 1227.07.",
+    "pandas_code": "df[df['Approved_Amount'] >= 1227.07]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount equal to 754.",
+    "pandas_code": "df[df['Approved_Amount'] == 754]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-03-26.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-26')]"
+  },
+  {
+    "Query": "Tell me claims where Loss Date before 2022-11-21.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-21')]"
+  },
+  {
+    "Query": "Count how many claims where Approved Amount between 495 and 856",
+    "pandas_code": "df[(df['Approved_Amount'] >= 495.35) & (df['Approved_Amount'] <= 856.73)].shape[0]"
+  },
+  {
+    "Query": "Tell me claims where Claim Id at most 273871.",
+    "pandas_code": "df[df['Claim_Id'] <= 273871]"
+  },
+  {
+    "Query": "Please get claims where Loss Date before 2022-12-04.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-04')]"
+  },
+  {
+    "Query": "Provide claims where Loss Type is 'GP Treatment' and Claim Type is 'CHS' How many are there?",
+    "pandas_code": "df['Loss_Type'] == 'GP Treatment' & 'Claim_Type'] == 'CHS'].shape[0]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount less than 1479.",
+    "pandas_code": "df[df['Approved_Amount'] < 1479]"
+  },
+  {
+    "Query": "Give me claims where Claim Amount at most 632 and Claim Type is 'Cashless'.",
+    "pandas_code": "df['Claim_Amount'] <= 632 & 'Claim_Type'] == 'Cashless']"
+  },
+  {
+    "Query": "Tell me claims where Deduction Amount equal to 8.97.",
+    "pandas_code": "df[df['Deduction_Amount'] == 8.97]"
+  },
+  {
+    "Query": "Get claims where Policy Number is 'BGDC221000006-01-000' and Product is 'Specialist'.",
+    "pandas_code": "df['Policy_Number'] == 'BGDC221000006-01-000' & 'Product'] == 'Specialist']"
+  },
+  {
+    "Query": "List claims where Provider Name is 'NATIONAL UNIVERSITY HOSPITAL (NUH)' and Loss Date between 2022-11-26 and 2023-01-12.",
+    "pandas_code": "df['Provider_Name'] == 'NATIONAL UNIVERSITY HOSPITAL (NUH)' & ('Loss_Date'] >= pd.to_datetime('2022-11-26')) & ('Loss_Date'] <= pd.to_datetime('2023-01-12'))]"
+  },
+  {
+    "Query": "Show claims where Patient Name contains 'Ms Che' and Claim Number is 'CBGDP23038619-00'.",
+    "pandas_code": "df['Patient_Name'].str.contains(r'Ms Che', na=False) & 'Claim_Number'] == 'CBGDP23038619-00']"
+  },
+  {
+    "Query": "Display claims where Claim Amount at most 1400.",
+    "pandas_code": "df[df['Claim_Amount'] <= 1400]"
+  },
+  {
+    "Query": "List claims where Claim Amount at most 838.04 and Claim Number is 'CBGDC23062404-00'.",
+    "pandas_code": "df['Claim_Amount'] <= 838.04 & 'Claim_Number'] == 'CBGDC23062404-00']"
+  },
+  {
+    "Query": "Could you fetch List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount at most 1074 and Claim Number is 'CBGDC23060098-00'.",
+    "pandas_code": "df['Claim_Amount'] <= 1074 & 'Claim_Number'] == 'CBGDC23060098-00']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2022-12-11 and Claim Type is 'Cashless'.",
+    "pandas_code": "df['Claim_Created_Date'] < pd.to_datetime('2022-12-11') & 'Claim_Type'] == 'Cashless']"
+  },
+  {
+    "Query": "Tell me claims where Provider Name is 'Changi General Hospital'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Changi General Hospital']"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 727 and 917.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 727.46) & (df['Approved_Amount'] <= 917.27)]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2023-05-30.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-30')]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 277 and 1431.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 277.96) & (df['Approved_Amount'] <= 1431.18)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Type contains 'Hospit' and Patient Name is 'Ms Sim Lee Cyndi Wee'.",
+    "pandas_code": "df['Loss_Type'].str.contains(r'Hospit', na=False) & 'Patient_Name'] == 'Ms Sim Lee Cyndi Wee']"
+  },
+  {
+    "Query": "Number of claims where Claim Amount between 327 and 841",
+    "pandas_code": "df[(df['Claim_Amount'] >= 327.35) & (df['Claim_Amount'] <= 841.18)].shape[0]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount at most 268.",
+    "pandas_code": "df[df['Claim_Amount'] <= 268]"
+  },
+  {
+    "Query": "Show claims where Patient Name is 'Ms Geok Im Claudia Theresa Beng'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Geok Im Claudia Theresa Beng']"
+  },
+  {
+    "Query": "Find claims where Deduction Amount greater than 1.13.",
+    "pandas_code": "df[df['Deduction_Amount'] > 1.13]"
+  },
+  {
+    "Query": "Give me claims where Policy Number contains 'BGDC21' and Claim Amount less than 54.",
+    "pandas_code": "df['Policy_Number'].str.contains(r'BGDC21', na=False) & 'Claim_Amount'] < 54]"
+  },
+  {
+    "Query": "Count how many claims where Approved Amount between 691 and 920",
+    "pandas_code": "df[(df['Approved_Amount'] >= 691.97) & (df['Approved_Amount'] <= 920.72)].shape[0]"
+  },
+  {
+    "Query": "List claims where Loss Date after 2023-02-11.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-02-11')]"
+  },
+  {
+    "Query": "Number of claims where Approved Amount equal to 892",
+    "pandas_code": "df[df['Approved_Amount'] == 892].shape[0]"
+  },
+  {
+    "Query": "Get claims where Deduction Amount equal to 2.99.",
+    "pandas_code": "df[df['Deduction_Amount'] == 2.99]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-05-09.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-09')]"
+  },
+  {
+    "Query": "Give me claims where Claim Number contains 'CBGDC2' and Claim Type is 'CHS'.",
+    "pandas_code": "df['Claim_Number'].str.contains(r'CBGDC2', na=False) & 'Claim_Type'] == 'CHS']"
+  },
+  {
+    "Query": "Tell me claims where Claim Created Date on 2023-04-19 and Claim Amount equal to 601.",
+    "pandas_code": "df['Claim_Created_Date'] == pd.to_datetime('2023-04-19') & 'Claim_Amount'] == 601]"
+  },
+  {
+    "Query": "How many me claims where Claim Id equal to 268536",
+    "pandas_code": "df[df['Claim_Id'] == 268536].shape[0]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2023-07-09.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-09')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-04-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-23')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Number is 'CBGDC23059571-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23059571-00']"
+  },
+  {
+    "Query": "Give me claims where Claim Number is 'CBGDP23044447-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23044447-00']"
+  },
+  {
+    "Query": "Fetch claims where Provider Code is 'PC103779'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103779']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2022-12-24 and Status is 'Payment Approval'.",
+    "pandas_code": "df['Claim_Created_Date'] > pd.to_datetime('2022-12-24') & 'Status'] == 'Payment Approval']"
+  },
+  {
+    "Query": "Tell me claims where Deduction Amount equal to 8.59.",
+    "pandas_code": "df[df['Deduction_Amount'] == 8.59]"
+  },
+  {
+    "Query": "Get claims where Loss Type contains 'GP Tre' and Patient Name is 'Siew Lin Ngan' How many are there?",
+    "pandas_code": "df['Loss_Type'].str.contains(r'GP Tre', na=False) & 'Patient_Name'] == 'Siew Lin Ngan'].shape[0]"
+  },
+  {
+    "Query": "Retrieve Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Tell me claims where Deduction Amount at least 5.18.",
+    "pandas_code": "df[df['Deduction_Amount'] >= 5.18]"
+  },
+  {
+    "Query": "Give me claims where Claim Number is 'CBGDC23070205-00' and Claim Created Date on 2023-01-14.",
+    "pandas_code": "df['Claim_Number'] == 'CBGDC23070205-00' & 'Claim_Created_Date'] == pd.to_datetime('2023-01-14')]"
+  },
+  {
+    "Query": "Find claims where Claim Number contains 'CBGDC2' and Loss Date on 2022-12-17.",
+    "pandas_code": "df['Claim_Number'].str.contains(r'CBGDC2', na=False) & 'Loss_Date'] == pd.to_datetime('2022-12-17')]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 46 and 1144 and Provider Code contains 'PC1023'.",
+    "pandas_code": "df[('Claim_Amount'] >= 46.92) & ('Claim_Amount'] <= 1144.18) & 'Provider_Code'].str.contains(r'PC1023', na=False)]"
+  },
+  {
+    "Query": "Tell me claims where Approved Amount greater than 391 and Status Code is 'DU0'.",
+    "pandas_code": "df['Approved_Amount'] > 391 & 'Status_Code'] == 'DU0']"
+  },
+  {
+    "Query": "Tell me claims where Claim Created Date between 2022-12-31 and 2023-05-22 and Status is 'Approved'.",
+    "pandas_code": "df[('Claim_Created_Date'] >= pd.to_datetime('2022-12-31')) & ('Claim_Created_Date'] <= pd.to_datetime('2023-05-22')) & 'Status'] == 'Approved']"
+  },
+  {
+    "Query": "Get claims where Claim Id equal to 266120.",
+    "pandas_code": "df[df['Claim_Id'] == 266120]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 226 and 1081.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 226.59) & (df['Approved_Amount'] <= 1081.31)]"
+  },
+  {
+    "Query": "Get List claims where Deduction_Amount > 0.1 * Claim_Amount.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.1 * df['Claim_Amount']]"
+  },
+  {
+    "Query": "Do we have find claims where Deduction Amount equal to 6.04.",
+    "pandas_code": "df[df['Deduction_Amount'] == 6.04]"
+  },
+  {
+    "Query": "Give me claims where Approved Amount equal to 364.",
+    "pandas_code": "df[df['Approved_Amount'] == 364]"
+  },
+  {
+    "Query": "How many claims where Approved Amount at least 427.58",
+    "pandas_code": "df[df['Approved_Amount'] >= 427.58].shape[0]"
+  },
+  {
+    "Query": "Can you fetch claims where Provider Code contains 'PC1028' and Scheme is 'SME 3-SP-CO-Panel Only' How many are there?",
+    "pandas_code": "df['Provider_Code'].str.contains(r'PC1028', na=False) & 'Scheme'] == 'SME 3-SP-CO-Panel Only'].shape[0]"
+  },
+  {
+    "Query": "Get claims where Claim Amount less than 703.",
+    "pandas_code": "df[df['Claim_Amount'] < 703]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-05-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-16')]"
+  },
+  {
+    "Query": "Give me claims where Loss Date on 2023-04-24 and Claim Id greater than 272555.",
+    "pandas_code": "df['Loss_Date'] == pd.to_datetime('2023-04-24') & 'Claim_Id'] > 272555]"
+  },
+  {
+    "Query": "Tell me Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2022-12-12 and 2023-01-04.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-12')) & (df['Loss_Date'] <= pd.to_datetime('2023-01-04'))]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 571 and 1307.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 571.9) & (df['Approved_Amount'] <= 1307.85)]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2022-12-25.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-25')]"
+  },
+  {
+    "Query": "Tell me tell me claims where Claim Created Date before 2023-02-03.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-02-03')]"
+  },
+  {
+    "Query": "Display claims where Claim Type is 'Cashless' and Claim Amount at most 249.",
+    "pandas_code": "df['Claim_Type'] == 'Cashless' & 'Claim_Amount'] <= 249]"
+  },
+  {
+    "Query": "Find claims where Provider Code is 'PC103465' and Patient Name is 'Ms Wei Ying Angeline Ng'.",
+    "pandas_code": "df['Provider_Code'] == 'PC103465' & 'Patient_Name'] == 'Ms Wei Ying Angeline Ng']"
+  },
+  {
+    "Query": "List claims where Scheme is 'SME 1-1BPTE' and Claim Type is 'Cashless' How many are there?",
+    "pandas_code": "df['Scheme'] == 'SME 1-1BPTE' & 'Claim_Type'] == 'Cashless'].shape[0]"
+  },
+  {
+    "Query": "Fetch claims where Provider Name is 'Nuffield Dental Raffles Place' and Approved Amount between 392 and 1373.",
+    "pandas_code": "df['Provider_Name'] == 'Nuffield Dental Raffles Place' & ('Approved_Amount'] >= 392.91) & ('Approved_Amount'] <= 1373.58)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date before 2023-04-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-25')]"
+  },
+  {
+    "Query": "Tell me display List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "List claims where Deduction Amount between 2 and 7.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 2.95) & (df['Deduction_Amount'] <= 7.03)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount equal to 1542 and Approved Amount between 16 and 1497 How many are there?",
+    "pandas_code": "df['Claim_Amount'] == 1542 & ('Approved_Amount'] >= 16.47) & ('Approved_Amount'] <= 1497.08)].shape[0]"
+  },
+  {
+    "Query": "Tell me claims where Approved Amount at least 873.49.",
+    "pandas_code": "df[df['Approved_Amount'] >= 873.49]"
+  },
+  {
+    "Query": "List Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Tell me claims where Loss Date between 2022-11-11 and 2023-02-02.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-11')) & (df['Loss_Date'] <= pd.to_datetime('2023-02-02'))]"
+  },
+  {
+    "Query": "Find Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Display claims where Provider Name is 'RAFFLESMEDICAL (Raffles Hospital)' and Provider Code is 'PC103209'.",
+    "pandas_code": "df['Provider_Name'] == 'RAFFLESMEDICAL (Raffles Hospital)' & 'Provider_Code'] == 'PC103209']"
+  },
+  {
+    "Query": "Get claims where Status is 'Approved' and Provider Code is 'PC101668'.",
+    "pandas_code": "df['Status'] == 'Approved' & 'Provider_Code'] == 'PC101668']"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2023-04-05 and 2023-06-18.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-04-05')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-18'))]"
+  },
+  {
+    "Query": "Do we have provide claims where Product is 'Group OP General Practitioner' and Provider Code contains 'PC1004'.",
+    "pandas_code": "df['Product'] == 'Group OP General Practitioner' & 'Provider_Code'].str.contains(r'PC1004', na=False)]"
+  },
+  {
+    "Query": "Tell me claims where Loss Type is 'Specialist Treatment' and Provider Code is 'PC101171'.",
+    "pandas_code": "df['Loss_Type'] == 'Specialist Treatment' & 'Provider_Code'] == 'PC101171']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2023-06-07.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-07')]"
+  },
+  {
+    "Query": "List claims where Claim Amount between 155 and 1547.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 155.01) & (df['Claim_Amount'] <= 1547.28)]"
+  },
+  {
+    "Query": "Show claims where Patient Name contains 'Ms Jia' and Provider Name contains 'Jurong' How many are there?",
+    "pandas_code": "df['Patient_Name'].str.contains(r'Ms Jia', na=False) & 'Provider_Name'].str.contains(r'Jurong', na=False)].shape[0]"
+  },
+  {
+    "Query": "Is there find Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2023-07-30.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-07-30')]"
+  },
+  {
+    "Query": "Number of claims where Approved Amount less than 1390.83",
+    "pandas_code": "df[df['Approved_Amount'] < 1390.83].shape[0]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id greater than 265181.03.",
+    "pandas_code": "df[df['Claim_Id'] > 265181.03]"
+  },
+  {
+    "Query": "Fetch claims where Product contains 'Hospit' and Loss Date between 2022-11-18 and 2023-03-23.",
+    "pandas_code": "df['Product'].str.contains(r'Hospit', na=False) & ('Loss_Date'] >= pd.to_datetime('2022-11-18')) & ('Loss_Date'] <= pd.to_datetime('2023-03-23'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Id greater than 271766.",
+    "pandas_code": "df[df['Claim_Id'] > 271766]"
+  },
+  {
+    "Query": "Provide claims where Status is 'Paid' and Claim Id at least 267056.",
+    "pandas_code": "df['Status'] == 'Paid' & 'Claim_Id'] >= 267056]"
+  },
+  {
+    "Query": "Show claims where Claim Amount at least 1293.",
+    "pandas_code": "df[df['Claim_Amount'] >= 1293]"
+  },
+  {
+    "Query": "Get claims where Status Code is 'AP2' and Deduction Amount at most 3.53.",
+    "pandas_code": "df['Status_Code'] == 'AP2' & 'Deduction_Amount'] <= 3.53]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2022-12-31 and 2023-02-02 and Provider Code is 'PC101171'.",
+    "pandas_code": "df[('Claim_Created_Date'] >= pd.to_datetime('2022-12-31')) & ('Claim_Created_Date'] <= pd.to_datetime('2023-02-02')) & 'Provider_Code'] == 'PC101171']"
+  },
+  {
+    "Query": "Provide claims where Claim Amount equal to 520.19.",
+    "pandas_code": "df[df['Claim_Amount'] == 520.19]"
+  },
+  {
+    "Query": "Find claims where Approved Amount at most 1453.",
+    "pandas_code": "df[df['Approved_Amount'] <= 1453]"
+  },
+  {
+    "Query": "Display claims where Patient Name contains 'Mr Thi' and Loss Type is 'Hospitalisation'.",
+    "pandas_code": "df['Patient_Name'].str.contains(r'Mr Thi', na=False) & 'Loss_Type'] == 'Hospitalisation']"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2022-12-06 and Status Code is 'RJ2' How many are there?",
+    "pandas_code": "df['Loss_Date'] == pd.to_datetime('2022-12-06') & 'Status_Code'] == 'RJ2'].shape[0]"
+  },
+  {
+    "Query": "Provide claims where Claim Id between 261709 and 271565 and Status Code is 'CV0'.",
+    "pandas_code": "df[('Claim_Id'] >= 261709.53) & ('Claim_Id'] <= 271565.64) & 'Status_Code'] == 'CV0']"
+  },
+  {
+    "Query": "List claims where Policy Number is 'BGDP221000904-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000904-01-000']"
+  },
+  {
+    "Query": "Fetch claims where Claim Id at most 278242 and Product is 'Hospital & Surgical' How many are there?",
+    "pandas_code": "df['Claim_Id'] <= 278242 & 'Product'] == 'Hospital & Surgical'].shape[0]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2023-04-12 and Claim Id equal to 274071.",
+    "pandas_code": "df['Loss_Date'] > pd.to_datetime('2023-04-12') & 'Claim_Id'] == 274071]"
+  },
+  {
+    "Query": "Fetch claims where Status Code is 'CV0' and Claim Number is 'CBGDP23037658-00'.",
+    "pandas_code": "df['Status_Code'] == 'CV0' & 'Claim_Number'] == 'CBGDP23037658-00']"
+  },
+  {
+    "Query": "Give me claims where Scheme contains 'SME 1-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 1-', na=False)]"
+  },
+  {
+    "Query": "Get Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2022-12-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-25')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Number is 'CBGDP23044147-00' and Policy Number contains 'BGDP22'.",
+    "pandas_code": "df['Claim_Number'] == 'CBGDP23044147-00' & 'Policy_Number'].str.contains(r'BGDP22', na=False)]"
+  },
+  {
+    "Query": "Count how many claims where Product is 'Dental'",
+    "pandas_code": "df[df['Product'] == 'Dental'].shape[0]"
+  },
+  {
+    "Query": "How many claims where Patient Name contains 'Ms Jia'",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Jia', na=False)].shape[0]"
+  },
+  {
+    "Query": "Give me claims where Claim Created Date before 2023-04-10.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-10')]"
+  },
+  {
+    "Query": "How many claims where Loss Date between 2023-03-24 and 2023-07-28",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-24')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-28'))].shape[0]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount at most 1418.",
+    "pandas_code": "df[df['Claim_Amount'] <= 1418]"
+  },
+  {
+    "Query": "Give me Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Tell me claims where Claim Type is 'CHS' and Product is 'Dental'.",
+    "pandas_code": "df['Claim_Type'] == 'CHS' & 'Product'] == 'Dental']"
+  },
+  {
+    "Query": "Provide claims where Claim Id greater than 262499.0.",
+    "pandas_code": "df[df['Claim_Id'] > 262499.0]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2023-04-15.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-15')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2023-03-24.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-24')]"
+  },
+  {
+    "Query": "Count how many claims where Claim Number contains 'CBGDC2'",
+    "pandas_code": "df[df['Claim_Number'].str.contains(r'CBGDC2', na=False)].shape[0]"
+  },
+  {
+    "Query": "Get claims where Claim Amount equal to 446.",
+    "pandas_code": "df[df['Claim_Amount'] == 446]"
+  },
+  {
+    "Query": "Give me claims where Provider Name is 'Q & M Dental Surgery (Bedok)' and Status Code contains 'RJ2'.",
+    "pandas_code": "df['Provider_Name'] == 'Q & M Dental Surgery (Bedok)' & 'Status_Code'].str.contains(r'RJ2', na=False)]"
+  },
+  {
+    "Query": "Tell me claims where Deduction Amount between 0 and 9 and Provider Name contains 'DENTAL'.",
+    "pandas_code": "df[('Deduction_Amount'] >= 0.85) & ('Deduction_Amount'] <= 9.01) & 'Provider_Name'].str.contains(r'DENTAL', na=False)]"
+  },
+  {
+    "Query": "Please fetch claims where Status Code contains 'RJ2' and Patient Name is 'Mr Hwee Huat Goh' How many are there?",
+    "pandas_code": "df['Status_Code'].str.contains(r'RJ2', na=False) & 'Patient_Name'] == 'Mr Hwee Huat Goh'].shape[0]"
+  },
+  {
+    "Query": "Tell me claims where Loss Date before 2022-11-21 and Patient Name is 'Mr Pendharkar Devendra Suhas'.",
+    "pandas_code": "df['Loss_Date'] < pd.to_datetime('2022-11-21') & 'Patient_Name'] == 'Mr Pendharkar Devendra Suhas']"
+  },
+  {
+    "Query": "Provide claims where Deduction Amount between 0 and 8.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 0.42) & (df['Deduction_Amount'] <= 8.32)]"
+  },
+  {
+    "Query": "Provide claims where Product contains 'Group ' and Claim Amount greater than 1257.",
+    "pandas_code": "df['Product'].str.contains(r'Group ', na=False) & 'Claim_Amount'] > 1257]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date before 2023-03-10.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-10')]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 626 and 962 and Loss Type is 'Hospitalisation'.",
+    "pandas_code": "df[('Claim_Amount'] >= 626.56) & ('Claim_Amount'] <= 962.65) & 'Loss_Type'] == 'Hospitalisation']"
+  },
+  {
+    "Query": "Give me claims where Policy Number is 'BGDP221000706-01-000' and Loss Type contains 'GP Tre'.",
+    "pandas_code": "df['Policy_Number'] == 'BGDP221000706-01-000' & 'Loss_Type'].str.contains(r'GP Tre', na=False)]"
+  },
+  {
+    "Query": "Number of me claims where Claim Type contains 'Cashle'",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Cashle', na=False)].shape[0]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 612 and 958.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 612.38) & (df['Approved_Amount'] <= 958.56)]"
+  },
+  {
+    "Query": "Tell me claims where Loss Date after 2023-07-26.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-26')]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount at least 895.",
+    "pandas_code": "df[df['Approved_Amount'] >= 895]"
+  },
+  {
+    "Query": "Provide claims where Claim Id equal to 264741 and Provider Code is 'PC103761'.",
+    "pandas_code": "df['Claim_Id'] == 264741 & 'Provider_Code'] == 'PC103761']"
+  },
+  {
+    "Query": "Tell me claims where Claim Amount equal to 1557.7 and Provider Code is 'PC100469'.",
+    "pandas_code": "df['Claim_Amount'] == 1557.7 & 'Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Show claims where Scheme is 'New Template 1' and Policy Number is 'BGDP221001107-00-000'.",
+    "pandas_code": "df['Scheme'] == 'New Template 1' & 'Policy_Number'] == 'BGDP221001107-00-000']"
+  },
+  {
+    "Query": "Tell me claims where Claim Amount between 139 and 1275.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 139.03) & (df['Claim_Amount'] <= 1275.46)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2023-02-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-02-24')]"
+  },
+  {
+    "Query": "Please show claims where Patient Name is 'Ms Alexa Zacharee Talavera Ostonal' and Deduction Amount greater than 6.76.",
+    "pandas_code": "df['Patient_Name'] == 'Ms Alexa Zacharee Talavera Ostonal' & 'Deduction_Amount'] > 6.76]"
+  },
+  {
+    "Query": "Display claims where Claim Id at most 267875.",
+    "pandas_code": "df[df['Claim_Id'] <= 267875]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount equal to 963.",
+    "pandas_code": "df[df['Approved_Amount'] == 963]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount equal to 1154.",
+    "pandas_code": "df[df['Claim_Amount'] == 1154]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2023-06-17.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-06-17')]"
+  },
+  {
+    "Query": "Count how many claims where Claim Amount less than 589",
+    "pandas_code": "df[df['Claim_Amount'] < 589].shape[0]"
+  },
+  {
+    "Query": "Get claims where Deduction Amount between 3 and 8 and Provider Code is 'PC103191'.",
+    "pandas_code": "df[('Deduction_Amount'] >= 3.03) & ('Deduction_Amount'] <= 8.66) & 'Provider_Code'] == 'PC103191']"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 269289 and 274359 and Claim Created Date before 2023-06-20.",
+    "pandas_code": "df[('Claim_Id'] >= 269289.01) & ('Claim_Id'] <= 274359.63) & 'Claim_Created_Date'] < pd.to_datetime('2023-06-20')]"
+  },
+  {
+    "Query": "Number of claims where Claim Id between 266937 and 277046",
+    "pandas_code": "df[(df['Claim_Id'] >= 266937.64) & (df['Claim_Id'] <= 277046.51)].shape[0]"
+  },
+  {
+    "Query": "Tell me claims where Status Code contains 'AP2' and Scheme contains 'SME 2-'.",
+    "pandas_code": "df['Status_Code'].str.contains(r'AP2', na=False) & 'Scheme'].str.contains(r'SME 2-', na=False)]"
+  },
+  {
+    "Query": "Find claims where Provider Code is 'PC101171'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101171']"
+  },
+  {
+    "Query": "Find claims where Deduction Amount equal to 8.86 and Claim Number is 'CBGDC23070035-00'.",
+    "pandas_code": "df['Deduction_Amount'] == 8.86 & 'Claim_Number'] == 'CBGDC23070035-00']"
+  },
+  {
+    "Query": "Get claims where Provider Code is 'PC103209' and Provider Name is 'RAFFLESMEDICAL (Raffles Hospital)'.",
+    "pandas_code": "df['Provider_Code'] == 'PC103209' & 'Provider_Name'] == 'RAFFLESMEDICAL (Raffles Hospital)']"
+  },
+  {
+    "Query": "Tell me tell me claims where Status is 'Duplicate' and Product contains 'Hospit'.",
+    "pandas_code": "df['Status'] == 'Duplicate' & 'Product'].str.contains(r'Hospit', na=False)]"
+  },
+  {
+    "Query": "Tell me claims where Status Code contains 'DU0' and Loss Date on 2023-07-22.",
+    "pandas_code": "df['Status_Code'].str.contains(r'DU0', na=False) & 'Loss_Date'] == pd.to_datetime('2023-07-22')]"
+  },
+  {
+    "Query": "List claims where Claim Id less than 277392.",
+    "pandas_code": "df[df['Claim_Id'] < 277392]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount greater than 570 and Loss Type is 'Hospitalisation'.",
+    "pandas_code": "df['Claim_Amount'] > 570 & 'Loss_Type'] == 'Hospitalisation']"
+  },
+  {
+    "Query": "Tell me claims where Provider Name contains 'TAN TO' and Claim Type is 'Reimbursement'.",
+    "pandas_code": "df['Provider_Name'].str.contains(r'TAN TO', na=False) & 'Claim_Type'] == 'Reimbursement']"
+  },
+  {
+    "Query": "Provide claims where Deduction Amount between 2 and 9.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 2.92) & (df['Deduction_Amount'] <= 9.24)]"
+  },
+  {
+    "Query": "Display claims where Approved Amount at most 965 and Deduction Amount at least 3.14.",
+    "pandas_code": "df['Approved_Amount'] <= 965 & 'Deduction_Amount'] >= 3.14]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 277 and 1415.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 277.22) & (df['Approved_Amount'] <= 1415.24)]"
+  },
+  {
+    "Query": "Is there retrieve claims where Claim Created Date after 2023-07-03 and Loss Type contains 'Dental'.",
+    "pandas_code": "df['Claim_Created_Date'] > pd.to_datetime('2023-07-03') & 'Loss_Type'].str.contains(r'Dental', na=False)]"
+  },
+  {
+    "Query": "Find claims where Loss Type contains 'Specia' and Claim Number is 'CBGDC23058968-00'.",
+    "pandas_code": "df['Loss_Type'].str.contains(r'Specia', na=False) & 'Claim_Number'] == 'CBGDC23058968-00']"
+  },
+  {
+    "Query": "How many claims where Claim Created Date on 2023-06-16",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-06-16')].shape[0]"
+  },
+  {
+    "Query": "List List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Get Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Count how many claims where Claim Id between 269331 and 275029",
+    "pandas_code": "df[(df['Claim_Id'] >= 269331.53) & (df['Claim_Id'] <= 275029.41)].shape[0]"
+  },
+  {
+    "Query": "Give me claims where Approved Amount between 631 and 1131.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 631.73) & (df['Approved_Amount'] <= 1131.52)]"
+  },
+  {
+    "Query": "Display claims where Claim Number contains 'CBGDC2' and Policy Number is 'BGDC211000035-02-000'.",
+    "pandas_code": "df['Claim_Number'].str.contains(r'CBGDC2', na=False) & 'Policy_Number'] == 'BGDC211000035-02-000']"
+  },
+  {
+    "Query": "Is there show claims where Product is 'Dental' and Claim Amount at most 203.",
+    "pandas_code": "df['Product'] == 'Dental' & 'Claim_Amount'] <= 203]"
+  },
+  {
+    "Query": "Display claims where Loss Type is 'Hospitalisation' and Product contains 'Dental'.",
+    "pandas_code": "df['Loss_Type'] == 'Hospitalisation' & 'Product'].str.contains(r'Dental', na=False)]"
+  },
+  {
+    "Query": "Tell me claims where Deduction Amount at most 3.81 and Patient Name is 'Mr Junfeng Andre Liang'.",
+    "pandas_code": "df['Deduction_Amount'] <= 3.81 & 'Patient_Name'] == 'Mr Junfeng Andre Liang']"
+  },
+  {
+    "Query": "Is there find claims where Approved Amount greater than 455.0.",
+    "pandas_code": "df[df['Approved_Amount'] > 455.0]"
+  },
+  {
+    "Query": "Get claims where Claim Number is 'CBGDC23060375-00' and Patient Name is 'Mr Wan Jun Zhang'.",
+    "pandas_code": "df['Claim_Number'] == 'CBGDC23060375-00' & 'Patient_Name'] == 'Mr Wan Jun Zhang']"
+  },
+  {
+    "Query": "Show claims where Patient Name is 'Ms Shi Ya Kayleigh Avery Wong' and Claim Number is 'CBGDC23061360-00'.",
+    "pandas_code": "df['Patient_Name'] == 'Ms Shi Ya Kayleigh Avery Wong' & 'Claim_Number'] == 'CBGDC23061360-00']"
+  },
+  {
+    "Query": "Can you tell me claims where Claim Created Date after 2023-02-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-23')]"
+  },
+  {
+    "Query": "Tell me claims where Scheme contains 'SME 1-' and Provider Code contains 'PC1040'.",
+    "pandas_code": "df['Scheme'].str.contains(r'SME 1-', na=False) & 'Provider_Code'].str.contains(r'PC1040', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2023-01-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-23')]"
+  },
+  {
+    "Query": "Is there fetch claims where Claim Created Date after 2023-03-05 and Provider Code is 'PC104077'.",
+    "pandas_code": "df['Claim_Created_Date'] > pd.to_datetime('2023-03-05') & 'Provider_Code'] == 'PC104077']"
+  },
+  {
+    "Query": "Tell me fetch claims where Claim Created Date between 2022-12-30 and 2023-06-25.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-30')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-25'))]"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name contains 'Mr Jia' and Scheme is 'New Template 3 '.",
+    "pandas_code": "df['Patient_Name'].str.contains(r'Mr Jia', na=False) & 'Scheme'] == 'New Template 3 ']"
+  },
+  {
+    "Query": "List claims where Scheme contains 'SME 1-' and Product contains 'Genera'.",
+    "pandas_code": "df['Scheme'].str.contains(r'SME 1-', na=False) & 'Product'].str.contains(r'Genera', na=False)]"
+  },
+  {
+    "Query": "Find claims where Provider Code is 'PC103185' and Approved Amount at least 240.06.",
+    "pandas_code": "df['Provider_Code'] == 'PC103185' & 'Approved_Amount'] >= 240.06]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-04-07.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-07')]"
+  },
+  {
+    "Query": "Tell me list claims where Scheme contains 'Per Di' and Claim Id equal to 276230.",
+    "pandas_code": "df['Scheme'].str.contains(r'Per Di', na=False) & 'Claim_Id'] == 276230]"
+  },
+  {
+    "Query": "Retrieve claims where Deduction Amount between 1 and 8 and Policy Number is 'BGDC201000022-02-000'.",
+    "pandas_code": "df[('Deduction_Amount'] >= 1.71) & ('Deduction_Amount'] <= 8.58) & 'Policy_Number'] == 'BGDC201000022-02-000']"
+  },
+  {
+    "Query": "How many claims where Policy Number contains 'BGDC21'",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDC21', na=False)].shape[0]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Code is 'PC101350' and Status is 'Duplicate'.",
+    "pandas_code": "df['Provider_Code'] == 'PC101350' & 'Status'] == 'Duplicate']"
+  },
+  {
+    "Query": "Provide claims where Claim Amount less than 374.",
+    "pandas_code": "df[df['Claim_Amount'] < 374]"
+  },
+  {
+    "Query": "Provide claims where Claim Id at most 270013.",
+    "pandas_code": "df[df['Claim_Id'] <= 270013]"
+  },
+  {
+    "Query": "Provide claims where Claim Number is 'CBGDP23038094-00' and Claim Type is 'Cashless'.",
+    "pandas_code": "df['Claim_Number'] == 'CBGDP23038094-00' & 'Claim_Type'] == 'Cashless']"
+  },
+  {
+    "Query": "Provide claims where Status Code is 'DU0' and Product contains 'Group '.",
+    "pandas_code": "df['Status_Code'] == 'DU0' & 'Product'].str.contains(r'Group ', na=False)]"
+  },
+  {
+    "Query": "Find claims where Deduction Amount at most 2.38.",
+    "pandas_code": "df[df['Deduction_Amount'] <= 2.38]"
+  },
+  {
+    "Query": "Provide claims where Provider Code contains 'PC1037' and Approved Amount at least 1412 How many are there?",
+    "pandas_code": "df['Provider_Code'].str.contains(r'PC1037', na=False) & 'Approved_Amount'] >= 1412].shape[0]"
+  },
+  {
+    "Query": "List claims where Status Code is 'RJ2' and Provider Name contains 'Tooth '.",
+    "pandas_code": "df['Status_Code'] == 'RJ2' & 'Provider_Name'].str.contains(r'Tooth ', na=False)]"
+  },
+  {
+    "Query": "Display claims where Provider Code contains 'PC1013' and Status is 'Duplicate'.",
+    "pandas_code": "df['Provider_Code'].str.contains(r'PC1013', na=False) & 'Status'] == 'Duplicate']"
+  },
+  {
+    "Query": "Display claims where Status is 'Payment Approval'.",
+    "pandas_code": "df[df['Status'] == 'Payment Approval']"
+  },
+  {
+    "Query": "Find claims where Claim Id between 265198 and 279492.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265198.07) & (df['Claim_Id'] <= 279492.14)]"
+  },
+  {
+    "Query": "Do we have find claims where Loss Type is 'Specialist Treatment' and Approved Amount between 405 and 1230.",
+    "pandas_code": "df['Loss_Type'] == 'Specialist Treatment' & ('Approved_Amount'] >= 405.51) & ('Approved_Amount'] <= 1230.49)]"
+  },
+  {
+    "Query": "Show claims where Status Code is 'PY1' and Deduction Amount at least 8.78.",
+    "pandas_code": "df['Status_Code'] == 'PY1' & 'Deduction_Amount'] >= 8.78]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 800 and 1348 and Claim Created Date between 2022-12-05 and 2023-01-05.",
+    "pandas_code": "df[('Claim_Amount'] >= 800.02) & ('Claim_Amount'] <= 1348.37) & ('Claim_Created_Date'] >= pd.to_datetime('2022-12-05')) & ('Claim_Created_Date'] <= pd.to_datetime('2023-01-05'))]"
+  },
+  {
+    "Query": "Find claims where Claim Number contains 'CBGDC2' and Loss Type contains 'Hospit'.",
+    "pandas_code": "df['Claim_Number'].str.contains(r'CBGDC2', na=False) & 'Loss_Type'].str.contains(r'Hospit', na=False)]"
+  },
+  {
+    "Query": "Tell me How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2022-12-13 and 2023-01-28.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-13')) & (df['Loss_Date'] <= pd.to_datetime('2023-01-28'))]"
+  },
+  {
+    "Query": "Fetch claims where Deduction Amount at least 8.4.",
+    "pandas_code": "df[df['Deduction_Amount'] >= 8.4]"
+  },
+  {
+    "Query": "Please tell me How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Show claims where Status is 'Payment Approval' and Scheme is 'Template 2' How many are there?",
+    "pandas_code": "df['Status'] == 'Payment Approval' & 'Scheme'] == 'Template 2'].shape[0]"
+  },
+  {
+    "Query": "Do we have display claims where Provider Code is 'PC102378' and Approved Amount at most 876.71.",
+    "pandas_code": "df['Provider_Code'] == 'PC102378' & 'Approved_Amount'] <= 876.71]"
+  },
+  {
+    "Query": "Find claims where Provider Code is 'PC104076'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104076']"
+  },
+  {
+    "Query": "Do we have show Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Provide claims where Provider Code is 'PC104076' and Claim Amount less than 1051.",
+    "pandas_code": "df['Provider_Code'] == 'PC104076' & 'Claim_Amount'] < 1051]"
+  },
+  {
+    "Query": "Tell me claims where Loss Date before 2022-11-30.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-30')]"
+  },
+  {
+    "Query": "Give me claims where Claim Id between 267450 and 275736.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267450.09) & (df['Claim_Id'] <= 275736.52)]"
+  },
+  {
+    "Query": "Number of claims where Loss Date on 2022-11-17",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-17')].shape[0]"
+  },
+  {
+    "Query": "Tell me claims where Status is 'Duplicate' and Loss Type is 'GP Treatment'.",
+    "pandas_code": "df['Status'] == 'Duplicate' & 'Loss_Type'] == 'GP Treatment']"
+  },
+  {
+    "Query": "Get claims where Claim Number is 'CBGDP23044164-00' and Claim Created Date between 2023-02-20 and 2023-05-17 How many are there?",
+    "pandas_code": "df['Claim_Number'] == 'CBGDP23044164-00' & ('Claim_Created_Date'] >= pd.to_datetime('2023-02-20')) & ('Claim_Created_Date'] <= pd.to_datetime('2023-05-17'))].shape[0]"
+  },
+  {
+    "Query": "Find claims where Claim Amount greater than 1154.",
+    "pandas_code": "df[df['Claim_Amount'] > 1154]"
+  },
+  {
+    "Query": "Retrieve Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Count how many me claims where Provider Code contains 'PC1017'",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1017', na=False)].shape[0]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-07-14 and 2023-07-15.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-07-14')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-15'))]"
+  },
+  {
+    "Query": "Give me claims where Claim Amount at most 905 and Provider Name contains 'KANDAN'.",
+    "pandas_code": "df['Claim_Amount'] <= 905 & 'Provider_Name'].str.contains(r'KANDAN', na=False)]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2023-04-14 and Policy Number contains 'BGDP22'.",
+    "pandas_code": "df['Loss_Date'] > pd.to_datetime('2023-04-14') & 'Policy_Number'].str.contains(r'BGDP22', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Patient Name is 'Ms Junling Fang'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Junling Fang']"
+  },
+  {
+    "Query": "Get claims where Claim Id equal to 262027.",
+    "pandas_code": "df[df['Claim_Id'] == 262027]"
+  },
+  {
+    "Query": "Tell me display claims where Deduction Amount equal to 1.43.",
+    "pandas_code": "df[df['Deduction_Amount'] == 1.43]"
+  },
+  {
+    "Query": "Tell me fetch claims where Deduction Amount at most 4.3.",
+    "pandas_code": "df[df['Deduction_Amount'] <= 4.3]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name is 'RAFFLESMEDICAL (Raffles Hospital)'.",
+    "pandas_code": "df[df['Provider_Name'] == 'RAFFLESMEDICAL (Raffles Hospital)']"
+  },
+  {
+    "Query": "Provide claims where Policy Number is 'BGDC211000090-02-000' and Approved Amount greater than 982.4.",
+    "pandas_code": "df['Policy_Number'] == 'BGDC211000090-02-000' & 'Approved_Amount'] > 982.4]"
+  },
+  {
+    "Query": "Fetch claims where Provider Code is 'PC100001' and Policy Number contains 'BGDC21'.",
+    "pandas_code": "df['Provider_Code'] == 'PC100001' & 'Policy_Number'].str.contains(r'BGDC21', na=False)]"
+  },
+  {
+    "Query": "Get claims where Provider Code is 'PC101195' and Claim Type contains 'Reimbu'.",
+    "pandas_code": "df['Provider_Code'] == 'PC101195' & 'Claim_Type'].str.contains(r'Reimbu', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Status Code is 'DU0' and Claim Type is 'Cashless'.",
+    "pandas_code": "df['Status_Code'] == 'DU0' & 'Claim_Type'] == 'Cashless']"
+  },
+  {
+    "Query": "Provide claims where Claim Amount at most 738 and Provider Code is 'PC103209'.",
+    "pandas_code": "df['Claim_Amount'] <= 738 & 'Provider_Code'] == 'PC103209']"
+  },
+  {
+    "Query": "Find claims where Policy Number is 'BGDC211000285-02-000' and Claim Number contains 'CBGDC2' How many are there?",
+    "pandas_code": "df['Policy_Number'] == 'BGDC211000285-02-000' & 'Claim_Number'].str.contains(r'CBGDC2', na=False)].shape[0]"
+  },
+  {
+    "Query": "Tell me Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "List claims where Claim Amount equal to 1605.",
+    "pandas_code": "df[df['Claim_Amount'] == 1605]"
+  },
+  {
+    "Query": "Display claims where Provider Name is 'DENTAL FOCUS JUNCTION 10 CLINIC' and Loss Type is 'Specialist Treatment' How many are there?",
+    "pandas_code": "df['Provider_Name'] == 'DENTAL FOCUS JUNCTION 10 CLINIC' & 'Loss_Type'] == 'Specialist Treatment'].shape[0]"
+  },
+  {
+    "Query": "Number of claims where Loss Type is 'Specialist Treatment'",
+    "pandas_code": "df[df['Loss_Type'] == 'Specialist Treatment'].shape[0]"
+  },
+  {
+    "Query": "Display Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Give me claims where Policy Number is 'BGDP211000108-01-000' and Scheme is 'SME 1-1BPTE'.",
+    "pandas_code": "df['Policy_Number'] == 'BGDP211000108-01-000' & 'Scheme'] == 'SME 1-1BPTE']"
+  },
+  {
+    "Query": "Tell me claims where Approved Amount between 235 and 837 and Status Code contains 'AP2'.",
+    "pandas_code": "df[('Approved_Amount'] >= 235.26) & ('Approved_Amount'] <= 837.78) & 'Status_Code'].str.contains(r'AP2', na=False)]"
+  },
+  {
+    "Query": "Give me claims where Scheme is 'SME 2-SP-1000' and Patient Name is 'Mr Hao En Oliver Kok'.",
+    "pandas_code": "df['Scheme'] == 'SME 2-SP-1000' & 'Patient_Name'] == 'Mr Hao En Oliver Kok']"
+  },
+  {
+    "Query": "Show Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Show claims where Loss Type contains 'Hospit' and Patient Name is 'Ms Qiu Xuan Lee'.",
+    "pandas_code": "df['Loss_Type'].str.contains(r'Hospit', na=False) & 'Patient_Name'] == 'Ms Qiu Xuan Lee']"
+  },
+  {
+    "Query": "Get claims where Scheme is 'SME 3-SP-CO-Panel Only' and Claim Created Date between 2022-11-15 and 2023-05-29 How many are there?",
+    "pandas_code": "df['Scheme'] == 'SME 3-SP-CO-Panel Only' & ('Claim_Created_Date'] >= pd.to_datetime('2022-11-15')) & ('Claim_Created_Date'] <= pd.to_datetime('2023-05-29'))].shape[0]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2022-11-25 and Claim Amount at most 1328.",
+    "pandas_code": "df['Loss_Date'] > pd.to_datetime('2022-11-25') & 'Claim_Amount'] <= 1328]"
+  },
+  {
+    "Query": "Display claims where Claim Id equal to 268386.",
+    "pandas_code": "df[df['Claim_Id'] == 268386]"
+  },
+  {
+    "Query": "Give me claims where Approved Amount between 95 and 1429 and Patient Name contains 'Mr Kan'.",
+    "pandas_code": "df[('Approved_Amount'] >= 95.11) & ('Approved_Amount'] <= 1429.36) & 'Patient_Name'].str.contains(r'Mr Kan', na=False)]"
+  },
+  {
+    "Query": "List claims where Status Code contains 'DU0' and Claim Created Date on 2023-06-03.",
+    "pandas_code": "df['Status_Code'].str.contains(r'DU0', na=False) & 'Claim_Created_Date'] == pd.to_datetime('2023-06-03')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Number is 'CBGDC23059207-00' and Loss Type contains 'Specia'.",
+    "pandas_code": "df['Claim_Number'] == 'CBGDC23059207-00' & 'Loss_Type'].str.contains(r'Specia', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2022-11-15 and 2023-07-06.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-15')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-06'))]"
+  },
+  {
+    "Query": "Tell me claims where Policy Number is 'BGDP221000151-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000151-01-000']"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount at most 580 and Claim Id between 261589 and 277704.",
+    "pandas_code": "df['Approved_Amount'] <= 580 & ('Claim_Id'] >= 261589.1) & ('Claim_Id'] <= 277704.06)]"
+  },
+  {
+    "Query": "Show claims where Deduction Amount greater than 8.41.",
+    "pandas_code": "df[df['Deduction_Amount'] > 8.41]"
+  },
+  {
+    "Query": "Give me claims where Claim Amount less than 451 and Claim Number is 'CBGDC23062107-00' How many are there?",
+    "pandas_code": "df['Claim_Amount'] < 451 & 'Claim_Number'] == 'CBGDC23062107-00'].shape[0]"
+  },
+  {
+    "Query": "Display claims where Claim Amount between 773 and 1136.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 773.83) & (df['Claim_Amount'] <= 1136.9)]"
+  },
+  {
+    "Query": "I want to know tell me claims where Deduction Amount at least 0.74.",
+    "pandas_code": "df[df['Deduction_Amount'] >= 0.74]"
+  },
+  {
+    "Query": "Show claims where Claim Amount less than 283.",
+    "pandas_code": "df[df['Claim_Amount'] < 283]"
+  },
+  {
+    "Query": "Tell me claims where Deduction Amount greater than 2.79.",
+    "pandas_code": "df[df['Deduction_Amount'] > 2.79]"
+  },
+  {
+    "Query": "Count how many claims where Loss Date after 2023-01-01",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-01-01')].shape[0]"
+  },
+  {
+    "Query": "Provide claims where Scheme contains 'SME 1-' and Approved Amount equal to 174.39.",
+    "pandas_code": "df['Scheme'].str.contains(r'SME 1-', na=False) & 'Approved_Amount'] == 174.39]"
+  },
+  {
+    "Query": "Give me List claims where Approved_Amount == Claim_Amount.",
+    "pandas_code": "df[df['Approved_Amount'] == df['Claim_Amount']]"
+  },
+  {
+    "Query": "Tell me give me claims where Deduction Amount between 1 and 7.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 1.91) & (df['Deduction_Amount'] <= 7.06)]"
+  },
+  {
+    "Query": "Give me claims where Product is 'Specialist' and Status contains 'Paymen'.",
+    "pandas_code": "df['Product'] == 'Specialist' & 'Status'].str.contains(r'Paymen', na=False)]"
+  },
+  {
+    "Query": "Give me claims where Loss Date on 2023-07-03.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-07-03')]"
+  },
+  {
+    "Query": "Give me claims where Policy Number is 'BGDP221001401-00-000' and Provider Name is 'Physio Down Under Pte ltd'.",
+    "pandas_code": "df['Policy_Number'] == 'BGDP221001401-00-000' & 'Provider_Name'] == 'Physio Down Under Pte ltd']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-03-07 and 2023-06-27 and Claim Amount at most 307 How many are there?",
+    "pandas_code": "df[('Claim_Created_Date'] >= pd.to_datetime('2023-03-07')) & ('Claim_Created_Date'] <= pd.to_datetime('2023-06-27')) & 'Claim_Amount'] <= 307].shape[0]"
+  },
+  {
+    "Query": "Provide claims where Provider Code is 'PC104074' and Product is 'Hospital & Surgical'.",
+    "pandas_code": "df['Provider_Code'] == 'PC104074' & 'Product'] == 'Hospital & Surgical']"
+  },
+  {
+    "Query": "Tell me claims where Claim Created Date after 2023-04-01.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-01')]"
+  },
+  {
+    "Query": "List claims where Deduction Amount less than 7.75.",
+    "pandas_code": "df[df['Deduction_Amount'] < 7.75]"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 663 and 959.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 663.65) & (df['Approved_Amount'] <= 959.02)]"
+  },
+  {
+    "Query": "Give me claims where Product contains 'Dental' and Loss Type is 'GP Treatment'.",
+    "pandas_code": "df['Product'].str.contains(r'Dental', na=False) & 'Loss_Type'] == 'GP Treatment']"
+  },
+  {
+    "Query": "Display claims where Status Code contains 'PY1' and Patient Name is 'Ms Si Ling Tan'.",
+    "pandas_code": "df['Status_Code'].str.contains(r'PY1', na=False) & 'Patient_Name'] == 'Ms Si Ling Tan']"
+  },
+  {
+    "Query": "Number of me claims where Claim Amount less than 1355.02",
+    "pandas_code": "df[df['Claim_Amount'] < 1355.02].shape[0]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2023-06-19.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-06-19')]"
+  },
+  {
+    "Query": "Show claims where Product is 'Specialist' and Patient Name is 'Mr Wei Siang Kenny Mah'.",
+    "pandas_code": "df['Product'] == 'Specialist' & 'Patient_Name'] == 'Mr Wei Siang Kenny Mah']"
+  },
+  {
+    "Query": "How many me claims where Loss Type contains 'Hospit'",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Hospit', na=False)].shape[0]"
+  },
+  {
+    "Query": "Tell me claims where Loss Date after 2023-02-04.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-02-04')]"
+  },
+  {
+    "Query": "Show claims where Scheme is 'SME 1-SP-CO-1500' and Claim Type contains 'Reimbu'.",
+    "pandas_code": "df['Scheme'] == 'SME 1-SP-CO-1500' & 'Claim_Type'].str.contains(r'Reimbu', na=False)]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 114 and 824.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 114.02) & (df['Approved_Amount'] <= 824.23)]"
+  },
+  {
+    "Query": "List claims where Claim Id greater than 269895 and Patient Name is 'Ms Lorkhumtong Praphim'.",
+    "pandas_code": "df['Claim_Id'] > 269895 & 'Patient_Name'] == 'Ms Lorkhumtong Praphim']"
+  },
+  {
+    "Query": "Give me claims where Approved Amount greater than 349.85 and Claim Id at most 262584.",
+    "pandas_code": "df['Approved_Amount'] > 349.85 & 'Claim_Id'] <= 262584]"
+  },
+  {
+    "Query": "Give me claims where Deduction Amount between 2 and 9.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 2.06) & (df['Deduction_Amount'] <= 9.6)]"
+  },
+  {
+    "Query": "Display claims where Policy Number contains 'BGDC21' and Approved Amount less than 788.",
+    "pandas_code": "df['Policy_Number'].str.contains(r'BGDC21', na=False) & 'Approved_Amount'] < 788]"
+  },
+  {
+    "Query": "Find claims where Provider Code is 'PC103779' and Scheme contains 'New Te'.",
+    "pandas_code": "df['Provider_Code'] == 'PC103779' & 'Scheme'].str.contains(r'New Te', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Status Code is 'DU0' and Policy Number is 'BGDP211000108-01-000'.",
+    "pandas_code": "df['Status_Code'] == 'DU0' & 'Policy_Number'] == 'BGDP211000108-01-000']"
+  },
+  {
+    "Query": "Retrieve claims where Provider Code is 'PC101171' and Deduction Amount between 2 and 9.",
+    "pandas_code": "df['Provider_Code'] == 'PC101171' & ('Deduction_Amount'] >= 2.51) & ('Deduction_Amount'] <= 9.56)]"
+  },
+  {
+    "Query": "Tell me claims where Claim Type is 'CHS' and Status Code is 'PY3' How many are there?",
+    "pandas_code": "df['Claim_Type'] == 'CHS' & 'Status_Code'] == 'PY3'].shape[0]"
+  },
+  {
+    "Query": "Do we have display claims where Patient Name is 'Mr Kang Jun Ng' and Provider Code is 'PC100466'.",
+    "pandas_code": "df['Patient_Name'] == 'Mr Kang Jun Ng' & 'Provider_Code'] == 'PC100466']"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount at most 191 and Provider Code contains 'PC1022'.",
+    "pandas_code": "df['Claim_Amount'] <= 191 & 'Provider_Code'].str.contains(r'PC1022', na=False)]"
+  },
+  {
+    "Query": "List claims where Loss Type is 'GP Treatment' and Product is 'Group Specialist'.",
+    "pandas_code": "df['Loss_Type'] == 'GP Treatment' & 'Product'] == 'Group Specialist']"
+  },
+  {
+    "Query": "Find claims where Deduction Amount less than 4.19.",
+    "pandas_code": "df[df['Deduction_Amount'] < 4.19]"
+  },
+  {
+    "Query": "Tell me display claims where Loss Date before 2023-05-13.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-13')]"
+  },
+  {
+    "Query": "Fetch claims where Provider Code is 'PC100541' and Provider Name is 'Bukit Batok Polyclinic'.",
+    "pandas_code": "df['Provider_Code'] == 'PC100541' & 'Provider_Name'] == 'Bukit Batok Polyclinic']"
+  },
+  {
+    "Query": "List claims where Claim Id at least 265790.3 and Status is 'Duplicate' How many are there?",
+    "pandas_code": "df['Claim_Id'] >= 265790.3 & 'Status'] == 'Duplicate'].shape[0]"
+  },
+  {
+    "Query": "Show claims where Provider Name is 'Q & M DENTAL SURGERY (SERANGOON CENTRAL) PTE LTD' and Provider Code is 'PC103209'.",
+    "pandas_code": "df['Provider_Name'] == 'Q & M DENTAL SURGERY (SERANGOON CENTRAL) PTE LTD' & 'Provider_Code'] == 'PC103209']"
+  },
+  {
+    "Query": "Retrieve claims where Status contains 'Approv' and Claim Id at most 276975 How many are there?",
+    "pandas_code": "df['Status'].str.contains(r'Approv', na=False) & 'Claim_Id'] <= 276975].shape[0]"
+  },
+  {
+    "Query": "Display claims where Claim Number contains 'CBGDC2' and Claim Id greater than 268402.",
+    "pandas_code": "df['Claim_Number'].str.contains(r'CBGDC2', na=False) & 'Claim_Id'] > 268402]"
+  },
+  {
+    "Query": "How many me claims where Claim Id less than 269409.57",
+    "pandas_code": "df[df['Claim_Id'] < 269409.57].shape[0]"
+  },
+  {
+    "Query": "Show claims where Approved Amount less than 1339.63 and Deduction Amount between 2 and 6.",
+    "pandas_code": "df['Approved_Amount'] < 1339.63 & ('Deduction_Amount'] >= 2.27) & ('Deduction_Amount'] <= 6.29)]"
+  },
+  {
+    "Query": "List claims where Patient Name contains 'Mr Edw' and Claim Created Date between 2023-04-29 and 2023-05-23 How many are there?",
+    "pandas_code": "df['Patient_Name'].str.contains(r'Mr Edw', na=False) & ('Claim_Created_Date'] >= pd.to_datetime('2023-04-29')) & ('Claim_Created_Date'] <= pd.to_datetime('2023-05-23'))].shape[0]"
+  },
+  {
+    "Query": "Show claims where Provider Code is 'PC102812' and Provider Name is 'Jurong Medical Centre' How many are there?",
+    "pandas_code": "df['Provider_Code'] == 'PC102812' & 'Provider_Name'] == 'Jurong Medical Centre'].shape[0]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2023-01-18 and 2023-07-31.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-18')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-31'))]"
+  },
+  {
+    "Query": "Tell me claims where Approved Amount greater than 419.5.",
+    "pandas_code": "df[df['Approved_Amount'] > 419.5]"
+  },
+  {
+    "Query": "Find Sum of Approved_Amount by Provider_Name.",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum()"
+  },
+  {
+    "Query": "Get claims where Status is 'Paid' and Patient Name is 'Mr Seng Wee Terence Quek'.",
+    "pandas_code": "df['Status'] == 'Paid' & 'Patient_Name'] == 'Mr Seng Wee Terence Quek']"
+  },
+  {
+    "Query": "Retrieve Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2022-11-15 and Provider Code is 'PC101668'.",
+    "pandas_code": "df['Claim_Created_Date'] == pd.to_datetime('2022-11-15') & 'Provider_Code'] == 'PC101668']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2022-11-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-11-16')]"
+  },
+  {
+    "Query": "Display claims where Patient Name is 'Ms Lai Nyuk Lam' and Claim Number is 'CBGDC23069040-00'.",
+    "pandas_code": "df['Patient_Name'] == 'Ms Lai Nyuk Lam' & 'Claim_Number'] == 'CBGDC23069040-00']"
+  },
+  {
+    "Query": "Provide claims where Product is 'Hospital & Surgical' and Loss Date between 2023-01-18 and 2023-02-13.",
+    "pandas_code": "df['Product'] == 'Hospital & Surgical' & ('Loss_Date'] >= pd.to_datetime('2023-01-18')) & ('Loss_Date'] <= pd.to_datetime('2023-02-13'))]"
+  },
+  {
+    "Query": "Tell me fetch claims where Provider Name contains 'Other ' and Status is 'Void'.",
+    "pandas_code": "df['Provider_Name'].str.contains(r'Other ', na=False) & 'Status'] == 'Void']"
+  },
+  {
+    "Query": "I want to know give me claims where Loss Date after 2023-04-10 and Policy Number is 'BGDP221001191-00-000'.",
+    "pandas_code": "df['Loss_Date'] > pd.to_datetime('2023-04-10') & 'Policy_Number'] == 'BGDP221001191-00-000']"
+  },
+  {
+    "Query": "Could you provide claims where Loss Date on 2023-05-23.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-23')]"
+  },
+  {
+    "Query": "Give me claims where Approved Amount greater than 329.",
+    "pandas_code": "df[df['Approved_Amount'] > 329]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount greater than 1519.",
+    "pandas_code": "df[df['Approved_Amount'] > 1519]"
+  },
+  {
+    "Query": "Find claims where Claim Amount at least 1193 and Approved Amount between 558 and 1323.",
+    "pandas_code": "df['Claim_Amount'] >= 1193 & ('Approved_Amount'] >= 558.98) & ('Approved_Amount'] <= 1323.7)]"
+  },
+  {
+    "Query": "Tell me claims where Loss Date before 2023-02-16.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-16')]"
+  },
+  {
+    "Query": "Tell me claims where Approved Amount equal to 1479.",
+    "pandas_code": "df[df['Approved_Amount'] == 1479]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2023-01-10 and 2023-02-04 and Claim Amount between 307 and 1250 How many are there?",
+    "pandas_code": "df[('Claim_Created_Date'] >= pd.to_datetime('2023-01-10')) & ('Claim_Created_Date'] <= pd.to_datetime('2023-02-04')) & ('Claim_Amount'] >= 307.64) & ('Claim_Amount'] <= 1250.82)].shape[0]"
+  },
+  {
+    "Query": "Give me claims where Loss Date after 2023-04-09.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-09')]"
+  },
+  {
+    "Query": "Get claims where Deduction Amount at least 2.44 and Claim Created Date between 2022-11-19 and 2023-06-24.",
+    "pandas_code": "df['Deduction_Amount'] >= 2.44 & ('Claim_Created_Date'] >= pd.to_datetime('2022-11-19')) & ('Claim_Created_Date'] <= pd.to_datetime('2023-06-24'))]"
+  },
+  {
+    "Query": "Give me claims where Approved Amount at most 142 and Product is 'Group Specialist' How many are there?",
+    "pandas_code": "df['Approved_Amount'] <= 142 & 'Product'] == 'Group Specialist'].shape[0]"
+  },
+  {
+    "Query": "List claims where Claim Amount equal to 1109 and Deduction Amount greater than 1.57.",
+    "pandas_code": "df['Claim_Amount'] == 1109 & 'Deduction_Amount'] > 1.57]"
+  },
+  {
+    "Query": "Retrieve claims where Policy Number is 'BGDP211000277-01-000' and Status Code is 'DU0'.",
+    "pandas_code": "df['Policy_Number'] == 'BGDP211000277-01-000' & 'Status_Code'] == 'DU0']"
+  },
+  {
+    "Query": "Number of claims where Claim Type contains 'CHS'",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'CHS', na=False)].shape[0]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount at most 1089 and Claim Number is 'CBGDC23062696-00'.",
+    "pandas_code": "df['Claim_Amount'] <= 1089 & 'Claim_Number'] == 'CBGDC23062696-00']"
+  },
+  {
+    "Query": "Provide claims where Approved Amount at least 1364.",
+    "pandas_code": "df[df['Approved_Amount'] >= 1364]"
+  },
+  {
+    "Query": "Provide claims where Claim Id equal to 275075 and Policy Number is 'BGDC221000022-01-000'.",
+    "pandas_code": "df['Claim_Id'] == 275075 & 'Policy_Number'] == 'BGDC221000022-01-000']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2023-05-13 and 2023-06-10.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-05-13')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-10'))]"
+  },
+  {
+    "Query": "Fetch claims where Patient Name contains 'Ms Yu ' and Status Code contains 'RJ2'.",
+    "pandas_code": "df['Patient_Name'].str.contains(r'Ms Yu ', na=False) & 'Status_Code'].str.contains(r'RJ2', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2022-11-12.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-12')]"
+  },
+  {
+    "Query": "Find claims where Claim Id greater than 268014.",
+    "pandas_code": "df[df['Claim_Id'] > 268014]"
+  },
+  {
+    "Query": "Provide claims where Claim Number is 'CBGDC23069250-00' and Status Code is 'PY1'.",
+    "pandas_code": "df['Claim_Number'] == 'CBGDC23069250-00' & 'Status_Code'] == 'PY1']"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 477 and 1380.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 477.23) & (df['Approved_Amount'] <= 1380.57)]"
+  },
+  {
+    "Query": "Tell me claims where Loss Date between 2022-11-30 and 2023-06-22.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-30')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-22'))]"
+  },
+  {
+    "Query": "Please display Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Tell me provide claims where Policy Number is 'BGDC211000448-01-000' and Status is 'Paid'.",
+    "pandas_code": "df['Policy_Number'] == 'BGDC211000448-01-000' & 'Status'] == 'Paid']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date between 2023-03-27 and 2023-05-10.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-27')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-10'))]"
+  },
+  {
+    "Query": "Find claims where Policy Number contains 'BGDP21' and Claim Number is 'CBGDC23069923-00'.",
+    "pandas_code": "df['Policy_Number'].str.contains(r'BGDP21', na=False) & 'Claim_Number'] == 'CBGDC23069923-00']"
+  },
+  {
+    "Query": "List claims where Claim Number contains 'CBGDC2' and Claim Type is 'Reimbursement' How many are there?",
+    "pandas_code": "df['Claim_Number'].str.contains(r'CBGDC2', na=False) & 'Claim_Type'] == 'Reimbursement'].shape[0]"
+  },
+  {
+    "Query": "Display claims where Deduction Amount at most 7.22 and Claim Id between 265220 and 272091 How many are there?",
+    "pandas_code": "df['Deduction_Amount'] <= 7.22 & ('Claim_Id'] >= 265220.71) & ('Claim_Id'] <= 272091.55)].shape[0]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Type contains 'Dental' and Claim Type contains 'CHS'.",
+    "pandas_code": "df['Loss_Type'].str.contains(r'Dental', na=False) & 'Claim_Type'].str.contains(r'CHS', na=False)]"
+  },
+  {
+    "Query": "Tell me claims where Scheme is 'SME 3-SP-CO-Panel Only' and Policy Number contains 'BGDP22'.",
+    "pandas_code": "df['Scheme'] == 'SME 3-SP-CO-Panel Only' & 'Policy_Number'].str.contains(r'BGDP22', na=False)]"
+  },
+  {
+    "Query": "List claims where Product is 'Group OP General Practitioner'.",
+    "pandas_code": "df[df['Product'] == 'Group OP General Practitioner']"
+  },
+  {
+    "Query": "Show claims where Status Code is 'DU0' and Claim Amount between 709 and 835.",
+    "pandas_code": "df['Status_Code'] == 'DU0' & ('Claim_Amount'] >= 709.18) & ('Claim_Amount'] <= 835.91)]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 31 and 1540.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 31.63) & (df['Claim_Amount'] <= 1540.11)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Type is 'Specialist Treatment' and Product is 'Group Dental Benefit'.",
+    "pandas_code": "df['Loss_Type'] == 'Specialist Treatment' & 'Product'] == 'Group Dental Benefit']"
+  },
+  {
+    "Query": "Could you get Correlation between Claim_Amount and Approved_Amount.",
+    "pandas_code": "df[['Claim_Amount','Approved_Amount']].corr()"
+  },
+  {
+    "Query": "Is there get claims where Loss Date before 2022-12-10.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-10')]"
+  },
+  {
+    "Query": "Show claims where Provider Code contains 'PC1037' and Claim Id between 266768 and 276604 How many are there?",
+    "pandas_code": "df['Provider_Code'].str.contains(r'PC1037', na=False) & ('Claim_Id'] >= 266768.16) & ('Claim_Id'] <= 276604.73)].shape[0]"
+  },
+  {
+    "Query": "Count how many claims where Product is 'General Practitioner'",
+    "pandas_code": "df[df['Product'] == 'General Practitioner'].shape[0]"
+  },
+  {
+    "Query": "Find claims where Deduction Amount at least 1.12 and Status is 'Approved'.",
+    "pandas_code": "df['Deduction_Amount'] >= 1.12 & 'Status'] == 'Approved']"
+  },
+  {
+    "Query": "Show claims where Claim Id equal to 264431.",
+    "pandas_code": "df[df['Claim_Id'] == 264431]"
+  },
+  {
+    "Query": "Retrieve claims where Deduction Amount between 3 and 7 and Status Code contains 'AP2' How many are there?",
+    "pandas_code": "df[('Deduction_Amount'] >= 3.48) & ('Deduction_Amount'] <= 7.64) & 'Status_Code'].str.contains(r'AP2', na=False)].shape[0]"
+  },
+  {
+    "Query": "Get How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Tell me claims where Deduction Amount between 4 and 8.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 4.74) & (df['Deduction_Amount'] <= 8.48)]"
+  },
+  {
+    "Query": "Fetch claims where Patient Name contains 'Mr Moh' and Policy Number is 'BGDP221000592-00-000'.",
+    "pandas_code": "df['Patient_Name'].str.contains(r'Mr Moh', na=False) & 'Policy_Number'] == 'BGDP221000592-00-000']"
+  },
+  {
+    "Query": "Can you retrieve claims where Claim Id at least 276940.",
+    "pandas_code": "df[df['Claim_Id'] >= 276940]"
+  },
+  {
+    "Query": "Give me How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Tell me claims where Approved Amount less than 1026.",
+    "pandas_code": "df[df['Approved_Amount'] < 1026]"
+  },
+  {
+    "Query": "Fetch Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Get claims where Claim Id less than 262123.11.",
+    "pandas_code": "df[df['Claim_Id'] < 262123.11]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount at least 1018.",
+    "pandas_code": "df[df['Claim_Amount'] >= 1018]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount less than 1344.75.",
+    "pandas_code": "df[df['Claim_Amount'] < 1344.75]"
+  },
+  {
+    "Query": "List claims where Claim Id at least 273191.",
+    "pandas_code": "df[df['Claim_Id'] >= 273191]"
+  },
+  {
+    "Query": "Show claims where Claim Type is 'Reimbursement' and Claim Id between 270608 and 275056.",
+    "pandas_code": "df['Claim_Type'] == 'Reimbursement' & ('Claim_Id'] >= 270608.71) & ('Claim_Id'] <= 275056.2)]"
+  },
+  {
+    "Query": "Count how many claims where Claim Amount equal to 369",
+    "pandas_code": "df[df['Claim_Amount'] == 369].shape[0]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-06-23 and Provider Name is 'Parkway East Hospital'.",
+    "pandas_code": "df['Loss_Date'] == pd.to_datetime('2023-06-23') & 'Provider_Name'] == 'Parkway East Hospital']"
+  },
+  {
+    "Query": "Provide claims where Loss Date after 2023-07-29.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-29')]"
+  },
+  {
+    "Query": "Fetch claims where Patient Name contains 'Ms Bee' and Deduction Amount at least 0.82.",
+    "pandas_code": "df['Patient_Name'].str.contains(r'Ms Bee', na=False) & 'Deduction_Amount'] >= 0.82]"
+  },
+  {
+    "Query": "List claims where Policy Number contains 'BGDC22' and Provider Code contains 'OTH000'.",
+    "pandas_code": "df['Policy_Number'].str.contains(r'BGDC22', na=False) & 'Provider_Code'].str.contains(r'OTH000', na=False)]"
+  },
+  {
+    "Query": "How many claims where Approved Amount less than 862",
+    "pandas_code": "df[df['Approved_Amount'] < 862].shape[0]"
+  },
+  {
+    "Query": "Tell me fetch claims where Loss Type is 'Hospitalisation'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Hospitalisation']"
+  },
+  {
+    "Query": "Tell me claims where Provider Name contains 'NTUC D'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'NTUC D', na=False)]"
+  },
+  {
+    "Query": "Tell me claims where Claim Type is 'Reimbursement'.",
+    "pandas_code": "df[df['Claim_Type'] == 'Reimbursement']"
+  },
+  {
+    "Query": "Fetch claims where Status is 'Approved' and Status Code contains 'AP2'.",
+    "pandas_code": "df['Status'] == 'Approved' & 'Status_Code'].str.contains(r'AP2', na=False)]"
+  },
+  {
+    "Query": "Show List all unique Provider_Name values.",
+    "pandas_code": "df['Provider_Name'].unique()"
+  },
+  {
+    "Query": "Tell me show claims where Approved Amount at most 1323.",
+    "pandas_code": "df[df['Approved_Amount'] <= 1323]"
+  },
+  {
+    "Query": "Number of claims where Loss Type contains 'GP Tre'",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'GP Tre', na=False)].shape[0]"
+  },
+  {
+    "Query": "Give me claims where Status contains 'Paymen' and Status Code is 'PY3'.",
+    "pandas_code": "df['Status'].str.contains(r'Paymen', na=False) & 'Status_Code'] == 'PY3']"
+  },
+  {
+    "Query": "Number of claims where Claim Created Date after 2023-02-13",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-13')].shape[0]"
+  },
+  {
+    "Query": "Tell me claims where Patient Name is 'Ms Poh Fun Christine Chan' and Claim Amount between 674 and 1255.",
+    "pandas_code": "df['Patient_Name'] == 'Ms Poh Fun Christine Chan' & ('Claim_Amount'] >= 674.7) & ('Claim_Amount'] <= 1255.44)]"
+  },
+  {
+    "Query": "Display claims where Product contains 'Group ' and Claim Amount between 103 and 1548.",
+    "pandas_code": "df['Product'].str.contains(r'Group ', na=False) & ('Claim_Amount'] >= 103.9) & ('Claim_Amount'] <= 1548.3)]"
+  },
+  {
+    "Query": "Display claims where Deduction Amount between 1 and 7 and Status Code contains 'DU0'.",
+    "pandas_code": "df[('Deduction_Amount'] >= 1.39) & ('Deduction_Amount'] <= 7.29) & 'Status_Code'].str.contains(r'DU0', na=False)]"
+  },
+  {
+    "Query": "Tell me claims where Patient Name is 'Mr More Shlok Vinayak'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr More Shlok Vinayak']"
+  },
+  {
+    "Query": "Find claims where Product contains 'Hospit'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Hospit', na=False)]"
+  },
+  {
+    "Query": "Show Top 5 claims by Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 484 and 1442 and Scheme contains 'SME 1-'.",
+    "pandas_code": "df[('Claim_Amount'] >= 484.52) & ('Claim_Amount'] <= 1442.73) & 'Scheme'].str.contains(r'SME 1-', na=False)]"
+  },
+  {
+    "Query": "Give me claims where Claim Id greater than 276644 and Status Code is 'AP2' How many are there?",
+    "pandas_code": "df['Claim_Id'] > 276644 & 'Status_Code'] == 'AP2'].shape[0]"
+  },
+  {
+    "Query": "Display claims where Deduction Amount between 2 and 6 and Claim Number is 'CBGDC23061446-00'.",
+    "pandas_code": "df[('Deduction_Amount'] >= 2.82) & ('Deduction_Amount'] <= 6.52) & 'Claim_Number'] == 'CBGDC23061446-00']"
+  },
+  {
+    "Query": "Please get claims where Product is 'Dental' and Claim Amount between 688 and 1469.",
+    "pandas_code": "df['Product'] == 'Dental' & ('Claim_Amount'] >= 688.64) & ('Claim_Amount'] <= 1469.0)]"
+  },
+  {
+    "Query": "Count how many claims where Claim Created Date before 2022-12-22",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-12-22')].shape[0]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date before 2023-06-10.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-06-10')]"
+  },
+  {
+    "Query": "List claims where Claim Type is 'Reimbursement' and Claim Created Date on 2023-05-22 How many are there?",
+    "pandas_code": "df['Claim_Type'] == 'Reimbursement' & 'Claim_Created_Date'] == pd.to_datetime('2023-05-22')].shape[0]"
+  },
+  {
+    "Query": "List claims where Claim Amount equal to 711.",
+    "pandas_code": "df[df['Claim_Amount'] == 711]"
+  },
+  {
+    "Query": "Please retrieve Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Provide claims where Deduction Amount equal to 8.12.",
+    "pandas_code": "df[df['Deduction_Amount'] == 8.12]"
+  },
+  {
+    "Query": "Get claims where Approved Amount at least 191.",
+    "pandas_code": "df[df['Approved_Amount'] >= 191]"
+  },
+  {
+    "Query": "How many claims where Claim Created Date after 2022-12-31",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-31')].shape[0]"
+  },
+  {
+    "Query": "Show claims where Provider Code is 'PC103779' and Loss Date between 2022-11-13 and 2023-05-07 How many are there?",
+    "pandas_code": "df['Provider_Code'] == 'PC103779' & ('Loss_Date'] >= pd.to_datetime('2022-11-13')) & ('Loss_Date'] <= pd.to_datetime('2023-05-07'))].shape[0]"
+  },
+  {
+    "Query": "Please display claims where Deduction Amount less than 6.66.",
+    "pandas_code": "df[df['Deduction_Amount'] < 6.66]"
+  },
+  {
+    "Query": "Tell me claims where Claim Type is 'CHS' and Claim Amount between 167 and 1537.",
+    "pandas_code": "df['Claim_Type'] == 'CHS' & ('Claim_Amount'] >= 167.45) & ('Claim_Amount'] <= 1537.62)]"
+  },
+  {
+    "Query": "Find Which columns have missing values?",
+    "pandas_code": "df.isnull().sum()"
+  },
+  {
+    "Query": "Show claims where Claim Amount at least 871 and Patient Name is 'Ms Jenna Lee'.",
+    "pandas_code": "df['Claim_Amount'] >= 871 & 'Patient_Name'] == 'Ms Jenna Lee']"
+  },
+  {
+    "Query": "Tell me claims where Loss Type is 'GP Treatment' and Patient Name is 'Lester Melvin Elago Edano' How many are there?",
+    "pandas_code": "df['Loss_Type'] == 'GP Treatment' & 'Patient_Name'] == 'Lester Melvin Elago Edano'].shape[0]"
+  },
+  {
+    "Query": "Display claims where Product is 'Group OP General Practitioner'.",
+    "pandas_code": "df[df['Product'] == 'Group OP General Practitioner']"
+  },
+  {
+    "Query": "List Show total number of claims in the dataset.",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Get claims where Deduction Amount less than 8.18.",
+    "pandas_code": "df[df['Deduction_Amount'] < 8.18]"
+  },
+  {
+    "Query": "Provide claims where Provider Name contains 'Kang A'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Kang A', na=False)]"
+  },
+  {
+    "Query": "Give me claims where Claim Id less than 279374.",
+    "pandas_code": "df[df['Claim_Id'] < 279374]"
+  },
+  {
+    "Query": "Show Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Show claims where Approved Amount at least 482.",
+    "pandas_code": "df[df['Approved_Amount'] >= 482]"
+  },
+  {
+    "Query": "Fetch claims where Claim Type is 'CHS' and Loss Type is 'Hospitalisation'.",
+    "pandas_code": "df['Claim_Type'] == 'CHS' & 'Loss_Type'] == 'Hospitalisation']"
+  },
+  {
+    "Query": "Give me claims where Loss Date on 2022-12-15.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-15')]"
+  },
+  {
+    "Query": "Count how many claims where Claim Type is 'Cashless'",
+    "pandas_code": "df[df['Claim_Type'] == 'Cashless'].shape[0]"
+  },
+  {
+    "Query": "Tell me claims where Provider Code is 'PC100731' and Claim Created Date between 2023-02-28 and 2023-03-02.",
+    "pandas_code": "df['Provider_Code'] == 'PC100731' & ('Claim_Created_Date'] >= pd.to_datetime('2023-02-28')) & ('Claim_Created_Date'] <= pd.to_datetime('2023-03-02'))]"
+  },
+  {
+    "Query": "Show claims where Status Code is 'CV0' and Patient Name is 'Mr Yu Chiu Sun'.",
+    "pandas_code": "df['Status_Code'] == 'CV0' & 'Patient_Name'] == 'Mr Yu Chiu Sun']"
+  },
+  {
+    "Query": "Show claims where Provider Code is 'PC101195' and Loss Type is 'GP Treatment' How many are there?",
+    "pandas_code": "df['Provider_Code'] == 'PC101195' & 'Loss_Type'] == 'GP Treatment'].shape[0]"
+  },
+  {
+    "Query": "Display claims where Approved Amount less than 470.",
+    "pandas_code": "df[df['Approved_Amount'] < 470]"
+  },
+  {
+    "Query": "Show claims where Deduction Amount at least 4.17 and Scheme is 'SME 1-SP-1500' How many are there?",
+    "pandas_code": "df['Deduction_Amount'] >= 4.17 & 'Scheme'] == 'SME 1-SP-1500'].shape[0]"
+  },
+  {
+    "Query": "Find claims where Claim Amount at least 615.08.",
+    "pandas_code": "df[df['Claim_Amount'] >= 615.08]"
+  },
+  {
+    "Query": "Show claims where Claim Number contains 'CBGDP2' and Claim Type is 'Cashless'.",
+    "pandas_code": "df['Claim_Number'].str.contains(r'CBGDP2', na=False) & 'Claim_Type'] == 'Cashless']"
+  },
+  {
+    "Query": "Give me claims where Loss Date after 2023-07-25.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-25')]"
+  },
+  {
+    "Query": "How many claims where Loss Date on 2023-05-12",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-12')].shape[0]"
+  },
+  {
+    "Query": "Give me claims where Claim Amount between 156 and 1249.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 156.31) & (df['Claim_Amount'] <= 1249.21)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 140 and 1094 and Deduction Amount at most 1.82 How many are there?",
+    "pandas_code": "df[('Approved_Amount'] >= 140.58) & ('Approved_Amount'] <= 1094.13) & 'Deduction_Amount'] <= 1.82].shape[0]"
+  },
+  {
+    "Query": "Number of claims where Claim Created Date on 2023-07-21",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-07-21')].shape[0]"
+  },
+  {
+    "Query": "Get Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2023-05-18 and Claim Amount between 348 and 832.",
+    "pandas_code": "df['Loss_Date'] == pd.to_datetime('2023-05-18') & ('Claim_Amount'] >= 348.56) & ('Claim_Amount'] <= 832.79)]"
+  },
+  {
+    "Query": "Give me claims where Approved Amount between 615 and 1533.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 615.32) & (df['Approved_Amount'] <= 1533.05)]"
+  },
+  {
+    "Query": "Please get claims where Claim Created Date on 2023-01-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-19')]"
+  },
+  {
+    "Query": "Give me claims where Provider Code is 'PC100001' and Claim Type is 'Reimbursement'.",
+    "pandas_code": "df['Provider_Code'] == 'PC100001' & 'Claim_Type'] == 'Reimbursement']"
+  },
+  {
+    "Query": "Show claims where Claim Number is 'CBGDC23060136-00' and Patient Name is 'Mr Boon Ann Tan'.",
+    "pandas_code": "df['Claim_Number'] == 'CBGDC23060136-00' & 'Patient_Name'] == 'Mr Boon Ann Tan']"
+  },
+  {
+    "Query": "Display Calculate Claim_Amount minus Approved_Amount for each claim.",
+    "pandas_code": "df['Claim_Amount'] - df['Approved_Amount']"
+  },
+  {
+    "Query": "Do we have find claims where Approved Amount between 21 and 879 and Provider Name contains 'Nuffie'.",
+    "pandas_code": "df[('Approved_Amount'] >= 21.2) & ('Approved_Amount'] <= 879.66) & 'Provider_Name'].str.contains(r'Nuffie', na=False)]"
+  },
+  {
+    "Query": "I want to know show claims where Claim Amount between 559 and 1179.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 559.28) & (df['Claim_Amount'] <= 1179.21)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Number is 'CBGDP23044466-00' and Scheme contains 'SME 1-' How many are there?",
+    "pandas_code": "df['Claim_Number'] == 'CBGDP23044466-00' & 'Scheme'].str.contains(r'SME 1-', na=False)].shape[0]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date on 2022-11-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-16')]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-05-24.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-24')]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-01-26 and Claim Number contains 'CBGDC2'.",
+    "pandas_code": "df['Loss_Date'] == pd.to_datetime('2023-01-26') & 'Claim_Number'].str.contains(r'CBGDC2', na=False)]"
+  },
+  {
+    "Query": "Find claims where Scheme contains 'SME 3-' and Approved Amount at most 803.",
+    "pandas_code": "df['Scheme'].str.contains(r'SME 3-', na=False) & 'Approved_Amount'] <= 803]"
+  },
+  {
+    "Query": "Give me claims where Loss Type is 'Specialist Treatment' and Loss Date before 2022-12-25 How many are there?",
+    "pandas_code": "df['Loss_Type'] == 'Specialist Treatment' & 'Loss_Date'] < pd.to_datetime('2022-12-25')].shape[0]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date on 2023-06-05.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-06-05')]"
+  },
+  {
+    "Query": "Show claims where Claim Id less than 265207.",
+    "pandas_code": "df[df['Claim_Id'] < 265207]"
+  },
+  {
+    "Query": "Tell me claims where Loss Date before 2023-03-28.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-28')]"
+  },
+  {
+    "Query": "How many me claims where Claim Id between 266676 and 271767",
+    "pandas_code": "df[(df['Claim_Id'] >= 266676.04) & (df['Claim_Id'] <= 271767.39)].shape[0]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2022-11-13 and 2023-05-23.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-13')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-23'))]"
+  },
+  {
+    "Query": "Tell me get claims where Status Code is 'AP2' and Claim Id at least 276456.43 How many are there?",
+    "pandas_code": "df['Status_Code'] == 'AP2' & 'Claim_Id'] >= 276456.43].shape[0]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount equal to 911.",
+    "pandas_code": "df[df['Approved_Amount'] == 911]"
+  },
+  {
+    "Query": "Provide claims where Policy Number is 'BGDC211000099-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000099-01-000']"
+  },
+  {
+    "Query": "Count how many me claims where Claim Amount at least 816",
+    "pandas_code": "df[df['Claim_Amount'] >= 816].shape[0]"
+  },
+  {
+    "Query": "Count how many claims where Deduction Amount between 2 and 9",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 2.84) & (df['Deduction_Amount'] <= 9.28)].shape[0]"
+  },
+  {
+    "Query": "List claims where Status Code is 'CV0' and Claim Id less than 272456.",
+    "pandas_code": "df['Status_Code'] == 'CV0' & 'Claim_Id'] < 272456]"
+  },
+  {
+    "Query": "Get claims where Claim Number contains 'CBGDC2' and Deduction Amount at least 1.64.",
+    "pandas_code": "df['Claim_Number'].str.contains(r'CBGDC2', na=False) & 'Deduction_Amount'] >= 1.64]"
+  },
+  {
+    "Query": "Display claims where Provider Name is 'SINGAPORE GENERAL HOSPITAL PTE LTD'.",
+    "pandas_code": "df[df['Provider_Name'] == 'SINGAPORE GENERAL HOSPITAL PTE LTD']"
+  },
+  {
+    "Query": "Can you give me claims where Status Code is 'DU0' and Loss Date between 2023-01-09 and 2023-05-23 How many are there?",
+    "pandas_code": "df['Status_Code'] == 'DU0' & ('Loss_Date'] >= pd.to_datetime('2023-01-09')) & ('Loss_Date'] <= pd.to_datetime('2023-05-23'))].shape[0]"
+  },
+  {
+    "Query": "Give me claims where Claim Created Date between 2023-04-21 and 2023-07-12 and Patient Name is 'Ms Fong Peng Annie Chew'.",
+    "pandas_code": "df[('Claim_Created_Date'] >= pd.to_datetime('2023-04-21')) & ('Claim_Created_Date'] <= pd.to_datetime('2023-07-12')) & 'Patient_Name'] == 'Ms Fong Peng Annie Chew']"
+  },
+  {
+    "Query": "Provide claims where Deduction Amount at least 8.62.",
+    "pandas_code": "df[df['Deduction_Amount'] >= 8.62]"
+  },
+  {
+    "Query": "Provide claims where Patient Name is 'Mr Tabea Hildegard Wanninger' and Status Code contains 'AP2'.",
+    "pandas_code": "df['Patient_Name'] == 'Mr Tabea Hildegard Wanninger' & 'Status_Code'].str.contains(r'AP2', na=False)]"
+  },
+  {
+    "Query": "Provide Median Claim_Amount.",
+    "pandas_code": "df['Claim_Amount'].median()"
+  },
+  {
+    "Query": "Give me claims where Claim Created Date after 2023-03-26.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-26')]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2023-02-27 and 2023-05-31.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-27')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-31'))]"
+  },
+  {
+    "Query": "Number of claims where Approved Amount at most 261.97",
+    "pandas_code": "df[df['Approved_Amount'] <= 261.97].shape[0]"
+  },
+  {
+    "Query": "Tell me claims where Status Code is 'DU0' and Claim Created Date between 2022-11-17 and 2022-12-25.",
+    "pandas_code": "df['Status_Code'] == 'DU0' & ('Claim_Created_Date'] >= pd.to_datetime('2022-11-17')) & ('Claim_Created_Date'] <= pd.to_datetime('2022-12-25'))]"
+  },
+  {
+    "Query": "Give me claims where Loss Type is 'Hospitalisation'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Hospitalisation']"
+  },
+  {
+    "Query": "Find How many records are there?",
+    "pandas_code": "len(df)"
+  },
+  {
+    "Query": "Display claims where Status is 'Paid' and Claim Number contains 'CBGDC2'.",
+    "pandas_code": "df['Status'] == 'Paid' & 'Claim_Number'].str.contains(r'CBGDC2', na=False)]"
+  },
+  {
+    "Query": "Give me claims where Product contains 'Group '.",
+    "pandas_code": "df[df['Product'].str.contains(r'Group ', na=False)]"
+  },
+  {
+    "Query": "Show claims where Status is 'Paid' and Approved Amount less than 897.",
+    "pandas_code": "df['Status'] == 'Paid' & 'Approved_Amount'] < 897]"
+  },
+  {
+    "Query": "I want to know provide claims where Scheme contains 'SME 2-' and Approved Amount between 408 and 1411 How many are there?",
+    "pandas_code": "df['Scheme'].str.contains(r'SME 2-', na=False) & ('Approved_Amount'] >= 408.51) & ('Approved_Amount'] <= 1411.45)].shape[0]"
+  },
+  {
+    "Query": "Tell me show claims where Loss Type contains 'GP Tre' and Provider Code is 'PC103209'.",
+    "pandas_code": "df['Loss_Type'].str.contains(r'GP Tre', na=False) & 'Provider_Code'] == 'PC103209']"
+  },
+  {
+    "Query": "Provide claims where Policy Number contains 'BGDC21' and Status Code is 'RJ2'.",
+    "pandas_code": "df['Policy_Number'].str.contains(r'BGDC21', na=False) & 'Status_Code'] == 'RJ2']"
+  },
+  {
+    "Query": "Provide claims where Status Code contains 'AP2' and Claim Number is 'CBGDC23061879-00'.",
+    "pandas_code": "df['Status_Code'].str.contains(r'AP2', na=False) & 'Claim_Number'] == 'CBGDC23061879-00']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date on 2023-03-31.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-31')]"
+  },
+  {
+    "Query": "Tell me claims where Deduction Amount between 2 and 8 and Claim Id equal to 266462.",
+    "pandas_code": "df[('Deduction_Amount'] >= 2.97) & ('Deduction_Amount'] <= 8.0) & 'Claim_Id'] == 266462]"
+  },
+  {
+    "Query": "Provide claims where Provider Name is 'Kang An TCM Pte Ltd' and Loss Type is 'GP Treatment'.",
+    "pandas_code": "df['Provider_Name'] == 'Kang An TCM Pte Ltd' & 'Loss_Type'] == 'GP Treatment']"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-02-05 and Scheme is 'Per Disability Template 2'.",
+    "pandas_code": "df['Loss_Date'] == pd.to_datetime('2023-02-05') & 'Scheme'] == 'Per Disability Template 2']"
+  },
+  {
+    "Query": "Tell me claims where Loss Date after 2023-05-07.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-07')]"
+  },
+  {
+    "Query": "Count how many claims where Claim Id less than 265360.96",
+    "pandas_code": "df[df['Claim_Id'] < 265360.96].shape[0]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-05-24 and 2023-07-27.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-05-24')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-27'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date before 2023-05-10 and Product contains 'Genera' How many are there?",
+    "pandas_code": "df['Claim_Created_Date'] < pd.to_datetime('2023-05-10') & 'Product'].str.contains(r'Genera', na=False)].shape[0]"
+  },
+  {
+    "Query": "Show claims where Scheme is 'New Template 3 ' and Loss Date on 2023-05-31.",
+    "pandas_code": "df['Scheme'] == 'New Template 3 ' & 'Loss_Date'] == pd.to_datetime('2023-05-31')]"
+  },
+  {
+    "Query": "List claims where Product is 'Hospital & Surgical' and Claim Type is 'CHS' How many are there?",
+    "pandas_code": "df['Product'] == 'Hospital & Surgical' & 'Claim_Type'] == 'CHS'].shape[0]"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name is 'Ms Siew Li Lai' and Scheme contains 'Templa'.",
+    "pandas_code": "df['Patient_Name'] == 'Ms Siew Li Lai' & 'Scheme'].str.contains(r'Templa', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount equal to 1386.",
+    "pandas_code": "df[df['Approved_Amount'] == 1386]"
+  },
+  {
+    "Query": "Show claims where Scheme is 'Template 2' and Approved Amount greater than 416.",
+    "pandas_code": "df['Scheme'] == 'Template 2' & 'Approved_Amount'] > 416]"
+  },
+  {
+    "Query": "Provide claims where Claim Id less than 278352.",
+    "pandas_code": "df[df['Claim_Id'] < 278352]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2022-12-17 and 2023-07-16.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-17')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-16'))]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2023-05-18.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-18')]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-07-25 and Deduction Amount between 1 and 6.",
+    "pandas_code": "df['Claim_Created_Date'] < pd.to_datetime('2023-07-25') & ('Deduction_Amount'] >= 1.02) & ('Deduction_Amount'] <= 6.06)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-01-11 and 2023-03-12.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-11')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-12'))]"
+  },
+  {
+    "Query": "List claims where Approved Amount equal to 914.",
+    "pandas_code": "df[df['Approved_Amount'] == 914]"
+  },
+  {
+    "Query": "Display claims where Claim Amount less than 1348 and Claim Id at most 263365.",
+    "pandas_code": "df['Claim_Amount'] < 1348 & 'Claim_Id'] <= 263365]"
+  },
+  {
+    "Query": "Find claims where Approved Amount greater than 1363.68.",
+    "pandas_code": "df[df['Approved_Amount'] > 1363.68]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2023-07-29.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-29')]"
+  },
+  {
+    "Query": "Fetch claims where Provider Code is 'PC101195' and Status is 'Approved'.",
+    "pandas_code": "df['Provider_Code'] == 'PC101195' & 'Status'] == 'Approved']"
+  },
+  {
+    "Query": "Tell me claims where Claim Id greater than 275371 and Policy Number is 'BGDC231000020-00-000'.",
+    "pandas_code": "df['Claim_Id'] > 275371 & 'Policy_Number'] == 'BGDC231000020-00-000']"
+  },
+  {
+    "Query": "Give me claims where Loss Type is 'Hospitalisation' and Product contains 'Group '.",
+    "pandas_code": "df['Loss_Type'] == 'Hospitalisation' & 'Product'].str.contains(r'Group ', na=False)]"
+  },
+  {
+    "Query": "Count how many claims where Claim Number is 'CBGDP23037682-00'",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23037682-00'].shape[0]"
+  },
+  {
+    "Query": "Show claims where Claim Type contains 'CHS' and Status is 'Rejected'.",
+    "pandas_code": "df['Claim_Type'].str.contains(r'CHS', na=False) & 'Status'] == 'Rejected']"
+  },
+  {
+    "Query": "Can you display claims where Loss Date on 2023-05-25 and Claim Id greater than 262478 How many are there?",
+    "pandas_code": "df['Loss_Date'] == pd.to_datetime('2023-05-25') & 'Claim_Id'] > 262478].shape[0]"
+  },
+  {
+    "Query": "Get claims where Claim Amount less than 117.",
+    "pandas_code": "df[df['Claim_Amount'] < 117]"
+  },
+  {
+    "Query": "Provide Std dev of Approved_Amount.",
+    "pandas_code": "df['Approved_Amount'].std()"
+  },
+  {
+    "Query": "Give me claims where Provider Name contains 'Casa D'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Casa D', na=False)]"
+  },
+  {
+    "Query": "Tell me claims where Approved Amount between 380 and 940 and Claim Type contains 'CHS' How many are there?",
+    "pandas_code": "df[('Approved_Amount'] >= 380.01) & ('Approved_Amount'] <= 940.13) & 'Claim_Type'].str.contains(r'CHS', na=False)].shape[0]"
+  },
+  {
+    "Query": "Number of me claims where Approved Amount between 429 and 898",
+    "pandas_code": "df[(df['Approved_Amount'] >= 429.47) & (df['Approved_Amount'] <= 898.63)].shape[0]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id less than 264924.",
+    "pandas_code": "df[df['Claim_Id'] < 264924]"
+  },
+  {
+    "Query": "Number of claims where Claim Amount less than 1565.51",
+    "pandas_code": "df[df['Claim_Amount'] < 1565.51].shape[0]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2022-11-22 and Claim Number contains 'CBGDC2'.",
+    "pandas_code": "df['Loss_Date'] < pd.to_datetime('2022-11-22') & 'Claim_Number'].str.contains(r'CBGDC2', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 299 and 1517 and Approved Amount equal to 74 How many are there?",
+    "pandas_code": "df[('Claim_Amount'] >= 299.0) & ('Claim_Amount'] <= 1517.1) & 'Approved_Amount'] == 74].shape[0]"
+  },
+  {
+    "Query": "List claims where Provider Code contains 'PC1000' and Deduction Amount equal to 7.16.",
+    "pandas_code": "df['Provider_Code'].str.contains(r'PC1000', na=False) & 'Deduction_Amount'] == 7.16]"
+  },
+  {
+    "Query": "List claims where Loss Date after 2023-05-07.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-07')]"
+  },
+  {
+    "Query": "Can you provide Average Claim_Amount by Product.",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean()"
+  },
+  {
+    "Query": "Fetch claims where Patient Name contains 'Mr Zhu' and Product contains 'Group ' How many are there?",
+    "pandas_code": "df['Patient_Name'].str.contains(r'Mr Zhu', na=False) & 'Product'].str.contains(r'Group ', na=False)].shape[0]"
+  },
+  {
+    "Query": "Please display claims where Approved Amount between 295 and 1007.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 295.29) & (df['Approved_Amount'] <= 1007.68)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Number contains 'CBGDP2' and Status Code is 'DU0'.",
+    "pandas_code": "df['Claim_Number'].str.contains(r'CBGDP2', na=False) & 'Status_Code'] == 'DU0']"
+  },
+  {
+    "Query": "Can you fetch claims where Approved Amount at most 905.",
+    "pandas_code": "df[df['Approved_Amount'] <= 905]"
+  },
+  {
+    "Query": "Provide claims where Loss Date between 2023-07-09 and 2023-07-15.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-07-09')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-15'))]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-07-30.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-30')]"
+  },
+  {
+    "Query": "Give me claims where Loss Type contains 'GP Tre' and Provider Code is 'PC100731'.",
+    "pandas_code": "df['Loss_Type'].str.contains(r'GP Tre', na=False) & 'Provider_Code'] == 'PC100731']"
+  },
+  {
+    "Query": "Number of claims where Loss Date after 2023-06-03",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-06-03')].shape[0]"
+  },
+  {
+    "Query": "Can you give me claims where Status Code is 'PY1' and Loss Type is 'Specialist Treatment'.",
+    "pandas_code": "df['Status_Code'] == 'PY1' & 'Loss_Type'] == 'Specialist Treatment']"
+  },
+  {
+    "Query": "Get claims where Scheme is 'New Template 1'.",
+    "pandas_code": "df[df['Scheme'] == 'New Template 1']"
+  },
+  {
+    "Query": "Show claims where Deduction Amount between 2 and 7.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 2.95) & (df['Deduction_Amount'] <= 7.02)]"
+  },
+  {
+    "Query": "Get claims where Approved Amount less than 1228 and Product contains 'Genera'.",
+    "pandas_code": "df['Approved_Amount'] < 1228 & 'Product'].str.contains(r'Genera', na=False)]"
+  },
+  {
+    "Query": "Find claims where Provider Name is 'Others' and Provider Code is 'PC100466'.",
+    "pandas_code": "df['Provider_Name'] == 'Others' & 'Provider_Code'] == 'PC100466']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Type is 'Reimbursement' and Approved Amount less than 439.",
+    "pandas_code": "df['Claim_Type'] == 'Reimbursement' & 'Approved_Amount'] < 439]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id equal to 278444.59 and Status Code contains 'RJ2'.",
+    "pandas_code": "df['Claim_Id'] == 278444.59 & 'Status_Code'].str.contains(r'RJ2', na=False)]"
+  },
+  {
+    "Query": "Find claims where Claim Number is 'CBGDC23060281-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23060281-00']"
+  },
+  {
+    "Query": "Tell me claims where Status Code is 'RJ2' and Product is 'Group Hospital and Surgical' How many are there?",
+    "pandas_code": "df['Status_Code'] == 'RJ2' & 'Product'] == 'Group Hospital and Surgical'].shape[0]"
+  },
+  {
+    "Query": "Display claims where Claim Amount equal to 1586.",
+    "pandas_code": "df[df['Claim_Amount'] == 1586]"
+  },
+  {
+    "Query": "Give me claims where Approved Amount less than 774.",
+    "pandas_code": "df[df['Approved_Amount'] < 774]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id equal to 272193.",
+    "pandas_code": "df[df['Claim_Id'] == 272193]"
+  },
+  {
+    "Query": "Number of claims where Claim Id at most 276025.62",
+    "pandas_code": "df[df['Claim_Id'] <= 276025.62].shape[0]"
+  },
+  {
+    "Query": "Is there retrieve claims where Claim Id between 261782 and 277763 and Deduction Amount at most 5.92.",
+    "pandas_code": "df[('Claim_Id'] >= 261782.8) & ('Claim_Id'] <= 277763.04) & 'Deduction_Amount'] <= 5.92]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount at least 1267.",
+    "pandas_code": "df[df['Approved_Amount'] >= 1267]"
+  },
+  {
+    "Query": "Count how many claims where Scheme contains 'New Te'",
+    "pandas_code": "df[df['Scheme'].str.contains(r'New Te', na=False)].shape[0]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 546 and 1392.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 546.13) & (df['Approved_Amount'] <= 1392.58)]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 780 and 1237.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 780.21) & (df['Claim_Amount'] <= 1237.56)]"
+  },
+  {
+    "Query": "Get claims where Policy Number contains 'BGDP22' and Provider Code is 'PC102812'.",
+    "pandas_code": "df['Policy_Number'].str.contains(r'BGDP22', na=False) & 'Provider_Code'] == 'PC102812']"
+  },
+  {
+    "Query": "Show claims where Deduction Amount greater than 6.88 and Loss Date after 2023-02-10.",
+    "pandas_code": "df['Deduction_Amount'] > 6.88 & 'Loss_Date'] > pd.to_datetime('2023-02-10')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 263347 and 271689.",
+    "pandas_code": "df[(df['Claim_Id'] >= 263347.74) & (df['Claim_Id'] <= 271689.42)]"
+  },
+  {
+    "Query": "Fetch claims where Status Code contains 'PY3' and Claim Amount between 587 and 1592.",
+    "pandas_code": "df['Status_Code'].str.contains(r'PY3', na=False) & ('Claim_Amount'] >= 587.09) & ('Claim_Amount'] <= 1592.2)]"
+  },
+  {
+    "Query": "Tell me find claims where Claim Amount at least 874 and Product is 'Dental'.",
+    "pandas_code": "df['Claim_Amount'] >= 874 & 'Product'] == 'Dental']"
+  },
+  {
+    "Query": "Retrieve claims where Product contains 'Group ' and Status contains 'Void'.",
+    "pandas_code": "df['Product'].str.contains(r'Group ', na=False) & 'Status'].str.contains(r'Void', na=False)]"
+  },
+  {
+    "Query": "Tell me claims where Status contains 'Duplic' and Provider Name contains 'SINGHE'.",
+    "pandas_code": "df['Status'].str.contains(r'Duplic', na=False) & 'Provider_Name'].str.contains(r'SINGHE', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date between 2022-11-14 and 2023-03-24.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-14')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-24'))]"
+  },
+  {
+    "Query": "Please provide claims where Claim Created Date on 2023-04-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-04-13')]"
+  },
+  {
+    "Query": "Fetch claims where Provider Name is 'ADS CLINIC' and Status is 'Approved'.",
+    "pandas_code": "df['Provider_Name'] == 'ADS CLINIC' & 'Status'] == 'Approved']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-06-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-06-04')]"
+  },
+  {
+    "Query": "List claims where Patient Name is 'Sook Kwan Yee' and Approved Amount at most 1298 How many are there?",
+    "pandas_code": "df['Patient_Name'] == 'Sook Kwan Yee' & 'Approved_Amount'] <= 1298].shape[0]"
+  },
+  {
+    "Query": "Number of claims where Loss Date on 2023-06-01",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-01')].shape[0]"
+  },
+  {
+    "Query": "How many claims where Patient Name is 'Mr Yu Chiu Sun'",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Yu Chiu Sun'].shape[0]"
+  },
+  {
+    "Query": "Retrieve claims where Policy Number is 'BGDC211000296-01-000' and Loss Date on 2023-01-23 How many are there?",
+    "pandas_code": "df['Policy_Number'] == 'BGDC211000296-01-000' & 'Loss_Date'] == pd.to_datetime('2023-01-23')].shape[0]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-04-12.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-12')]"
+  },
+  {
+    "Query": "Get claims where Claim Id equal to 276674.28.",
+    "pandas_code": "df[df['Claim_Id'] == 276674.28]"
+  },
+  {
+    "Query": "Get claims where Patient Name is 'Mr Hwee Li Willy Chua' and Claim Number is 'CBGDC23062390-00'.",
+    "pandas_code": "df['Patient_Name'] == 'Mr Hwee Li Willy Chua' & 'Claim_Number'] == 'CBGDC23062390-00']"
+  },
+  {
+    "Query": "Can you show Show value counts for Status.",
+    "pandas_code": "df['Status'].value_counts()"
+  },
+  {
+    "Query": "Give me claims where Policy Number is 'BGDC211000219-02-000' and Claim Id at least 270900.",
+    "pandas_code": "df['Policy_Number'] == 'BGDC211000219-02-000' & 'Claim_Id'] >= 270900]"
+  },
+  {
+    "Query": "Count how many claims where Deduction Amount between 1 and 5",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 1.26) & (df['Deduction_Amount'] <= 5.28)].shape[0]"
+  },
+  {
+    "Query": "Provide claims where Deduction Amount equal to 6.95.",
+    "pandas_code": "df[df['Deduction_Amount'] == 6.95]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 173 and 1458.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 173.75) & (df['Claim_Amount'] <= 1458.36)]"
+  },
+  {
+    "Query": "Find claims where Status Code is 'RJ2'.",
+    "pandas_code": "df[df['Status_Code'] == 'RJ2']"
+  },
+  {
+    "Query": "Number of claims where Status is 'Void'",
+    "pandas_code": "df[df['Status'] == 'Void'].shape[0]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2022-12-02.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-12-02')]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount between 193 and 1278.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 193.17) & (df['Approved_Amount'] <= 1278.09)]"
+  },
+  {
+    "Query": "List claims where Status is 'Duplicate' and Approved Amount between 405 and 1089.",
+    "pandas_code": "df['Status'] == 'Duplicate' & ('Approved_Amount'] >= 405.87) & ('Approved_Amount'] <= 1089.07)]"
+  },
+  {
+    "Query": "Display claims where Claim Id between 264679 and 279309.",
+    "pandas_code": "df[(df['Claim_Id'] >= 264679.74) & (df['Claim_Id'] <= 279309.26)]"
+  },
+  {
+    "Query": "Give me claims where Product contains 'Group ' and Status is 'Void'.",
+    "pandas_code": "df['Product'].str.contains(r'Group ', na=False) & 'Status'] == 'Void']"
+  },
+  {
+    "Query": "Tell me claims where Product is 'Specialist'.",
+    "pandas_code": "df[df['Product'] == 'Specialist']"
+  },
+  {
+    "Query": "Provide claims where Scheme contains 'SME 1-' and Claim Created Date after 2023-05-21.",
+    "pandas_code": "df['Scheme'].str.contains(r'SME 1-', na=False) & 'Claim_Created_Date'] > pd.to_datetime('2023-05-21')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2022-11-09 and 2022-12-01 and Provider Code is 'PC104116'.",
+    "pandas_code": "df[('Claim_Created_Date'] >= pd.to_datetime('2022-11-09')) & ('Claim_Created_Date'] <= pd.to_datetime('2022-12-01')) & 'Provider_Code'] == 'PC104116']"
+  },
+  {
+    "Query": "List claims where Deduction Amount between 1 and 8 and Claim Number contains 'CBGDC2' How many are there?",
+    "pandas_code": "df[('Deduction_Amount'] >= 1.01) & ('Deduction_Amount'] <= 8.96) & 'Claim_Number'].str.contains(r'CBGDC2', na=False)].shape[0]"
+  },
+  {
+    "Query": "List claims where Product is 'Dental' and Claim Type contains 'Cashle' How many are there?",
+    "pandas_code": "df['Product'] == 'Dental' & 'Claim_Type'].str.contains(r'Cashle', na=False)].shape[0]"
+  },
+  {
+    "Query": "How many claims where Claim Id between 266794 and 276191",
+    "pandas_code": "df[(df['Claim_Id'] >= 266794.38) & (df['Claim_Id'] <= 276191.69)].shape[0]"
+  },
+  {
+    "Query": "Do we have retrieve Min Deduction_Amount.",
+    "pandas_code": "df['Deduction_Amount'].min()"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount equal to 897 and Deduction Amount at least 3.65.",
+    "pandas_code": "df['Approved_Amount'] == 897 & 'Deduction_Amount'] >= 3.65]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 386 and 912 and Claim Type contains 'CHS'.",
+    "pandas_code": "df[('Claim_Amount'] >= 386.93) & ('Claim_Amount'] <= 912.97) & 'Claim_Type'].str.contains(r'CHS', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 265401 and 278547 and Patient Name is 'Ms Karen Tobioka'.",
+    "pandas_code": "df[('Claim_Id'] >= 265401.69) & ('Claim_Id'] <= 278547.62) & 'Patient_Name'] == 'Ms Karen Tobioka']"
+  },
+  {
+    "Query": "Can you provide claims where Patient Name contains 'Jingyu' and Scheme is 'SME 1-SP-1500'.",
+    "pandas_code": "df['Patient_Name'].str.contains(r'Jingyu', na=False) & 'Scheme'] == 'SME 1-SP-1500']"
+  },
+  {
+    "Query": "Get claims where Product contains 'Specia' and Status is 'Payment Approval'.",
+    "pandas_code": "df['Product'].str.contains(r'Specia', na=False) & 'Status'] == 'Payment Approval']"
+  },
+  {
+    "Query": "Tell me claims where Claim Created Date before 2022-11-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-11-27')]"
+  },
+  {
+    "Query": "Count how many me claims where Deduction Amount between 4 and 7",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 4.88) & (df['Deduction_Amount'] <= 7.88)].shape[0]"
+  },
+  {
+    "Query": "Tell me claims where Approved Amount between 691 and 1114.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 691.6) & (df['Approved_Amount'] <= 1114.79)]"
+  },
+  {
+    "Query": "Retrieve claims where Policy Number is 'BGDC211000402-02-000' and Provider Name is 'Other Overseas Dental Clinic' How many are there?",
+    "pandas_code": "df['Policy_Number'] == 'BGDC211000402-02-000' & 'Provider_Name'] == 'Other Overseas Dental Clinic'].shape[0]"
+  },
+  {
+    "Query": "How many me claims where Loss Date between 2022-11-20 and 2023-06-15",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-20')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-15'))].shape[0]"
+  },
+  {
+    "Query": "Give me claims where Claim Created Date before 2023-06-11.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-06-11')]"
+  },
+  {
+    "Query": "List claims where Approved Amount at least 299 and Provider Name is 'NATIONAL UNIVERSITY HOSPITAL (NUH)'.",
+    "pandas_code": "df['Approved_Amount'] >= 299 & 'Provider_Name'] == 'NATIONAL UNIVERSITY HOSPITAL (NUH)']"
+  },
+  {
+    "Query": "Number of claims where Loss Date before 2022-12-04",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-04')].shape[0]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount greater than 446.42.",
+    "pandas_code": "df[df['Claim_Amount'] > 446.42]"
+  },
+  {
+    "Query": "How many claims where Provider Code is 'DR100002'",
+    "pandas_code": "df[df['Provider_Code'] == 'DR100002'].shape[0]"
+  },
+  {
+    "Query": "Find claims where Patient Name contains 'Alicia'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Alicia', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Provider Name is 'Jurong Medical Centre' and Approved Amount less than 1385.75.",
+    "pandas_code": "df['Provider_Name'] == 'Jurong Medical Centre' & 'Approved_Amount'] < 1385.75]"
+  },
+  {
+    "Query": "List all claims where Approved_Amount is greater than 200.",
+    "pandas_code": "df[df['Approved_Amount'] > 200]"
+  },
+  {
+    "Query": "Show claims where Deduction_Amount is less than 100 and Status is 'Approved'.",
+    "pandas_code": "df[(df['Deduction_Amount'] < 100) & (df['Status'] == 'Approved')]"
+  },
+  {
+    "Query": "Fetch all claims created after June 1, 2023.",
+    "pandas_code": "df[df['Claim_Created_Date'] > '2023-06-01']"
+  },
+  {
+    "Query": "Count how many claims have Loss_Type equal to 'Hospitalisation'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Hospitalisation'].shape[0]"
+  },
+  {
+    "Query": "List all claims where Claim_Amount is greater than Approved_Amount.",
+    "pandas_code": "df[df['Claim_Amount'] > df['Approved_Amount']]"
+  },
+  {
+    "Query": "Show all claims from Provider_Name 'Alliance Medinet Pte Ltd'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Alliance Medinet Pte Ltd']"
+  },
+  {
+    "Query": "Get claims where Product is 'Group Specialist' and Approved_Amount exceeds 300.",
+    "pandas_code": "df[(df['Product'] == 'Group Specialist') & (df['Approved_Amount'] > 300)]"
+  },
+  {
+    "Query": "Display claims where Claim_Type is 'Reimbursement' and Deduction_Amount is greater than 50.",
+    "pandas_code": "df[(df['Claim_Type'] == 'Reimbursement') & (df['Deduction_Amount'] > 50)]"
+  },
+  {
+    "Query": "Find all claims where Loss_Date is before January 1, 2023.",
+    "pandas_code": "df[df['Loss_Date'] < '2023-01-01']"
+  },
+  {
+    "Query": "Show all claims grouped by Product with their average Approved_Amount.",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean()"
+  },
+  {
+    "Query": "List top 5 claims with the highest Approved_Amount.",
+    "pandas_code": "df.nlargest(5, 'Approved_Amount')"
+  },
+  {
+    "Query": "Get all claims where Claim_Amount minus Deduction_Amount is greater than 100.",
+    "pandas_code": "df[(df['Claim_Amount'] - df['Deduction_Amount']) > 100]"
+  },
+  {
+    "Query": "Retrieve all claims created between June 2023 and August 2023.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= '2023-06-01') & (df['Claim_Created_Date'] <= '2023-08-31')]"
+  },
+  {
+    "Query": "Find all claims with Status 'Rejected'.",
+    "pandas_code": "df[df['Status'] == 'Rejected']"
+  },
+  {
+    "Query": "Show claims where Scheme is 'SME 1-GP' and Loss_Type is 'GP Treatment'.",
+    "pandas_code": "df[(df['Scheme'] == 'SME 1-GP') & (df['Loss_Type'] == 'GP Treatment')]"
+  },
+ 
+  {
+    "Query": "Provide claims where Deduction Amount less than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] < 0.0]"
+  },
+  {
+    "Query": "Provide claims where Claim Id less than 271325.48.",
+    "pandas_code": "df[df['Claim_Id'] < 271325.48]"
+  },
+  {
+    "Query": "Find claims where Claim Type contains 'Reimbu'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Reimbu', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount between 678 and 1020.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 678.7) & (df['Claim_Amount'] <= 1020.54)]"
+  },
+  {
+    "Query": "Provide claims where Deduction Amount equal to 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] == 0.0]"
+  },
+  {
+    "Query": "Find claims where Claim Type is 'CHS'.",
+    "pandas_code": "df[df['Claim_Type'] == 'CHS']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-05-21.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-21')]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 568 and 1050.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 568.35) & (df['Approved_Amount'] <= 1050.77)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 266055 and 272643.",
+    "pandas_code": "df[(df['Claim_Id'] >= 266055.88) & (df['Claim_Id'] <= 272643.39)]"
+  },
+  {
+    "Query": "Fetch claims where Provider Code is 'PC101195'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101195']"
+  },
+  {
+    "Query": "Show claims where Deduction Amount equal to 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] == 0.0]"
+  },
+  {
+    "Query": "Fetch claims where Provider Name contains 'TAN TO'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'TAN TO', na=False)]"
+  },
+  {
+    "Query": "List claims where Scheme contains 'New Te'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'New Te', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Status Code is 'CV0'.",
+    "pandas_code": "df[df['Status_Code'] == 'CV0']"
+  },
+  {
+    "Query": "Display claims where Claim Id between 263050 and 276089.",
+    "pandas_code": "df[(df['Claim_Id'] >= 263050.92) & (df['Claim_Id'] <= 276089.32)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2023-02-05 and 2023-02-12.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-05')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-02-12'))]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 557 and 1243.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 557.24) & (df['Claim_Amount'] <= 1243.41)]"
+  },
+  {
+    "Query": "Fetch claims where Deduction Amount at least 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] >= 0.0]"
+  },
+  {
+    "Query": "Get claims where Status Code is 'RJ2'.",
+    "pandas_code": "df[df['Status_Code'] == 'RJ2']"
+  },
+  {
+    "Query": "Display claims where Approved Amount at most 271.64.",
+    "pandas_code": "df[df['Approved_Amount'] <= 271.64]"
+  },
+  {
+    "Query": "Display claims where Provider Code contains 'PC1040'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1040', na=False)]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 753 and 1465.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 753.98) & (df['Claim_Amount'] <= 1465.29)]"
+  },
+  {
+    "Query": "Display claims where Claim Id between 267693 and 278182.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267693.08) & (df['Claim_Id'] <= 278182.51)]"
+  },
+  {
+    "Query": "Show claims where Claim Number contains 'CBGDC2'.",
+    "pandas_code": "df[df['Claim_Number'].str.contains(r'CBGDC2', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date before 2023-04-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-24')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Number is 'CBGDC23069216-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23069216-00']"
+  },
+  {
+    "Query": "Fetch claims where Scheme is 'Per Disability Template 1'.",
+    "pandas_code": "df[df['Scheme'] == 'Per Disability Template 1']"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 343 and 1496.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 343.24) & (df['Claim_Amount'] <= 1496.44)]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2023-03-31.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-31')]"
+  },
+  {
+    "Query": "List claims where Deduction Amount at most 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] <= 0.0]"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2023-03-23.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-23')]"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 620 and 1376.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 620.3) & (df['Approved_Amount'] <= 1376.09)]"
+  },
+  {
+    "Query": "Display claims where Claim Id between 269740 and 278435.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269740.19) & (df['Claim_Id'] <= 278435.26)]"
+  },
+  {
+    "Query": "Show claims where Provider Name is 'RAFFLESMEDICAL (Raffles Hospital)'.",
+    "pandas_code": "df[df['Provider_Name'] == 'RAFFLESMEDICAL (Raffles Hospital)']"
+  },
+  {
+    "Query": "Get claims where Status Code is 'CV0'.",
+    "pandas_code": "df[df['Status_Code'] == 'CV0']"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 58 and 1431.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 58.29) & (df['Claim_Amount'] <= 1431.7)]"
+  },
+  {
+    "Query": "Retrieve claims where Product contains 'Hospit'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Hospit', na=False)]"
+  },
+  {
+    "Query": "Get claims where Provider Code is 'PC103191'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103191']"
+  },
+  {
+    "Query": "Get claims where Patient Name is 'Mr More Shlok Vinayak'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr More Shlok Vinayak']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Type is 'Reimbursement'.",
+    "pandas_code": "df[df['Claim_Type'] == 'Reimbursement']"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2022-12-25.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-25')]"
+  },
+  {
+    "Query": "Fetch claims where Provider Code contains 'PC1040'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1040', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 242 and 857.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 242.01) & (df['Approved_Amount'] <= 857.28)]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-03-08.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-08')]"
+  },
+  {
+    "Query": "Find claims where Claim Number is 'CBGDC23069997-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23069997-00']"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount less than 210.4.",
+    "pandas_code": "df[df['Approved_Amount'] < 210.4]"
+  },
+  {
+    "Query": "List claims where Deduction Amount between 0 and 0.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 0.0) & (df['Deduction_Amount'] <= 0.0)]"
+  },
+  {
+    "Query": "Get claims where Status Code is 'PY1'.",
+    "pandas_code": "df[df['Status_Code'] == 'PY1']"
+  },
+  {
+    "Query": "Retrieve claims where Deduction Amount greater than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.0]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 654 and 1370.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 654.14) & (df['Approved_Amount'] <= 1370.85)]"
+  },
+  {
+    "Query": "Display claims where Loss Type contains 'GP Tre'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'GP Tre', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-04-08.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-08')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2022-11-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-12')]"
+  },
+  {
+    "Query": "Retrieve claims where Status Code contains 'AP2'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'AP2', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2022-12-16 and 2023-07-25.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-16')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-25'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 645 and 1451.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 645.55) & (df['Claim_Amount'] <= 1451.27)]"
+  },
+  {
+    "Query": "Find claims where Deduction Amount between 0 and 0.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 0.0) & (df['Deduction_Amount'] <= 0.0)]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 686 and 1003.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 686.8) & (df['Claim_Amount'] <= 1003.82)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-02-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-04')]"
+  },
+  {
+    "Query": "Show claims where Deduction Amount between 0 and 0.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 0.0) & (df['Deduction_Amount'] <= 0.0)]"
+  },
+  {
+    "Query": "Find claims where Loss Type contains 'Hospit'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Hospit', na=False)]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 537 and 1336.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 537.09) & (df['Approved_Amount'] <= 1336.77)]"
+  },
+  {
+    "Query": "Get claims where Provider Name contains 'Yap Te'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Yap Te', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Type is 'CHS'.",
+    "pandas_code": "df[df['Claim_Type'] == 'CHS']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2023-05-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-24')]"
+  },
+  {
+    "Query": "Find claims where Claim Number is 'CBGDP23037704-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23037704-00']"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-06-15.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-06-15')]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount greater than 340.16.",
+    "pandas_code": "df[df['Approved_Amount'] > 340.16]"
+  },
+  {
+    "Query": "List claims where Claim Amount between 763 and 1370.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 763.57) & (df['Claim_Amount'] <= 1370.13)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-04-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-23')]"
+  },
+  {
+    "Query": "List claims where Deduction Amount at least 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] >= 0.0]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-01-07 and 2023-06-26.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-07')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-26'))]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2022-11-20.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-20')]"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2023-05-29 and 2023-06-11.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-05-29')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-11'))]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2022-12-09.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-12-09')]"
+  },
+  {
+    "Query": "Provide claims where Product is 'Group Hospital and Surgical'.",
+    "pandas_code": "df[df['Product'] == 'Group Hospital and Surgical']"
+  },
+  {
+    "Query": "Show claims where Claim Type is 'CHS'.",
+    "pandas_code": "df[df['Claim_Type'] == 'CHS']"
+  },
+  {
+    "Query": "List claims where Approved Amount between 395 and 1193.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 395.27) & (df['Approved_Amount'] <= 1193.15)]"
+  },
+  {
+    "Query": "Display claims where Claim Type is 'Cashless'.",
+    "pandas_code": "df[df['Claim_Type'] == 'Cashless']"
+  },
+  {
+    "Query": "Retrieve claims where Policy Number contains 'BGDC22'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDC22', na=False)]"
+  },
+  {
+    "Query": "Get claims where Provider Name is 'TAN TOCK SENG HOSPITAL (OUTPATIENT)'.",
+    "pandas_code": "df[df['Provider_Name'] == 'TAN TOCK SENG HOSPITAL (OUTPATIENT)']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-07-14 and 2023-07-21.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-07-14')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-21'))]"
+  },
+  {
+    "Query": "List claims where Product contains 'Specia'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Specia', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 263187 and 271108.",
+    "pandas_code": "df[(df['Claim_Id'] >= 263187.43) & (df['Claim_Id'] <= 271108.73)]"
+  },
+  {
+    "Query": "Find claims where Claim Number contains 'CBGDC2'.",
+    "pandas_code": "df[df['Claim_Number'].str.contains(r'CBGDC2', na=False)]"
+  },
+  {
+    "Query": "Get claims where Deduction Amount between 0 and 0.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 0.0) & (df['Deduction_Amount'] <= 0.0)]"
+  },
+  {
+    "Query": "List claims where Claim Amount less than 1171.78.",
+    "pandas_code": "df[df['Claim_Amount'] < 1171.78]"
+  },
+  {
+    "Query": "List claims where Policy Number contains 'BGDC21'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDC21', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Type is 'Reimbursement'.",
+    "pandas_code": "df[df['Claim_Type'] == 'Reimbursement']"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2023-05-05 and 2023-07-13.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-05-05')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-13'))]"
+  },
+  {
+    "Query": "Retrieve claims where Deduction Amount at least 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] >= 0.0]"
+  },
+  {
+    "Query": "Display claims where Status Code is 'RJ2'.",
+    "pandas_code": "df[df['Status_Code'] == 'RJ2']"
+  },
+  {
+    "Query": "Fetch claims where Product is 'Group Hospital and Surgical'.",
+    "pandas_code": "df[df['Product'] == 'Group Hospital and Surgical']"
+  },
+  {
+    "Query": "Show claims where Policy Number is 'BGDC211000555-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000555-01-000']"
+  },
+  {
+    "Query": "Find claims where Status is 'Paid'.",
+    "pandas_code": "df[df['Status'] == 'Paid']"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-07-02.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-07-02')]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 106 and 1024.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 106.76) & (df['Approved_Amount'] <= 1024.88)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2023-01-11.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-11')]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 319 and 1165.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 319.32) & (df['Approved_Amount'] <= 1165.77)]"
+  },
+  {
+    "Query": "List claims where Loss Type contains 'Hospit'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Hospit', na=False)]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-02-14.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-14')]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2022-11-28.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-28')]"
+  },
+  {
+    "Query": "Get claims where Scheme is 'SME 1-GP'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-GP']"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name is 'Ms Lay Har (Huang Lixia) Ng'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Lay Har (Huang Lixia) Ng']"
+  },
+  {
+    "Query": "Get claims where Claim Id between 267766 and 270766.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267766.95) & (df['Claim_Id'] <= 270766.42)]"
+  },
+  {
+    "Query": "Show claims where Product contains 'Dental'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Dental', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Amount between 290 and 867.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 290.41) & (df['Claim_Amount'] <= 867.04)]"
+  },
+  {
+    "Query": "Fetch claims where Policy Number is 'BGDP221000491-00-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000491-00-000']"
+  },
+  {
+    "Query": "Find claims where Claim Id at least 265537.6.",
+    "pandas_code": "df[df['Claim_Id'] >= 265537.6]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2023-07-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-07-12')]"
+  },
+  {
+    "Query": "List claims where Loss Date after 2023-03-29.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-29')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-03-08 and 2023-05-04.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-08')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-04'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2023-06-11.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-11')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-01-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-19')]"
+  },
+  {
+    "Query": "Retrieve claims where Policy Number contains 'BGDC21'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDC21', na=False)]"
+  },
+  {
+    "Query": "Display claims where Status contains 'Duplic'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Duplic', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 188 and 957.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 188.76) & (df['Claim_Amount'] <= 957.67)]"
+  },
+  {
+    "Query": "Get claims where Claim Id less than 263379.48.",
+    "pandas_code": "df[df['Claim_Id'] < 263379.48]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-02-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-28')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-03-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-04')]"
+  },
+  {
+    "Query": "Find claims where Provider Code contains 'PC1031'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1031', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount less than 151.79.",
+    "pandas_code": "df[df['Claim_Amount'] < 151.79]"
+  },
+  {
+    "Query": "Find claims where Approved Amount at most 325.75.",
+    "pandas_code": "df[df['Approved_Amount'] <= 325.75]"
+  },
+  {
+    "Query": "Display claims where Patient Name is 'Ms Danqi Mao'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Danqi Mao']"
+  },
+  {
+    "Query": "Find claims where Policy Number contains 'BGDC21'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDC21', na=False)]"
+  },
+  {
+    "Query": "Get claims where Deduction Amount at least 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] >= 0.0]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-04-03 and 2023-06-01.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-04-03')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-01'))]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 481 and 958.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 481.07) & (df['Approved_Amount'] <= 958.13)]"
+  },
+  {
+    "Query": "List claims where Claim Type is 'Cashless'.",
+    "pandas_code": "df[df['Claim_Type'] == 'Cashless']"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2022-12-30.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-30')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date between 2022-12-26 and 2023-02-15.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-26')) & (df['Loss_Date'] <= pd.to_datetime('2023-02-15'))]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2022-11-29.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-29')]"
+  },
+  {
+    "Query": "List claims where Status Code is 'CV0'.",
+    "pandas_code": "df[df['Status_Code'] == 'CV0']"
+  },
+  {
+    "Query": "Provide claims where Loss Date between 2023-03-26 and 2023-06-10.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-26')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-10'))]"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 560 and 818.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 560.3) & (df['Approved_Amount'] <= 818.57)]"
+  },
+  {
+    "Query": "Find claims where Approved Amount at most 623.47.",
+    "pandas_code": "df[df['Approved_Amount'] <= 623.47]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 54 and 1541.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 54.07) & (df['Claim_Amount'] <= 1541.24)]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-07-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-24')]"
+  },
+  {
+    "Query": "Find claims where Status Code contains 'RJ2'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'RJ2', na=False)]"
+  },
+  {
+    "Query": "List claims where Provider Name contains 'Others'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Others', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2023-03-20.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-20')]"
+  },
+  {
+    "Query": "Show claims where Provider Name is 'SINGAPORE NATIONAL EYE CENTRE'.",
+    "pandas_code": "df[df['Provider_Name'] == 'SINGAPORE NATIONAL EYE CENTRE']"
+  },
+  {
+    "Query": "Fetch claims where Claim Id less than 265294.66.",
+    "pandas_code": "df[df['Claim_Id'] < 265294.66]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-06-16.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-06-16')]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 579 and 979.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 579.39) & (df['Approved_Amount'] <= 979.91)]"
+  },
+  {
+    "Query": "List claims where Claim Id between 265563 and 278188.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265563.71) & (df['Claim_Id'] <= 278188.01)]"
+  },
+  {
+    "Query": "Provide claims where Status Code contains 'PY3'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'PY3', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id greater than 279608.34.",
+    "pandas_code": "df[df['Claim_Id'] > 279608.34]"
+  },
+  {
+    "Query": "Show claims where Status contains 'Approv'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Approv', na=False)]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-04-01.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-01')]"
+  },
+  {
+    "Query": "Find claims where Claim Id between 270616 and 275498.",
+    "pandas_code": "df[(df['Claim_Id'] >= 270616.46) & (df['Claim_Id'] <= 275498.78)]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-01-04.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-01-04')]"
+  },
+  {
+    "Query": "Show claims where Scheme is 'Template 2'.",
+    "pandas_code": "df[df['Scheme'] == 'Template 2']"
+  },
+  {
+    "Query": "Find claims where Approved Amount at most 986.27.",
+    "pandas_code": "df[df['Approved_Amount'] <= 986.27]"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2023-02-06.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-02-06')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2023-03-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-19')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2023-02-18 and 2023-04-08.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-18')) & (df['Loss_Date'] <= pd.to_datetime('2023-04-08'))]"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 61 and 1335.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 61.85) & (df['Approved_Amount'] <= 1335.07)]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-03-09 and 2023-07-01.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-09')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-01'))]"
+  },
+  {
+    "Query": "Display claims where Product contains 'Group '.",
+    "pandas_code": "df[df['Product'].str.contains(r'Group ', na=False)]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-07-30.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-30')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-01-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-16')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Number contains 'CBGDC2'.",
+    "pandas_code": "df[df['Claim_Number'].str.contains(r'CBGDC2', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-04-02 and 2023-05-15.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-04-02')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-15'))]"
+  },
+  {
+    "Query": "Display claims where Claim Id between 268104 and 278025.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268104.21) & (df['Claim_Id'] <= 278025.77)]"
+  },
+  {
+    "Query": "Get claims where Deduction Amount greater than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.0]"
+  },
+  {
+    "Query": "Display claims where Claim Id between 269945 and 272246.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269945.66) & (df['Claim_Id'] <= 272246.68)]"
+  },
+  {
+    "Query": "Provide claims where Deduction Amount at least 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] >= 0.0]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2023-01-29 and 2023-03-18.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-29')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-18'))]"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2023-03-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-12')]"
+  },
+  {
+    "Query": "Display claims where Deduction Amount between 0 and 0.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 0.0) & (df['Deduction_Amount'] <= 0.0)]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-07-13.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-07-13')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2022-11-29.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-29')]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 526 and 1215.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 526.21) & (df['Approved_Amount'] <= 1215.91)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount equal to 166.65.",
+    "pandas_code": "df[df['Claim_Amount'] == 166.65]"
+  },
+  {
+    "Query": "Display claims where Provider Code is 'PC104074'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104074']"
+  },
+  {
+    "Query": "Show claims where Claim Id at most 262807.42.",
+    "pandas_code": "df[df['Claim_Id'] <= 262807.42]"
+  },
+  {
+    "Query": "List claims where Claim Id equal to 279252.05.",
+    "pandas_code": "df[df['Claim_Id'] == 279252.05]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2023-06-04 and 2023-07-09.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-06-04')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-09'))]"
+  },
+  {
+    "Query": "List claims where Claim Created Date before 2022-11-14.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-11-14')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Number contains 'CBGDP2'.",
+    "pandas_code": "df[df['Claim_Number'].str.contains(r'CBGDP2', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2022-12-09.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-09')]"
+  },
+  {
+    "Query": "Find claims where Product is 'Group OP General Practitioner'.",
+    "pandas_code": "df[df['Product'] == 'Group OP General Practitioner']"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount at most 553.07.",
+    "pandas_code": "df[df['Approved_Amount'] <= 553.07]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-03-21.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-21')]"
+  },
+  {
+    "Query": "Find claims where Product is 'Dental'.",
+    "pandas_code": "df[df['Product'] == 'Dental']"
+  },
+  {
+    "Query": "Fetch claims where Provider Name contains 'Nuffie'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Nuffie', na=False)]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 92 and 1251.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 92.21) & (df['Claim_Amount'] <= 1251.95)]"
+  },
+  {
+    "Query": "Display claims where Product is 'General Practitioner'.",
+    "pandas_code": "df[df['Product'] == 'General Practitioner']"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2023-02-11 and 2023-03-19.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-11')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-19'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Type is 'Reimbursement'.",
+    "pandas_code": "df[df['Claim_Type'] == 'Reimbursement']"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2023-02-11 and 2023-05-18.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-11')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-18'))]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 709 and 843.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 709.12) & (df['Approved_Amount'] <= 843.87)]"
+  },
+  {
+    "Query": "Display claims where Deduction Amount equal to 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] == 0.0]"
+  },
+  {
+    "Query": "Show claims where Claim Amount equal to 513.37.",
+    "pandas_code": "df[df['Claim_Amount'] == 513.37]"
+  },
+  {
+    "Query": "Find claims where Claim Id at least 271323.25.",
+    "pandas_code": "df[df['Claim_Id'] >= 271323.25]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-06-18 and 2023-06-24.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-06-18')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-24'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2022-12-28 and 2023-04-01.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-28')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-04-01'))]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2022-12-12.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-12')]"
+  },
+  {
+    "Query": "Display claims where Policy Number is 'BGDP221000130-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000130-01-000']"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-05-26 and 2023-07-06.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-05-26')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-06'))]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-06-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-24')]"
+  },
+  {
+    "Query": "Find claims where Policy Number is 'BGDC211000300-02-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000300-02-000']"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 431 and 1085.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 431.63) & (df['Approved_Amount'] <= 1085.47)]"
+  },
+  {
+    "Query": "Show claims where Claim Amount equal to 1129.34.",
+    "pandas_code": "df[df['Claim_Amount'] == 1129.34]"
+  },
+  {
+    "Query": "Find claims where Approved Amount at least 1370.31.",
+    "pandas_code": "df[df['Approved_Amount'] >= 1370.31]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2023-02-05 and 2023-05-12.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-05')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-12'))]"
+  },
+  {
+    "Query": "Display claims where Provider Code is 'PC100541'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100541']"
+  },
+  {
+    "Query": "Fetch claims where Provider Name contains 'HEALTH'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'HEALTH', na=False)]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-03-10.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-10')]"
+  },
+  {
+    "Query": "List claims where Provider Code is 'PC101350'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "List claims where Approved Amount between 516 and 1427.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 516.34) & (df['Approved_Amount'] <= 1427.22)]"
+  },
+  {
+    "Query": "List claims where Claim Amount equal to 558.2.",
+    "pandas_code": "df[df['Claim_Amount'] == 558.2]"
+  },
+  {
+    "Query": "Provide claims where Loss Date between 2023-02-01 and 2023-03-03.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-01')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-03'))]"
+  },
+  {
+    "Query": "Find claims where Deduction Amount equal to 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] == 0.0]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2023-03-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-27')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2023-04-04.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-04')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Type contains 'Cashle'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Cashle', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2022-11-17.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-11-17')]"
+  },
+  {
+    "Query": "Retrieve claims where Policy Number contains 'BGDP22'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDP22', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Provider Name is 'Q & M Dental Surgery (Bedok)'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Q & M Dental Surgery (Bedok)']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date on 2023-03-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-12')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-03-30.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-30')]"
+  },
+  {
+    "Query": "Display claims where Status contains 'Void'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Void', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Status Code is 'CV0'.",
+    "pandas_code": "df[df['Status_Code'] == 'CV0']"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2023-02-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-15')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount at most 502.56.",
+    "pandas_code": "df[df['Claim_Amount'] <= 502.56]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-01-11 and 2023-07-28.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-11')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-28'))]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 171 and 1215.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 171.29) & (df['Approved_Amount'] <= 1215.53)]"
+  },
+  {
+    "Query": "Provide claims where Claim Type contains 'Reimbu'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Reimbu', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount at least 584.67.",
+    "pandas_code": "df[df['Approved_Amount'] >= 584.67]"
+  },
+  {
+    "Query": "Get claims where Deduction Amount at most 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] <= 0.0]"
+  },
+  {
+    "Query": "List claims where Claim Amount between 430 and 906.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 430.4) & (df['Claim_Amount'] <= 906.37)]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date on 2023-01-07.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-07')]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-04-12.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-12')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2023-06-05.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-06-05')]"
+  },
+  {
+    "Query": "Show claims where Policy Number contains 'BGDC22'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDC22', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 25 and 1336.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 25.07) & (df['Approved_Amount'] <= 1336.82)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2023-06-21 and 2023-06-22.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-06-21')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-22'))]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-02-10.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-02-10')]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date on 2023-04-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-04-24')]"
+  },
+  {
+    "Query": "Display claims where Provider Name contains 'Bukit '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Bukit ', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount less than 1391.45.",
+    "pandas_code": "df[df['Approved_Amount'] < 1391.45]"
+  },
+  {
+    "Query": "Show claims where Approved Amount at least 935.45.",
+    "pandas_code": "df[df['Approved_Amount'] >= 935.45]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 562 and 1350.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 562.38) & (df['Approved_Amount'] <= 1350.82)]"
+  },
+  {
+    "Query": "Find claims where Claim Number is 'CBGDC23069765-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23069765-00']"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 636 and 933.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 636.26) & (df['Approved_Amount'] <= 933.77)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-07-31.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-31')]"
+  },
+  {
+    "Query": "Show claims where Claim Type contains 'CHS'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'CHS', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date after 2023-02-20.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-02-20')]"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2022-11-16 and 2023-03-27.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-16')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-27'))]"
+  },
+  {
+    "Query": "Display claims where Claim Number contains 'CBGDC2'.",
+    "pandas_code": "df[df['Claim_Number'].str.contains(r'CBGDC2', na=False)]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2022-11-23.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-23')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-01-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-25')]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2022-12-23 and 2023-07-31.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-23')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-31'))]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 744 and 1063.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 744.78) & (df['Claim_Amount'] <= 1063.54)]"
+  },
+  {
+    "Query": "List claims where Loss Date before 2023-02-24.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-24')]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount greater than 1305.92.",
+    "pandas_code": "df[df['Approved_Amount'] > 1305.92]"
+  },
+  {
+    "Query": "Retrieve claims where Status Code contains 'PY1'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'PY1', na=False)]"
+  },
+  {
+    "Query": "Show claims where Loss Type contains 'Dental'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Dental', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Claim Id between 266056 and 278405.",
+    "pandas_code": "df[(df['Claim_Id'] >= 266056.33) & (df['Claim_Id'] <= 278405.49)]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-07-30.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-30')]"
+  },
+  {
+    "Query": "List claims where Loss Date before 2022-11-16.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-16')]"
+  },
+  {
+    "Query": "Fetch claims where Deduction Amount between 0 and 0.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 0.0) & (df['Deduction_Amount'] <= 0.0)]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 657 and 1526.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 657.86) & (df['Approved_Amount'] <= 1526.18)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 123 and 1453.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 123.57) & (df['Approved_Amount'] <= 1453.02)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2023-01-26.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-01-26')]"
+  },
+  {
+    "Query": "Show claims where Status Code contains 'AP2'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'AP2', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Id equal to 263262.03.",
+    "pandas_code": "df[df['Claim_Id'] == 263262.03]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-06-17.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-17')]"
+  },
+  {
+    "Query": "Fetch claims where Scheme is 'Template 2'.",
+    "pandas_code": "df[df['Scheme'] == 'Template 2']"
+  },
+  {
+    "Query": "Show claims where Claim Id between 269782 and 277850.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269782.74) & (df['Claim_Id'] <= 277850.93)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-04-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-13')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2022-11-19 and 2023-03-16.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-19')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-16'))]"
+  },
+  {
+    "Query": "Provide claims where Deduction Amount greater than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.0]"
+  },
+  {
+    "Query": "Show claims where Scheme is 'SME 1-SP-1500'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-SP-1500']"
+  },
+  {
+    "Query": "Find claims where Loss Type contains 'Specia'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Specia', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Type contains 'Cashle'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Cashle', na=False)]"
+  },
+  {
+    "Query": "Find claims where Provider Code is 'PC103185'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103185']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-07-02.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-02')]"
+  },
+  {
+    "Query": "List claims where Claim Id at least 273111.38.",
+    "pandas_code": "df[df['Claim_Id'] >= 273111.38]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-07-21.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-07-21')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 265632 and 279204.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265632.34) & (df['Claim_Id'] <= 279204.82)]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name contains 'Q & M '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Q & M ', na=False)]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-03-17 and 2023-06-23.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-17')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-23'))]"
+  },
+  {
+    "Query": "Get claims where Approved Amount greater than 807.24.",
+    "pandas_code": "df[df['Approved_Amount'] > 807.24]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2022-12-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-04')]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 67 and 1430.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 67.27) & (df['Approved_Amount'] <= 1430.72)]"
+  },
+  {
+    "Query": "List claims where Scheme is 'Per Disability Template 1'.",
+    "pandas_code": "df[df['Scheme'] == 'Per Disability Template 1']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2022-12-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-23')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date before 2023-03-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-04')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-04-07 and 2023-07-25.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-04-07')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-25'))]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name is 'Yap Teck Seng TCM'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Yap Teck Seng TCM']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2023-04-04.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-04')]"
+  },
+  {
+    "Query": "Find claims where Deduction Amount greater than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.0]"
+  },
+  {
+    "Query": "Display claims where Loss Type is 'Hospitalisation'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Hospitalisation']"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2023-01-26 and 2023-01-27.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-26')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-01-27'))]"
+  },
+  {
+    "Query": "Display claims where Status Code contains 'CV0'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'CV0', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Amount less than 900.52.",
+    "pandas_code": "df[df['Claim_Amount'] < 900.52]"
+  },
+  {
+    "Query": "Find claims where Claim Amount greater than 992.61.",
+    "pandas_code": "df[df['Claim_Amount'] > 992.61]"
+  },
+  {
+    "Query": "Retrieve claims where Product is 'Dental'.",
+    "pandas_code": "df[df['Product'] == 'Dental']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2022-12-10.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-12-10')]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name contains 'Mark L'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Mark L', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount equal to 53.44.",
+    "pandas_code": "df[df['Claim_Amount'] == 53.44]"
+  },
+  {
+    "Query": "Get claims where Provider Code is 'PC102259'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC102259']"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 760 and 1425.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 760.71) & (df['Claim_Amount'] <= 1425.23)]"
+  },
+  {
+    "Query": "Show claims where Scheme contains 'Per Di'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'Per Di', na=False)]"
+  },
+  {
+    "Query": "Display claims where Status contains 'Approv'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Approv', na=False)]"
+  },
+  {
+    "Query": "Find claims where Product contains 'Genera'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Genera', na=False)]"
+  },
+  {
+    "Query": "Display claims where Patient Name contains 'Ms Rad'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Rad', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Product is 'Specialist'.",
+    "pandas_code": "df[df['Product'] == 'Specialist']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-05-12.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-12')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2023-05-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-15')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id at most 277484.93.",
+    "pandas_code": "df[df['Claim_Id'] <= 277484.93]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-06-11 and 2023-07-28.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-06-11')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-28'))]"
+  },
+  {
+    "Query": "List claims where Claim Type contains 'CHS'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'CHS', na=False)]"
+  },
+  {
+    "Query": "Display claims where Policy Number contains 'BGDP22'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDP22', na=False)]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2022-12-11.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-11')]"
+  },
+  {
+    "Query": "Provide claims where Product is 'Dental'.",
+    "pandas_code": "df[df['Product'] == 'Dental']"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name is 'Tooth Matters Dental Surgery'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Tooth Matters Dental Surgery']"
+  },
+  {
+    "Query": "Find claims where Claim Amount at most 192.21.",
+    "pandas_code": "df[df['Claim_Amount'] <= 192.21]"
+  },
+  {
+    "Query": "Provide claims where Claim Type is 'CHS'.",
+    "pandas_code": "df[df['Claim_Type'] == 'CHS']"
+  },
+  {
+    "Query": "Fetch claims where Patient Name is 'Ms Hwee Ching Goh'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Hwee Ching Goh']"
+  },
+  {
+    "Query": "Provide claims where Loss Date between 2023-03-01 and 2023-07-14.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-01')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-14'))]"
+  },
+  {
+    "Query": "Display claims where Deduction Amount at least 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] >= 0.0]"
+  },
+  {
+    "Query": "Find claims where Claim Id between 269161 and 271356.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269161.18) & (df['Claim_Id'] <= 271356.34)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-04-02.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-02')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2023-05-23 and 2023-05-25.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-05-23')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-25'))]"
+  },
+  {
+    "Query": "List claims where Claim Amount greater than 1255.28.",
+    "pandas_code": "df[df['Claim_Amount'] > 1255.28]"
+  },
+  {
+    "Query": "Display claims where Claim Id at most 266631.52.",
+    "pandas_code": "df[df['Claim_Id'] <= 266631.52]"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 513 and 1305.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 513.67) & (df['Approved_Amount'] <= 1305.78)]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-07-01 and 2023-07-11.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-07-01')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-11'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2023-03-19 and 2023-05-07.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-19')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-07'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2022-12-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-12-25')]"
+  },
+  {
+    "Query": "Retrieve claims where Status Code contains 'PY3'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'PY3', na=False)]"
+  },
+  {
+    "Query": "Display claims where Product contains 'Genera'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Genera', na=False)]"
+  },
+  {
+    "Query": "List claims where Provider Code is 'PC100466'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100466']"
+  },
+  {
+    "Query": "List claims where Approved Amount less than 552.83.",
+    "pandas_code": "df[df['Approved_Amount'] < 552.83]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2022-11-28 and 2022-12-27.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-28')) & (df['Claim_Created_Date'] <= pd.to_datetime('2022-12-27'))]"
+  },
+  {
+    "Query": "Get claims where Patient Name is 'Mr Kim Chuan Francis Er'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Kim Chuan Francis Er']"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2022-11-18.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-18')]"
+  },
+  {
+    "Query": "Retrieve claims where Scheme contains 'New Te'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'New Te', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-06-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-25')]"
+  },
+  {
+    "Query": "Show claims where Provider Name is 'Tooth Matters Dental Surgery'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Tooth Matters Dental Surgery']"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-06-23 and 2023-07-29.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-06-23')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-29'))]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-04-17.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-17')]"
+  },
+  {
+    "Query": "Find claims where Claim Amount greater than 883.54.",
+    "pandas_code": "df[df['Claim_Amount'] > 883.54]"
+  },
+  {
+    "Query": "Get claims where Claim Number contains 'CBGDC2'.",
+    "pandas_code": "df[df['Claim_Number'].str.contains(r'CBGDC2', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Type contains 'Reimbu'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Reimbu', na=False)]"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2023-03-05 and 2023-07-27.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-05')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-27'))]"
+  },
+  {
+    "Query": "Get claims where Patient Name contains 'Mr Sha'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Sha', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount at most 927.99.",
+    "pandas_code": "df[df['Claim_Amount'] <= 927.99]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2022-11-18.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-18')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date between 2022-12-25 and 2023-03-27.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-25')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-27'))]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date on 2023-04-05.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-04-05')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 267787 and 278306.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267787.29) & (df['Claim_Id'] <= 278306.75)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-03-14.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-14')]"
+  },
+  {
+    "Query": "Provide claims where Claim Id between 261715 and 272879.",
+    "pandas_code": "df[(df['Claim_Id'] >= 261715.53) & (df['Claim_Id'] <= 272879.47)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-07-17.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-07-17')]"
+  },
+  {
+    "Query": "List claims where Claim Amount between 286 and 982.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 286.37) & (df['Claim_Amount'] <= 982.83)]"
+  },
+  {
+    "Query": "Fetch claims where Status is 'Duplicate'.",
+    "pandas_code": "df[df['Status'] == 'Duplicate']"
+  },
+  {
+    "Query": "Get claims where Patient Name contains 'Ms Zhe'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Zhe', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount between 637 and 869.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 637.75) & (df['Approved_Amount'] <= 869.29)]"
+  },
+  {
+    "Query": "Get claims where Approved Amount greater than 1357.05.",
+    "pandas_code": "df[df['Approved_Amount'] > 1357.05]"
+  },
+  {
+    "Query": "Find claims where Patient Name contains 'Mr Bal'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Bal', na=False)]"
+  },
+  {
+    "Query": "Display claims where Approved Amount less than 1224.36.",
+    "pandas_code": "df[df['Approved_Amount'] < 1224.36]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2022-12-31 and 2023-05-20.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-31')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-20'))]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date on 2022-12-21.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-12-21')]"
+  },
+  {
+    "Query": "Get claims where Status Code is 'AP2'.",
+    "pandas_code": "df[df['Status_Code'] == 'AP2']"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-06-07.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-06-07')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-03-06.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-06')]"
+  },
+  {
+    "Query": "Retrieve claims where Status is 'Payment Approval'.",
+    "pandas_code": "df[df['Status'] == 'Payment Approval']"
+  },
+  {
+    "Query": "Provide claims where Provider Name is 'Other TCM Clinic'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Other TCM Clinic']"
+  },
+  {
+    "Query": "List claims where Claim Amount equal to 1330.81.",
+    "pandas_code": "df[df['Claim_Amount'] == 1330.81]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-03-20 and 2023-04-18.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-20')) & (df['Loss_Date'] <= pd.to_datetime('2023-04-18'))]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 26 and 956.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 26.05) & (df['Approved_Amount'] <= 956.16)]"
+  },
+  {
+    "Query": "Display claims where Provider Code contains 'PC1013'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1013', na=False)]"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2022-11-09.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-09')]"
+  },
+  {
+    "Query": "Display claims where Scheme is 'New Template 1'.",
+    "pandas_code": "df[df['Scheme'] == 'New Template 1']"
+  },
+  {
+    "Query": "Find claims where Status contains 'Paid'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Paid', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2023-05-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-13')]"
+  },
+  {
+    "Query": "Display claims where Patient Name is 'Ms Narumatha Mohana Das'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Narumatha Mohana Das']"
+  },
+  {
+    "Query": "Display claims where Claim Amount equal to 1226.63.",
+    "pandas_code": "df[df['Claim_Amount'] == 1226.63]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2023-06-25.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-06-25')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-07-07.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-07-07')]"
+  },
+  {
+    "Query": "Show claims where Approved Amount at most 171.2.",
+    "pandas_code": "df[df['Approved_Amount'] <= 171.2]"
+  },
+  {
+    "Query": "Fetch claims where Deduction Amount at most 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] <= 0.0]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2022-12-18 and 2023-03-16.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-18')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-16'))]"
+  },
+  {
+    "Query": "Retrieve claims where Status Code is 'AP2'.",
+    "pandas_code": "df[df['Status_Code'] == 'AP2']"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount at most 1254.45.",
+    "pandas_code": "df[df['Approved_Amount'] <= 1254.45]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2023-01-14.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-14')]"
+  },
+  {
+    "Query": "List claims where Claim Id greater than 265471.81.",
+    "pandas_code": "df[df['Claim_Id'] > 265471.81]"
+  },
+  {
+    "Query": "Provide claims where Patient Name contains 'Ms Lai'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Lai', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2022-11-20 and 2023-02-26.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-20')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-02-26'))]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-01-23 and 2023-07-17.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-23')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-17'))]"
+  },
+  {
+    "Query": "Show claims where Loss Date before 2023-05-05.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-05')]"
+  },
+  {
+    "Query": "List claims where Claim Amount between 75 and 1085.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 75.66) & (df['Claim_Amount'] <= 1085.94)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id at most 269980.94.",
+    "pandas_code": "df[df['Claim_Id'] <= 269980.94]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date before 2023-04-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-28')]"
+  },
+  {
+    "Query": "Get claims where Product is 'Group Dental Benefit'.",
+    "pandas_code": "df[df['Product'] == 'Group Dental Benefit']"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 189 and 1459.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 189.07) & (df['Claim_Amount'] <= 1459.68)]"
+  },
+  {
+    "Query": "Display claims where Patient Name contains 'Ms Kae'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Kae', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Provider Name is 'SINGAPORE GENERAL HOSPITAL PTE LTD'.",
+    "pandas_code": "df[df['Provider_Name'] == 'SINGAPORE GENERAL HOSPITAL PTE LTD']"
+  },
+  {
+    "Query": "Show claims where Provider Code contains 'PC1004'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1004', na=False)]"
+  },
+  {
+    "Query": "Get claims where Loss Type is 'Hospitalisation'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Hospitalisation']"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name is 'CASA DENTAL ADMIRALTY'.",
+    "pandas_code": "df[df['Provider_Name'] == 'CASA DENTAL ADMIRALTY']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-03-24 and 2023-06-15.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-24')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-15'))]"
+  },
+  {
+    "Query": "Find claims where Claim Type contains 'Cashle'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Cashle', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date on 2023-05-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-28')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id at least 275417.62.",
+    "pandas_code": "df[df['Claim_Id'] >= 275417.62]"
+  },
+  {
+    "Query": "Find claims where Claim Id between 266881 and 270934.",
+    "pandas_code": "df[(df['Claim_Id'] >= 266881.27) & (df['Claim_Id'] <= 270934.15)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 267042 and 276544.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267042.06) & (df['Claim_Id'] <= 276544.57)]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2023-05-30.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-30')]"
+  },
+  {
+    "Query": "Show claims where Scheme is 'SME 3-SP-Panel Only'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 3-SP-Panel Only']"
+  },
+  {
+    "Query": "Get claims where Claim Amount less than 1391.72.",
+    "pandas_code": "df[df['Claim_Amount'] < 1391.72]"
+  },
+  {
+    "Query": "Provide claims where Provider Code is 'PC102378'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC102378']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-05-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-13')]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2022-11-22.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-11-22')]"
+  },
+  {
+    "Query": "Find claims where Claim Id between 264068 and 271577.",
+    "pandas_code": "df[(df['Claim_Id'] >= 264068.45) & (df['Claim_Id'] <= 271577.08)]"
+  },
+  {
+    "Query": "Retrieve claims where Deduction Amount equal to 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] == 0.0]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id less than 276131.37.",
+    "pandas_code": "df[df['Claim_Id'] < 276131.37]"
+  },
+  {
+    "Query": "Get claims where Approved Amount at least 1420.2.",
+    "pandas_code": "df[df['Approved_Amount'] >= 1420.2]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-02-05.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-02-05')]"
+  },
+  {
+    "Query": "Provide claims where Status Code contains 'CV0'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'CV0', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name contains 'TAN TO'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'TAN TO', na=False)]"
+  },
+  {
+    "Query": "Find claims where Provider Code is 'PC101350'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-07-19.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-07-19')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date on 2023-04-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-04-25')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-03-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-25')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-01-29.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-29')]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount at least 683.33.",
+    "pandas_code": "df[df['Approved_Amount'] >= 683.33]"
+  },
+  {
+    "Query": "Find claims where Approved Amount equal to 1412.47.",
+    "pandas_code": "df[df['Approved_Amount'] == 1412.47]"
+  },
+  {
+    "Query": "Find claims where Product is 'Group Specialist'.",
+    "pandas_code": "df[df['Product'] == 'Group Specialist']"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2023-02-08.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-08')]"
+  },
+  {
+    "Query": "Show claims where Approved Amount at most 815.41.",
+    "pandas_code": "df[df['Approved_Amount'] <= 815.41]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-01-11.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-11')]"
+  },
+  {
+    "Query": "Show claims where Loss Date before 2023-04-25.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-25')]"
+  },
+  {
+    "Query": "Find claims where Policy Number is 'BGDP211000636-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP211000636-01-000']"
+  },
+  {
+    "Query": "List claims where Loss Date before 2023-04-21.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-21')]"
+  },
+  {
+    "Query": "Find claims where Claim Id between 268487 and 276808.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268487.18) & (df['Claim_Id'] <= 276808.86)]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date before 2023-03-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-23')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date before 2022-11-20.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-11-20')]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-01-25 and 2023-02-07.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-25')) & (df['Loss_Date'] <= pd.to_datetime('2023-02-07'))]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2023-01-04 and 2023-05-03.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-04')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-03'))]"
+  },
+  {
+    "Query": "Fetch claims where Status Code contains 'PY1'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'PY1', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2022-11-28.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-28')]"
+  },
+  {
+    "Query": "Get claims where Approved Amount equal to 780.09.",
+    "pandas_code": "df[df['Approved_Amount'] == 780.09]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date on 2023-04-01.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-04-01')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-06-01.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-01')]"
+  },
+  {
+    "Query": "Provide claims where Deduction Amount between 0 and 0.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 0.0) & (df['Deduction_Amount'] <= 0.0)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount at least 552.79.",
+    "pandas_code": "df[df['Claim_Amount'] >= 552.79]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-07-21.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-21')]"
+  },
+  {
+    "Query": "Show claims where Claim Amount at most 803.34.",
+    "pandas_code": "df[df['Claim_Amount'] <= 803.34]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2023-05-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-19')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date on 2023-01-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-25')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2022-11-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-11-19')]"
+  },
+  {
+    "Query": "List claims where Patient Name is 'Ms Jiang Yuan'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Jiang Yuan']"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-02-11.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-02-11')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2023-07-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-04')]"
+  },
+  {
+    "Query": "Get claims where Claim Amount greater than 1388.51.",
+    "pandas_code": "df[df['Claim_Amount'] > 1388.51]"
+  },
+  {
+    "Query": "Find claims where Claim Id at least 269645.94.",
+    "pandas_code": "df[df['Claim_Id'] >= 269645.94]"
+  },
+  {
+    "Query": "Display claims where Patient Name is 'Mr Felix Gunawan'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Felix Gunawan']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2023-01-08.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-08')]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 663 and 1483.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 663.24) & (df['Approved_Amount'] <= 1483.85)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-03-06.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-06')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 265306 and 271434.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265306.54) & (df['Claim_Id'] <= 271434.32)]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date before 2023-05-20.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-20')]"
+  },
+  {
+    "Query": "Provide claims where Status is 'Duplicate'.",
+    "pandas_code": "df[df['Status'] == 'Duplicate']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2023-03-22.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-22')]"
+  },
+  {
+    "Query": "Fetch claims where Status Code is 'DU0'.",
+    "pandas_code": "df[df['Status_Code'] == 'DU0']"
+  },
+  {
+    "Query": "Get claims where Provider Name contains 'G CHEE'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'G CHEE', na=False)]"
+  },
+  {
+    "Query": "Show claims where Status is 'Void'.",
+    "pandas_code": "df[df['Status'] == 'Void']"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 130 and 1022.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 130.05) & (df['Approved_Amount'] <= 1022.64)]"
+  },
+  {
+    "Query": "Display claims where Deduction Amount at most 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] <= 0.0]"
+  },
+  {
+    "Query": "List claims where Claim Id equal to 266469.9.",
+    "pandas_code": "df[df['Claim_Id'] == 266469.9]"
+  },
+  {
+    "Query": "Provide claims where Patient Name is 'Mr Ali Mir Jahagir'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Ali Mir Jahagir']"
+  },
+  {
+    "Query": "Show claims where Loss Date between 2022-11-17 and 2023-04-20.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-17')) & (df['Loss_Date'] <= pd.to_datetime('2023-04-20'))]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2022-11-18.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-18')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date between 2023-03-31 and 2023-06-16.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-31')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-16'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount less than 1285.04.",
+    "pandas_code": "df[df['Claim_Amount'] < 1285.04]"
+  },
+  {
+    "Query": "Show claims where Patient Name is 'Ms Zhuojun He'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Zhuojun He']"
+  },
+  {
+    "Query": "Provide claims where Provider Code contains 'PC1031'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1031', na=False)]"
+  },
+  {
+    "Query": "List claims where Deduction Amount less than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] < 0.0]"
+  },
+  {
+    "Query": "Find claims where Status is 'Approved'.",
+    "pandas_code": "df[df['Status'] == 'Approved']"
+  },
+  {
+    "Query": "Fetch claims where Deduction Amount less than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] < 0.0]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 482 and 1351.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 482.1) & (df['Claim_Amount'] <= 1351.68)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2023-03-06.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-06')]"
+  },
+  {
+    "Query": "Get claims where Provider Name is 'Other Overseas Hospital'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Other Overseas Hospital']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2022-12-10 and 2023-07-31.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-10')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-31'))]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 422 and 1295.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 422.46) & (df['Approved_Amount'] <= 1295.87)]"
+  },
+  {
+    "Query": "Display claims where Scheme is 'SME 1-GP'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-GP']"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2022-12-01.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-01')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date before 2022-11-11.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-11-11')]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 97 and 1197.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 97.76) & (df['Approved_Amount'] <= 1197.42)]"
+  },
+  {
+    "Query": "Display claims where Policy Number is 'BGDP221000148-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000148-01-000']"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-01-15.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-15')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2023-03-24.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-24')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Type is 'Hospitalisation'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Hospitalisation']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 265604 and 272689.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265604.7) & (df['Claim_Id'] <= 272689.5)]"
+  },
+  {
+    "Query": "Show claims where Loss Date between 2022-12-03 and 2023-01-23.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-03')) & (df['Loss_Date'] <= pd.to_datetime('2023-01-23'))]"
+  },
+  {
+    "Query": "Get claims where Claim Id between 268093 and 273615.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268093.15) & (df['Claim_Id'] <= 273615.79)]"
+  },
+  {
+    "Query": "Find claims where Approved Amount at most 85.53.",
+    "pandas_code": "df[df['Approved_Amount'] <= 85.53]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date on 2023-01-20.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-20')]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 467 and 1576.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 467.35) & (df['Claim_Amount'] <= 1576.23)]"
+  },
+  {
+    "Query": "Retrieve claims where Deduction Amount between 0 and 0.",
+    "pandas_code": "df[(df['Deduction_Amount'] >= 0.0) & (df['Deduction_Amount'] <= 0.0)]"
+  },
+  {
+    "Query": "Get claims where Provider Name is 'Changi General Hospital'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Changi General Hospital']"
+  },
+  {
+    "Query": "Find claims where Claim Id equal to 276942.5.",
+    "pandas_code": "df[df['Claim_Id'] == 276942.5]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Number is 'CBGDC23061325-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23061325-00']"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2023-06-18 and 2023-07-31.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-06-18')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-31'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount less than 138.81.",
+    "pandas_code": "df[df['Claim_Amount'] < 138.81]"
+  },
+  {
+    "Query": "Get claims where Claim Id between 264774 and 275285.",
+    "pandas_code": "df[(df['Claim_Id'] >= 264774.44) & (df['Claim_Id'] <= 275285.67)]"
+  },
+  {
+    "Query": "Display claims where Status is 'Paid'.",
+    "pandas_code": "df[df['Status'] == 'Paid']"
+  },
+  {
+    "Query": "Find claims where Provider Name is 'Q & M Dental Surgery (Bedok)'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Q & M Dental Surgery (Bedok)']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2023-07-26.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-07-26')]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount less than 403.95.",
+    "pandas_code": "df[df['Approved_Amount'] < 403.95]"
+  },
+  {
+    "Query": "Find claims where Claim Amount less than 82.36.",
+    "pandas_code": "df[df['Claim_Amount'] < 82.36]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount at least 39.33.",
+    "pandas_code": "df[df['Claim_Amount'] >= 39.33]"
+  },
+  {
+    "Query": "List claims where Approved Amount greater than 60.28.",
+    "pandas_code": "df[df['Approved_Amount'] > 60.28]"
+  },
+  {
+    "Query": "List claims where Policy Number is 'BGDC211000361-02-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000361-02-000']"
+  },
+  {
+    "Query": "Show claims where Claim Number is 'CBGDC23061810-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23061810-00']"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2023-04-18.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-18')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date on 2023-01-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-27')]"
+  },
+  {
+    "Query": "Find claims where Claim Number is 'CBGDP23039022-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23039022-00']"
+  },
+  {
+    "Query": "Provide claims where Claim Type contains 'CHS'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'CHS', na=False)]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 620 and 939.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 620.81) & (df['Approved_Amount'] <= 939.86)]"
+  },
+  {
+    "Query": "Get claims where Scheme is 'SME 1-SP-CO-1500'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-SP-CO-1500']"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2022-12-26.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-26')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2022-11-28 and 2023-05-31.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-28')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-31'))]"
+  },
+  {
+    "Query": "List claims where Provider Name is 'Physio Down Under Pte ltd'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Physio Down Under Pte ltd']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount at least 788.53.",
+    "pandas_code": "df[df['Claim_Amount'] >= 788.53]"
+  },
+  {
+    "Query": "Display claims where Claim Id greater than 273639.11.",
+    "pandas_code": "df[df['Claim_Id'] > 273639.11]"
+  },
+  {
+    "Query": "Display claims where Claim Number is 'CBGDC23061879-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23061879-00']"
+  },
+  {
+    "Query": "List claims where Loss Date on 2022-12-20.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-20')]"
+  },
+  {
+    "Query": "Find claims where Patient Name is 'Ms Sarika Ramanathan'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Sarika Ramanathan']"
+  },
+  {
+    "Query": "Show claims where Status Code is 'AP2'.",
+    "pandas_code": "df[df['Status_Code'] == 'AP2']"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-02-12 and 2023-03-06.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-12')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-06'))]"
+  },
+  {
+    "Query": "Provide claims where Loss Date between 2022-12-04 and 2023-01-01.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-04')) & (df['Loss_Date'] <= pd.to_datetime('2023-01-01'))]"
+  },
+  {
+    "Query": "Provide claims where Scheme is 'Per Disability Template 2'.",
+    "pandas_code": "df[df['Scheme'] == 'Per Disability Template 2']"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2023-06-30.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-30')]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name contains 'Guang '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Guang ', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Loss Type is 'GP Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'GP Treatment']"
+  },
+  {
+    "Query": "Fetch claims where Product contains 'Group '.",
+    "pandas_code": "df[df['Product'].str.contains(r'Group ', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 135 and 1085.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 135.22) & (df['Approved_Amount'] <= 1085.06)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 534 and 1372.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 534.13) & (df['Claim_Amount'] <= 1372.14)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-07-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-12')]"
+  },
+  {
+    "Query": "Find claims where Approved Amount greater than 1162.02.",
+    "pandas_code": "df[df['Approved_Amount'] > 1162.02]"
+  },
+  {
+    "Query": "Find claims where Deduction Amount less than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] < 0.0]"
+  },
+  {
+    "Query": "Find claims where Product contains 'Dental'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Dental', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-01-08 and 2023-07-12.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-08')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-12'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2022-12-14.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-14')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-07-20.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-20')]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 506 and 1324.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 506.14) & (df['Approved_Amount'] <= 1324.35)]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 190 and 1301.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 190.71) & (df['Approved_Amount'] <= 1301.64)]"
+  },
+  {
+    "Query": "Display claims where Claim Id equal to 271543.7.",
+    "pandas_code": "df[df['Claim_Id'] == 271543.7]"
+  },
+  {
+    "Query": "Get claims where Claim Id between 263466 and 274680.",
+    "pandas_code": "df[(df['Claim_Id'] >= 263466.99) & (df['Claim_Id'] <= 274680.8)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2023-05-03.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-03')]"
+  },
+  {
+    "Query": "List claims where Scheme contains 'SME 3-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 3-', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Deduction Amount at most 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] <= 0.0]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id less than 275763.52.",
+    "pandas_code": "df[df['Claim_Id'] < 275763.52]"
+  },
+  {
+    "Query": "Provide claims where Claim Number is 'CBGDC23059416-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23059416-00']"
+  },
+  {
+    "Query": "Display claims where Claim Number is 'CBGDP23037782-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23037782-00']"
+  },
+  {
+    "Query": "List claims where Claim Amount between 450 and 1243.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 450.75) & (df['Claim_Amount'] <= 1243.4)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 604 and 1201.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 604.25) & (df['Claim_Amount'] <= 1201.13)]"
+  },
+  {
+    "Query": "Show claims where Loss Date before 2023-03-27.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-27')]"
+  },
+  {
+    "Query": "Get claims where Scheme contains 'SME 1-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 1-', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 226 and 1121.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 226.51) & (df['Claim_Amount'] <= 1121.44)]"
+  },
+  {
+    "Query": "Provide claims where Claim Number is 'CBGDC23062390-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23062390-00']"
+  },
+  {
+    "Query": "Display claims where Scheme is 'SME 1-SP-1500'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-SP-1500']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2022-12-21.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-21')]"
+  },
+  {
+    "Query": "Find claims where Claim Amount greater than 1270.75.",
+    "pandas_code": "df[df['Claim_Amount'] > 1270.75]"
+  },
+  {
+    "Query": "Show claims where Claim Amount at least 1040.92.",
+    "pandas_code": "df[df['Claim_Amount'] >= 1040.92]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2023-06-24.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-06-24')]"
+  },
+  {
+    "Query": "Find claims where Status is 'Duplicate'.",
+    "pandas_code": "df[df['Status'] == 'Duplicate']"
+  },
+  {
+    "Query": "Show claims where Claim Id between 269903 and 277602.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269903.8) & (df['Claim_Id'] <= 277602.97)]"
+  },
+  {
+    "Query": "Display claims where Loss Date on 2022-11-14.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-14')]"
+  },
+  {
+    "Query": "Show claims where Claim Id between 262649 and 276514.",
+    "pandas_code": "df[(df['Claim_Id'] >= 262649.76) & (df['Claim_Id'] <= 276514.97)]"
+  },
+  {
+    "Query": "Show claims where Loss Date before 2022-11-14.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-14')]"
+  },
+  {
+    "Query": "List claims where Loss Date before 2023-02-12.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-12')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Type contains 'Dental'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Dental', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id greater than 270253.58.",
+    "pandas_code": "df[df['Claim_Id'] > 270253.58]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date on 2023-07-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-07-24')]"
+  },
+  {
+    "Query": "List claims where Claim Number is 'CBGDP23044147-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23044147-00']"
+  },
+  {
+    "Query": "List claims where Product contains 'Hospit'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Hospit', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount equal to 1341.14.",
+    "pandas_code": "df[df['Claim_Amount'] == 1341.14]"
+  },
+  {
+    "Query": "Show claims where Claim Amount at least 1458.95.",
+    "pandas_code": "df[df['Claim_Amount'] >= 1458.95]"
+  },
+  {
+    "Query": "Provide claims where Loss Type is 'Dental'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Dental']"
+  },
+  {
+    "Query": "List claims where Claim Created Date on 2023-02-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-02-12')]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 489 and 906.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 489.27) & (df['Approved_Amount'] <= 906.52)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2023-01-31.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-31')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount at most 1481.48.",
+    "pandas_code": "df[df['Claim_Amount'] <= 1481.48]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 332 and 877.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 332.28) & (df['Approved_Amount'] <= 877.71)]"
+  },
+  {
+    "Query": "Show claims where Policy Number is 'BGDC211000067-02-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000067-02-000']"
+  },
+  {
+    "Query": "Show claims where Approved Amount equal to 491.57.",
+    "pandas_code": "df[df['Approved_Amount'] == 491.57]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2022-12-13 and 2023-03-21.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-13')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-21'))]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 474 and 1217.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 474.49) & (df['Approved_Amount'] <= 1217.22)]"
+  },
+  {
+    "Query": "Show claims where Loss Date between 2022-12-13 and 2022-12-29.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-13')) & (df['Loss_Date'] <= pd.to_datetime('2022-12-29'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Id greater than 278170.66.",
+    "pandas_code": "df[df['Claim_Id'] > 278170.66]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount equal to 1150.37.",
+    "pandas_code": "df[df['Approved_Amount'] == 1150.37]"
+  },
+  {
+    "Query": "List claims where Policy Number is 'BGDC221000032-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC221000032-01-000']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2023-06-06.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-06-06')]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2023-07-07.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-07')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2023-01-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-28')]"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 391 and 830.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 391.27) & (df['Approved_Amount'] <= 830.92)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-03-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-15')]"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2023-03-18 and 2023-04-22.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-18')) & (df['Loss_Date'] <= pd.to_datetime('2023-04-22'))]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-04-22.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-22')]"
+  },
+  {
+    "Query": "List claims where Status is 'Rejected'.",
+    "pandas_code": "df[df['Status'] == 'Rejected']"
+  },
+  {
+    "Query": "Fetch claims where Product is 'Hospital & Surgical'.",
+    "pandas_code": "df[df['Product'] == 'Hospital & Surgical']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2023-06-15.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-15')]"
+  },
+  {
+    "Query": "List claims where Claim Amount less than 224.11.",
+    "pandas_code": "df[df['Claim_Amount'] < 224.11]"
+  },
+  {
+    "Query": "List claims where Status Code contains 'PY3'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'PY3', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount at most 306.52.",
+    "pandas_code": "df[df['Claim_Amount'] <= 306.52]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 179 and 1413.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 179.35) & (df['Approved_Amount'] <= 1413.71)]"
+  },
+  {
+    "Query": "Provide claims where Product is 'Group Specialist'.",
+    "pandas_code": "df[df['Product'] == 'Group Specialist']"
+  },
+  {
+    "Query": "List claims where Loss Date after 2023-07-07.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-07')]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date on 2022-11-26.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-26')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-03-14 and 2023-05-30.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-14')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-30'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-02-21.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-02-21')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2023-05-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-16')]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 290 and 839.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 290.47) & (df['Approved_Amount'] <= 839.06)]"
+  },
+  {
+    "Query": "Fetch claims where Patient Name contains 'Ms Yu '.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Yu ', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2023-04-24.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-24')]"
+  },
+  {
+    "Query": "Display claims where Claim Amount equal to 563.71.",
+    "pandas_code": "df[df['Claim_Amount'] == 563.71]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2022-12-02.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-02')]"
+  },
+  {
+    "Query": "Show claims where Provider Code is 'PC101668'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101668']"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 625 and 917.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 625.53) & (df['Approved_Amount'] <= 917.09)]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-03-03.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-03')]"
+  },
+  {
+    "Query": "Show claims where Status Code contains 'PY3'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'PY3', na=False)]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2022-11-13.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-13')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date between 2022-12-04 and 2023-02-08.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-04')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-02-08'))]"
+  },
+  {
+    "Query": "Provide claims where Provider Code is 'PC104077'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104077']"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-05-31.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-31')]"
+  },
+  {
+    "Query": "Get claims where Provider Name contains 'SINGAP'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'SINGAP', na=False)]"
+  },
+  {
+    "Query": "List claims where Approved Amount equal to 1419.61.",
+    "pandas_code": "df[df['Approved_Amount'] == 1419.61]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-02-17.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-02-17')]"
+  },
+  {
+    "Query": "Find claims where Claim Amount less than 80.72.",
+    "pandas_code": "df[df['Claim_Amount'] < 80.72]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-06-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-12')]"
+  },
+  {
+    "Query": "Provide claims where Loss Type contains 'Dental'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Dental', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date on 2023-03-03.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-03')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2023-03-25.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-25')]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount at most 719.69.",
+    "pandas_code": "df[df['Approved_Amount'] <= 719.69]"
+  },
+  {
+    "Query": "Display claims where Claim Amount between 37 and 1340.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 37.6) & (df['Claim_Amount'] <= 1340.05)]"
+  },
+  {
+    "Query": "Retrieve claims where Scheme contains 'SME 1-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 1-', na=False)]"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2022-11-19 and 2023-05-06.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-19')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-06'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2023-02-03.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-02-03')]"
+  },
+  {
+    "Query": "List claims where Claim Amount at least 854.09.",
+    "pandas_code": "df[df['Claim_Amount'] >= 854.09]"
+  },
+  {
+    "Query": "Display claims where Deduction Amount less than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] < 0.0]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 303 and 1603.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 303.24) & (df['Claim_Amount'] <= 1603.65)]"
+  },
+  {
+    "Query": "Get claims where Claim Amount equal to 1327.42.",
+    "pandas_code": "df[df['Claim_Amount'] == 1327.42]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 162 and 1073.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 162.42) & (df['Approved_Amount'] <= 1073.83)]"
+  },
+  {
+    "Query": "Provide claims where Status contains 'Approv'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Approv', na=False)]"
+  },
+  {
+    "Query": "Display claims where Provider Name contains 'Guang '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Guang ', na=False)]"
+  },
+  {
+    "Query": "List claims where Claim Number is 'CBGDC23061325-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23061325-00']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-02-02 and 2023-07-05.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-02')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-05'))]"
+  },
+  {
+    "Query": "Show claims where Claim Id between 264456 and 276800.",
+    "pandas_code": "df[(df['Claim_Id'] >= 264456.55) & (df['Claim_Id'] <= 276800.08)]"
+  },
+  {
+    "Query": "Provide claims where Claim Id at least 269971.94.",
+    "pandas_code": "df[df['Claim_Id'] >= 269971.94]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-01-01 and 2023-04-29.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-01')) & (df['Loss_Date'] <= pd.to_datetime('2023-04-29'))]"
+  },
+  {
+    "Query": "Display claims where Scheme contains 'New Te'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'New Te', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount less than 1191.43.",
+    "pandas_code": "df[df['Approved_Amount'] < 1191.43]"
+  },
+  {
+    "Query": "List claims where Loss Date before 2023-02-22.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-22')]"
+  },
+  {
+    "Query": "List claims where Claim Id between 262059 and 271032.",
+    "pandas_code": "df[(df['Claim_Id'] >= 262059.63) & (df['Claim_Id'] <= 271032.02)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2023-06-21.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-06-21')]"
+  },
+  {
+    "Query": "Get claims where Provider Code is 'PC104074'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104074']"
+  },
+  {
+    "Query": "Find claims where Claim Created Date on 2023-04-08.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-04-08')]"
+  },
+  {
+    "Query": "Display claims where Loss Date on 2023-01-15.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-01-15')]"
+  },
+  {
+    "Query": "Display claims where Claim Id at most 268510.73.",
+    "pandas_code": "df[df['Claim_Id'] <= 268510.73]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount between 277 and 841.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 277.54) & (df['Claim_Amount'] <= 841.4)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2022-11-21.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-21')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Number is 'CBGDC23069430-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23069430-00']"
+  },
+  {
+    "Query": "Fetch claims where Claim Id equal to 269521.32.",
+    "pandas_code": "df[df['Claim_Id'] == 269521.32]"
+  },
+  {
+    "Query": "List claims where Status contains 'Paymen'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Paymen', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Status Code is 'DU0'.",
+    "pandas_code": "df[df['Status_Code'] == 'DU0']"
+  },
+  {
+    "Query": "Retrieve claims where Policy Number is 'BGDP221000151-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000151-01-000']"
+  },
+  {
+    "Query": "List claims where Claim Amount greater than 1600.2.",
+    "pandas_code": "df[df['Claim_Amount'] > 1600.2]"
+  },
+  {
+    "Query": "Show claims where Deduction Amount greater than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.0]"
+  },
+  {
+    "Query": "Find claims where Provider Name is 'Jurong Medical Centre'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Jurong Medical Centre']"
+  },
+  {
+    "Query": "Provide claims where Approved Amount less than 772.13.",
+    "pandas_code": "df[df['Approved_Amount'] < 772.13]"
+  },
+  {
+    "Query": "Get claims where Scheme is 'SME 1-1BPTE'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-1BPTE']"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-05-31.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-31')]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount at most 1228.38.",
+    "pandas_code": "df[df['Claim_Amount'] <= 1228.38]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-01-05.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-01-05')]"
+  },
+  {
+    "Query": "Display claims where Status contains 'Reject'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Reject', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2023-01-08.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-01-08')]"
+  },
+  {
+    "Query": "Fetch claims where Provider Code is 'PC101171'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101171']"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-05-09.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-09')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2023-02-09.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-09')]"
+  },
+  {
+    "Query": "Provide claims where Provider Code is 'PC103191'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103191']"
+  },
+  {
+    "Query": "Get claims where Patient Name is 'Mr Hwee Li Willy Chua'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Hwee Li Willy Chua']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-06-25 and 2023-07-11.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-06-25')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-11'))]"
+  },
+  {
+    "Query": "List claims where Claim Created Date before 2023-02-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-02-13')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-01-01.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-01-01')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 310 and 1262.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 310.22) & (df['Claim_Amount'] <= 1262.92)]"
+  },
+  {
+    "Query": "Display claims where Claim Number is 'CBGDC23061209-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23061209-00']"
+  },
+  {
+    "Query": "List claims where Approved Amount between 676 and 1379.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 676.61) & (df['Approved_Amount'] <= 1379.99)]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-03-29.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-29')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-04-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-15')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2022-12-17.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-12-17')]"
+  },
+  {
+    "Query": "Get claims where Provider Name is 'Other Physiotherapy Clinic'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Other Physiotherapy Clinic']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-03-10.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-10')]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name is 'DENTAL FOCUS JUNCTION 10 CLINIC'.",
+    "pandas_code": "df[df['Provider_Name'] == 'DENTAL FOCUS JUNCTION 10 CLINIC']"
+  },
+  {
+    "Query": "List claims where Patient Name is 'Mr Becegato De Carvalho Murilo'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Becegato De Carvalho Murilo']"
+  },
+  {
+    "Query": "Find claims where Claim Id at most 266511.48.",
+    "pandas_code": "df[df['Claim_Id'] <= 266511.48]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-02-16 and 2023-03-27.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-16')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-27'))]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date on 2022-12-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-12-28')]"
+  },
+  {
+    "Query": "Display claims where Loss Type contains 'Dental'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Dental', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2023-04-05.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-05')]"
+  },
+  {
+    "Query": "Fetch claims where Provider Code is 'PC102256'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC102256']"
+  },
+  {
+    "Query": "Provide claims where Claim Amount greater than 1553.65.",
+    "pandas_code": "df[df['Claim_Amount'] > 1553.65]"
+  },
+  {
+    "Query": "Show claims where Scheme contains 'SME 1-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 1-', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 265180 and 274298.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265180.29) & (df['Claim_Id'] <= 274298.15)]"
+  },
+  {
+    "Query": "Display claims where Claim Id between 267444 and 273889.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267444.39) & (df['Claim_Id'] <= 273889.81)]"
+  },
+  {
+    "Query": "Show claims where Patient Name is 'Ms Yu Liu'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Yu Liu']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2023-07-22.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-07-22')]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-01-28.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-01-28')]"
+  },
+  {
+    "Query": "Show claims where Loss Type is 'Specialist Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Specialist Treatment']"
+  },
+  {
+    "Query": "List claims where Approved Amount greater than 698.46.",
+    "pandas_code": "df[df['Approved_Amount'] > 698.46]"
+  },
+  {
+    "Query": "Show claims where Approved Amount at least 1358.7.",
+    "pandas_code": "df[df['Approved_Amount'] >= 1358.7]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date before 2023-03-03.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-03')]"
+  },
+  {
+    "Query": "Provide claims where Status contains 'Duplic'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Duplic', na=False)]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-04-21.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-21')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 267480 and 276076.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267480.97) & (df['Claim_Id'] <= 276076.38)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 270307 and 275270.",
+    "pandas_code": "df[(df['Claim_Id'] >= 270307.48) & (df['Claim_Id'] <= 275270.17)]"
+  },
+  {
+    "Query": "Show claims where Claim Id between 269740 and 277642.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269740.21) & (df['Claim_Id'] <= 277642.51)]"
+  },
+  {
+    "Query": "Display claims where Deduction Amount greater than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.0]"
+  },
+  {
+    "Query": "List claims where Deduction Amount greater than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.0]"
+  },
+  {
+    "Query": "List claims where Deduction Amount equal to 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] == 0.0]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date on 2023-01-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-12')]"
+  },
+  {
+    "Query": "Show claims where Provider Code is 'PC103185'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103185']"
+  },
+  {
+    "Query": "Get claims where Claim Id equal to 261896.82.",
+    "pandas_code": "df[df['Claim_Id'] == 261896.82]"
+  },
+  {
+    "Query": "Show claims where Claim Number is 'CBGDC23062532-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23062532-00']"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-07-27.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-07-27')]"
+  },
+  {
+    "Query": "Provide claims where Provider Name contains 'Bukit '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Bukit ', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2023-03-08.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-08')]"
+  },
+  {
+    "Query": "List claims where Claim Number is 'CBGDC23062531-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23062531-00']"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-01-21 and 2023-06-25.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-21')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-25'))]"
+  },
+  {
+    "Query": "Fetch claims where Status Code is 'PY1'.",
+    "pandas_code": "df[df['Status_Code'] == 'PY1']"
+  },
+  {
+    "Query": "Provide claims where Claim Id less than 279622.59.",
+    "pandas_code": "df[df['Claim_Id'] < 279622.59]"
+  },
+  {
+    "Query": "Display claims where Provider Name is 'Fullerton Healthcare'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Fullerton Healthcare']"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2022-12-24.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-24')]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-02-26.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-02-26')]"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2023-06-11.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-11')]"
+  },
+  {
+    "Query": "Display claims where Policy Number is 'BGDP221001216-00-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221001216-00-000']"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-03-06.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-06')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2022-11-10.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-10')]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 640 and 1031.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 640.11) & (df['Claim_Amount'] <= 1031.41)]"
+  },
+  {
+    "Query": "Find claims where Approved Amount less than 1101.1.",
+    "pandas_code": "df[df['Approved_Amount'] < 1101.1]"
+  },
+  {
+    "Query": "Provide claims where Claim Number contains 'CBGDP2'.",
+    "pandas_code": "df[df['Claim_Number'].str.contains(r'CBGDP2', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2022-12-18.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-18')]"
+  },
+  {
+    "Query": "List claims where Claim Type contains 'Cashle'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Cashle', na=False)]"
+  },
+  {
+    "Query": "Find claims where Claim Number is 'CBGDP23038445-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23038445-00']"
+  },
+  {
+    "Query": "Provide claims where Loss Date after 2023-01-07.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-01-07')]"
+  },
+  {
+    "Query": "Provide claims where Loss Type is 'Specialist Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Specialist Treatment']"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2022-11-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-16')]"
+  },
+  {
+    "Query": "Provide claims where Status Code contains 'PY1'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'PY1', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Id between 267889 and 273666.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267889.64) & (df['Claim_Id'] <= 273666.4)]"
+  },
+  {
+    "Query": "Show claims where Status Code contains 'PY1'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'PY1', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount less than 522.12.",
+    "pandas_code": "df[df['Approved_Amount'] < 522.12]"
+  },
+  {
+    "Query": "Fetch claims where Scheme is 'SME 2-500-NonPanel'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 2-500-NonPanel']"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-07-21.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-21')]"
+  },
+  {
+    "Query": "List claims where Loss Date after 2022-11-28.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-28')]"
+  },
+  {
+    "Query": "Display claims where Provider Name contains 'CASA D'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'CASA D', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-04-21.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-21')]"
+  },
+  {
+    "Query": "Display claims where Status Code is 'AP2'.",
+    "pandas_code": "df[df['Status_Code'] == 'AP2']"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2023-05-02.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-02')]"
+  },
+  {
+    "Query": "Display claims where Claim Id equal to 264861.43.",
+    "pandas_code": "df[df['Claim_Id'] == 264861.43]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date before 2023-07-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-28')]"
+  },
+  {
+    "Query": "Show claims where Loss Date before 2023-05-31.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-31')]"
+  },
+  {
+    "Query": "Show claims where Policy Number is 'BGDP221000369-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000369-01-000']"
+  },
+  {
+    "Query": "Display claims where Provider Code contains 'PC1007'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1007', na=False)]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2023-06-16 and 2023-06-26.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-06-16')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-26'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Id at most 262389.95.",
+    "pandas_code": "df[df['Claim_Id'] <= 262389.95]"
+  },
+  {
+    "Query": "Find claims where Approved Amount at least 731.69.",
+    "pandas_code": "df[df['Approved_Amount'] >= 731.69]"
+  },
+  {
+    "Query": "Show claims where Status Code contains 'RJ2'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'RJ2', na=False)]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-02-18.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-02-18')]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-01-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-01-15')]"
+  },
+  {
+    "Query": "Retrieve claims where Scheme is 'Template 2'.",
+    "pandas_code": "df[df['Scheme'] == 'Template 2']"
+  },
+  {
+    "Query": "Display claims where Loss Date on 2023-05-20.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-20')]"
+  },
+  {
+    "Query": "Provide claims where Provider Code contains 'OTH000'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'OTH000', na=False)]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 586 and 1123.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 586.29) & (df['Approved_Amount'] <= 1123.95)]"
+  },
+  {
+    "Query": "Find claims where Policy Number is 'BGDC221000041-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC221000041-01-000']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2023-04-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-12')]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2022-11-29 and 2023-04-26.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-29')) & (df['Loss_Date'] <= pd.to_datetime('2023-04-26'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 160 and 1121.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 160.61) & (df['Claim_Amount'] <= 1121.37)]"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2023-02-24 and 2023-02-25.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-24')) & (df['Loss_Date'] <= pd.to_datetime('2023-02-25'))]"
+  },
+  {
+    "Query": "Display claims where Claim Id between 269023 and 272084.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269023.18) & (df['Claim_Id'] <= 272084.66)]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2023-07-10 and 2023-07-27.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-07-10')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-27'))]"
+  },
+  {
+    "Query": "List claims where Scheme is 'SME 1-1000-NonPanel'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-1000-NonPanel']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount equal to 1438.05.",
+    "pandas_code": "df[df['Claim_Amount'] == 1438.05]"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2023-05-30.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-30')]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 588 and 946.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 588.09) & (df['Approved_Amount'] <= 946.05)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2022-11-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-11-24')]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2022-11-08 and 2023-06-07.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-08')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-07'))]"
+  },
+  {
+    "Query": "Get claims where Product contains 'Specia'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Specia', na=False)]"
+  },
+  {
+    "Query": "Find claims where Approved Amount greater than 1030.55.",
+    "pandas_code": "df[df['Approved_Amount'] > 1030.55]"
+  },
+  {
+    "Query": "Fetch claims where Status Code is 'RJ2'.",
+    "pandas_code": "df[df['Status_Code'] == 'RJ2']"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2022-12-21.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-21')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2022-11-14.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-11-14')]"
+  },
+  {
+    "Query": "Show claims where Scheme is 'Template 1'.",
+    "pandas_code": "df[df['Scheme'] == 'Template 1']"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2023-06-26.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-06-26')]"
+  },
+  {
+    "Query": "List claims where Status Code is 'DU0'.",
+    "pandas_code": "df[df['Status_Code'] == 'DU0']"
+  },
+  {
+    "Query": "Fetch claims where Status contains 'Approv'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Approv', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Claim Number is 'CBGDC23070332-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23070332-00']"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-03-22.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-22')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-02-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-24')]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2022-12-28.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-28')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2023-01-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-28')]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2022-12-07 and 2023-07-12.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-07')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-12'))]"
+  },
+  {
+    "Query": "Retrieve claims where Product is 'Group OP General Practitioner'.",
+    "pandas_code": "df[df['Product'] == 'Group OP General Practitioner']"
+  },
+  {
+    "Query": "Get claims where Provider Name contains 'Guang '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Guang ', na=False)]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2023-02-16.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-16')]"
+  },
+  {
+    "Query": "Show claims where Provider Code is 'PC101195'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101195']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-01-01.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-01')]"
+  },
+  {
+    "Query": "Show claims where Loss Type contains 'GP Tre'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'GP Tre', na=False)]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2022-11-10.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-10')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date before 2023-05-21.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-21')]"
+  },
+  {
+    "Query": "List claims where Loss Date before 2023-05-08.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-08')]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-05-08.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-08')]"
+  },
+  {
+    "Query": "List claims where Claim Id between 264650 and 273026.",
+    "pandas_code": "df[(df['Claim_Id'] >= 264650.99) & (df['Claim_Id'] <= 273026.88)]"
+  },
+  {
+    "Query": "Get claims where Approved Amount at most 1023.66.",
+    "pandas_code": "df[df['Approved_Amount'] <= 1023.66]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2022-12-22.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-12-22')]"
+  },
+  {
+    "Query": "List claims where Loss Date after 2022-11-29.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-29')]"
+  },
+  {
+    "Query": "Show claims where Loss Type is 'GP Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'GP Treatment']"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2022-11-21.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-21')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-03-20.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-20')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date between 2023-01-23 and 2023-02-06.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-23')) & (df['Loss_Date'] <= pd.to_datetime('2023-02-06'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 105 and 982.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 105.84) & (df['Claim_Amount'] <= 982.31)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2023-02-11 and 2023-03-05.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-11')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-05'))]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-05-29.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-29')]"
+  },
+  {
+    "Query": "List claims where Approved Amount greater than 1323.91.",
+    "pandas_code": "df[df['Approved_Amount'] > 1323.91]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2023-01-11.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-11')]"
+  },
+  {
+    "Query": "Display claims where Approved Amount greater than 524.07.",
+    "pandas_code": "df[df['Approved_Amount'] > 524.07]"
+  },
+  {
+    "Query": "Retrieve claims where Status is 'Duplicate'.",
+    "pandas_code": "df[df['Status'] == 'Duplicate']"
+  },
+  {
+    "Query": "Find claims where Claim Amount equal to 1533.64.",
+    "pandas_code": "df[df['Claim_Amount'] == 1533.64]"
+  },
+  {
+    "Query": "Get claims where Policy Number is 'BGDP221000724-00-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000724-00-000']"
+  },
+  {
+    "Query": "Show claims where Deduction Amount at least 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] >= 0.0]"
+  },
+  {
+    "Query": "Fetch claims where Patient Name is 'Mr Chye Jin Jonathan Soh'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Chye Jin Jonathan Soh']"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2022-11-09 and 2023-06-23.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-09')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-23'))]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-06-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-06-25')]"
+  },
+  {
+    "Query": "Display claims where Claim Amount between 708 and 1200.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 708.07) & (df['Claim_Amount'] <= 1200.44)]"
+  },
+  {
+    "Query": "Display claims where Claim Id between 268831 and 272767.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268831.82) & (df['Claim_Id'] <= 272767.41)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date between 2022-11-09 and 2023-03-23.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-09')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-23'))]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount greater than 857.71.",
+    "pandas_code": "df[df['Approved_Amount'] > 857.71]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-02-02.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-02')]"
+  },
+  {
+    "Query": "List claims where Claim Id between 262760 and 272075.",
+    "pandas_code": "df[(df['Claim_Id'] >= 262760.43) & (df['Claim_Id'] <= 272075.65)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2023-01-09 and 2023-04-29.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-09')) & (df['Loss_Date'] <= pd.to_datetime('2023-04-29'))]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-07-28.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-07-28')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2023-04-03.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-03')]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount greater than 749.19.",
+    "pandas_code": "df[df['Approved_Amount'] > 749.19]"
+  },
+  {
+    "Query": "Display claims where Scheme is 'New Template 2'.",
+    "pandas_code": "df[df['Scheme'] == 'New Template 2']"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-02-14 and 2023-05-09.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-14')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-09'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-07-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-13')]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-01-02.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-01-02')]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name is 'SINGAPORE NATIONAL EYE CENTRE'.",
+    "pandas_code": "df[df['Provider_Name'] == 'SINGAPORE NATIONAL EYE CENTRE']"
+  },
+  {
+    "Query": "Find claims where Patient Name is 'Ms Wei Ying Angeline Ng'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Wei Ying Angeline Ng']"
+  },
+  {
+    "Query": "Get claims where Status contains 'Duplic'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Duplic', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 816 and 990.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 816.34) & (df['Claim_Amount'] <= 990.0)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-06-30.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-30')]"
+  },
+  {
+    "Query": "Find claims where Patient Name is 'Ms Xin Puong (Wu Xinpeng) Goh'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Xin Puong (Wu Xinpeng) Goh']"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2022-12-16.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-16')]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2023-05-11.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-11')]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Code is 'PC102889'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC102889']"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-02-25.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-02-25')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2023-04-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-23')]"
+  },
+  {
+    "Query": "List claims where Provider Name is 'Other Physiotherapy Clinic'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Other Physiotherapy Clinic']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-01-10.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-01-10')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-07-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-16')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2023-04-22 and 2023-07-14.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-04-22')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-14'))]"
+  },
+  {
+    "Query": "Find claims where Claim Amount at most 648.14.",
+    "pandas_code": "df[df['Claim_Amount'] <= 648.14]"
+  },
+  {
+    "Query": "Show claims where Claim Id equal to 265344.54.",
+    "pandas_code": "df[df['Claim_Id'] == 265344.54]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id at most 263954.92.",
+    "pandas_code": "df[df['Claim_Id'] <= 263954.92]"
+  },
+  {
+    "Query": "List claims where Claim Created Date before 2022-11-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-11-25')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-05-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-23')]"
+  },
+  {
+    "Query": "List claims where Approved Amount at least 1082.3.",
+    "pandas_code": "df[df['Approved_Amount'] >= 1082.3]"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2023-05-01 and 2023-07-13.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-05-01')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-13'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-07-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-16')]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 100 and 1242.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 100.14) & (df['Approved_Amount'] <= 1242.33)]"
+  },
+  {
+    "Query": "Fetch claims where Provider Code contains 'PC1023'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1023', na=False)]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 20 and 1276.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 20.95) & (df['Approved_Amount'] <= 1276.26)]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-04-19.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-19')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2023-02-18 and 2023-06-01.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-18')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-01'))]"
+  },
+  {
+    "Query": "Get claims where Claim Type contains 'Cashle'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Cashle', na=False)]"
+  },
+  {
+    "Query": "Display claims where Status Code is 'DU0'.",
+    "pandas_code": "df[df['Status_Code'] == 'DU0']"
+  },
+  {
+    "Query": "Provide claims where Patient Name is 'Mr Simon Jeremy Eric Dupin'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Simon Jeremy Eric Dupin']"
+  },
+  {
+    "Query": "Find claims where Status Code is 'AP2'.",
+    "pandas_code": "df[df['Status_Code'] == 'AP2']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2023-01-23 and 2023-05-18.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-23')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-18'))]"
+  },
+  {
+    "Query": "Show claims where Approved Amount at least 32.42.",
+    "pandas_code": "df[df['Approved_Amount'] >= 32.42]"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2023-04-03.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-03')]"
+  },
+  {
+    "Query": "List claims where Provider Name contains 'Other '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Other ', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-07-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-24')]"
+  },
+  {
+    "Query": "List claims where Loss Date before 2023-05-03.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-03')]"
+  },
+  {
+    "Query": "List claims where Provider Name is 'Casa Dental'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Casa Dental']"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 217 and 1568.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 217.92) & (df['Claim_Amount'] <= 1568.92)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2023-02-17.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-17')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2023-03-17 and 2023-07-11.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-17')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-11'))]"
+  },
+  {
+    "Query": "Display claims where Claim Number contains 'CBGDP2'.",
+    "pandas_code": "df[df['Claim_Number'].str.contains(r'CBGDP2', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Scheme contains 'SME 3-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 3-', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Type contains 'Specia'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Specia', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date before 2023-07-21.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-21')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2022-12-18.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-18')]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount between 54 and 883.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 54.06) & (df['Claim_Amount'] <= 883.94)]"
+  },
+  {
+    "Query": "List claims where Claim Id at most 261708.28.",
+    "pandas_code": "df[df['Claim_Id'] <= 261708.28]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date before 2023-01-11.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-01-11')]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 491 and 1019.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 491.39) & (df['Approved_Amount'] <= 1019.78)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2023-04-17.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-17')]"
+  },
+  {
+    "Query": "Find claims where Status contains 'Approv'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Approv', na=False)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-05-07 and 2023-07-08.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-05-07')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-08'))]"
+  },
+  {
+    "Query": "List claims where Provider Code is 'PC102812'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC102812']"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2022-12-30 and 2023-06-10.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-30')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-10'))]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-02-04.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-02-04')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2023-01-12.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-01-12')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Type contains 'Dental'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Dental', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Number is 'CBGDC23060449-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23060449-00']"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2023-06-21.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-06-21')]"
+  },
+  {
+    "Query": "Show claims where Policy Number contains 'BGDC21'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDC21', na=False)]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date on 2023-05-08.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-08')]"
+  },
+  {
+    "Query": "Show claims where Approved Amount greater than 657.67.",
+    "pandas_code": "df[df['Approved_Amount'] > 657.67]"
+  },
+  {
+    "Query": "Get claims where Scheme is 'New Template 3 '.",
+    "pandas_code": "df[df['Scheme'] == 'New Template 3 ']"
+  },
+  {
+    "Query": "Show claims where Claim Id at least 263999.09.",
+    "pandas_code": "df[df['Claim_Id'] >= 263999.09]"
+  },
+  {
+    "Query": "Fetch claims where Policy Number is 'BGDC211000437-02-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000437-02-000']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-07-22.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-22')]"
+  },
+  {
+    "Query": "List claims where Claim Id less than 276493.35.",
+    "pandas_code": "df[df['Claim_Id'] < 276493.35]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-02-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-13')]"
+  },
+  {
+    "Query": "Get claims where Claim Id equal to 273478.03.",
+    "pandas_code": "df[df['Claim_Id'] == 273478.03]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date on 2023-03-29.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-29')]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount between 23 and 1530.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 23.32) & (df['Approved_Amount'] <= 1530.45)]"
+  },
+  {
+    "Query": "List claims where Loss Date after 2023-02-19.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-02-19')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2023-05-04.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-04')]"
+  },
+  {
+    "Query": "Display claims where Claim Amount between 812 and 1494.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 812.96) & (df['Claim_Amount'] <= 1494.81)]"
+  },
+  {
+    "Query": "Provide claims where Product is 'Group Dental Benefit'.",
+    "pandas_code": "df[df['Product'] == 'Group Dental Benefit']"
+  },
+  {
+    "Query": "Show claims where Provider Code is 'PC104074'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104074']"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-05-04.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-04')]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount less than 877.16.",
+    "pandas_code": "df[df['Approved_Amount'] < 877.16]"
+  },
+  {
+    "Query": "Display claims where Claim Id between 267568 and 279554.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267568.21) & (df['Claim_Id'] <= 279554.67)]"
+  },
+  {
+    "Query": "Provide claims where Claim Id between 265855 and 276495.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265855.4) & (df['Claim_Id'] <= 276495.22)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-02-22 and 2023-06-15.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-22')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-15'))]"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2023-07-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-25')]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 38 and 874.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 38.08) & (df['Approved_Amount'] <= 874.19)]"
+  },
+  {
+    "Query": "List claims where Claim Amount between 25 and 1612.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 25.44) & (df['Claim_Amount'] <= 1612.76)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 567 and 937.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 567.63) & (df['Claim_Amount'] <= 937.75)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 266328 and 275174.",
+    "pandas_code": "df[(df['Claim_Id'] >= 266328.25) & (df['Claim_Id'] <= 275174.91)]"
+  },
+  {
+    "Query": "List claims where Loss Type contains 'Specia'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Specia', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2023-04-18.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-18')]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-04-20 and 2023-07-25.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-04-20')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-25'))]"
+  },
+  {
+    "Query": "Provide claims where Scheme contains 'SME 2-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 2-', na=False)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-05-09 and 2023-07-18.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-05-09')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-18'))]"
+  },
+  {
+    "Query": "Get claims where Patient Name is 'Mr Wai Cheng Wong'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Wai Cheng Wong']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 269327 and 278051.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269327.93) & (df['Claim_Id'] <= 278051.66)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 288 and 1398.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 288.13) & (df['Claim_Amount'] <= 1398.15)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-02-02.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-02-02')]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-07-31.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-07-31')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2023-04-21.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-21')]"
+  },
+  {
+    "Query": "Provide claims where Claim Number is 'CBGDC23061958-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23061958-00']"
+  },
+  {
+    "Query": "Get claims where Status is 'Paid'.",
+    "pandas_code": "df[df['Status'] == 'Paid']"
+  },
+  {
+    "Query": "Fetch claims where Claim Number is 'CBGDC23070399-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23070399-00']"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 311 and 1293.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 311.72) & (df['Claim_Amount'] <= 1293.37)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id at most 272031.4.",
+    "pandas_code": "df[df['Claim_Id'] <= 272031.4]"
+  },
+  {
+    "Query": "List claims where Loss Date after 2023-01-27.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-01-27')]"
+  },
+  {
+    "Query": "Get claims where Claim Id less than 269391.94.",
+    "pandas_code": "df[df['Claim_Id'] < 269391.94]"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2023-07-07.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-07')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date on 2023-02-18.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-02-18')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2022-11-27 and 2023-05-08.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-27')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-08'))]"
+  },
+  {
+    "Query": "Show claims where Claim Amount equal to 75.48.",
+    "pandas_code": "df[df['Claim_Amount'] == 75.48]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 467 and 853.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 467.48) & (df['Approved_Amount'] <= 853.46)]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-04-20 and 2023-06-15.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-04-20')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-15'))]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-02-14 and 2023-06-24.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-14')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-24'))]"
+  },
+  {
+    "Query": "Display claims where Claim Amount between 516 and 1028.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 516.02) & (df['Claim_Amount'] <= 1028.78)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2023-04-07.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-04-07')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2022-11-08.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-11-08')]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount between 439 and 1581.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 439.21) & (df['Claim_Amount'] <= 1581.22)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 265 and 1625.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 265.47) & (df['Claim_Amount'] <= 1625.62)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2023-04-09.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-09')]"
+  },
+  {
+    "Query": "Show claims where Claim Amount equal to 856.34.",
+    "pandas_code": "df[df['Claim_Amount'] == 856.34]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount less than 1213.5.",
+    "pandas_code": "df[df['Claim_Amount'] < 1213.5]"
+  },
+  {
+    "Query": "Retrieve claims where Scheme contains 'SME 2-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 2-', na=False)]"
+  },
+  {
+    "Query": "List claims where Status Code contains 'AP2'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'AP2', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date before 2022-12-18.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-12-18')]"
+  },
+  {
+    "Query": "Get claims where Provider Code is 'PC104076'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104076']"
+  },
+  {
+    "Query": "Provide claims where Claim Id at least 270403.05.",
+    "pandas_code": "df[df['Claim_Id'] >= 270403.05]"
+  },
+  {
+    "Query": "Fetch claims where Status is 'Approved'.",
+    "pandas_code": "df[df['Status'] == 'Approved']"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2023-03-17.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-17')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Type is 'GP Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'GP Treatment']"
+  },
+  {
+    "Query": "List claims where Loss Date after 2023-04-19.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-19')]"
+  },
+  {
+    "Query": "Provide claims where Claim Id equal to 268908.53.",
+    "pandas_code": "df[df['Claim_Id'] == 268908.53]"
+  },
+  {
+    "Query": "Fetch claims where Claim Number is 'CBGDC23061488-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23061488-00']"
+  },
+  {
+    "Query": "Get claims where Claim Amount less than 458.0.",
+    "pandas_code": "df[df['Claim_Amount'] < 458.0]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount equal to 1302.58.",
+    "pandas_code": "df[df['Approved_Amount'] == 1302.58]"
+  },
+  {
+    "Query": "Fetch claims where Provider Name is 'Tooth Matters Dental Surgery'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Tooth Matters Dental Surgery']"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-02-23.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-02-23')]"
+  },
+  {
+    "Query": "List claims where Loss Date after 2023-07-16.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-16')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2022-12-08.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-12-08')]"
+  },
+  {
+    "Query": "Find claims where Patient Name is 'Ms Yi Yun Joyce Chua'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Yi Yun Joyce Chua']"
+  },
+  {
+    "Query": "Get claims where Claim Number is 'CBGDP23037834-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23037834-00']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 690 and 1345.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 690.69) & (df['Claim_Amount'] <= 1345.58)]"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name is 'Mr Fahim Dowla'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Fahim Dowla']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date on 2023-02-02.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-02-02')]"
+  },
+  {
+    "Query": "Get claims where Claim Id between 269111 and 270984.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269111.44) & (df['Claim_Id'] <= 270984.61)]"
+  },
+  {
+    "Query": "Display claims where Claim Id between 262388 and 272354.",
+    "pandas_code": "df[(df['Claim_Id'] >= 262388.62) & (df['Claim_Id'] <= 272354.99)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date on 2022-11-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-15')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2022-12-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-19')]"
+  },
+  {
+    "Query": "Display claims where Provider Code contains 'PC1004'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1004', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Amount between 546 and 988.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 546.06) & (df['Claim_Amount'] <= 988.39)]"
+  },
+  {
+    "Query": "Show claims where Claim Id between 269406 and 275201.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269406.22) & (df['Claim_Id'] <= 275201.96)]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-01-02 and 2023-05-14.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-02')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-14'))]"
+  },
+  {
+    "Query": "Provide claims where Loss Type is 'Hospitalisation'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Hospitalisation']"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2023-02-03.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-02-03')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2023-07-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-27')]"
+  },
+  {
+    "Query": "List claims where Claim Amount at most 407.36.",
+    "pandas_code": "df[df['Claim_Amount'] <= 407.36]"
+  },
+  {
+    "Query": "Provide claims where Claim Id equal to 277321.68.",
+    "pandas_code": "df[df['Claim_Id'] == 277321.68]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2023-07-06.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-06')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date after 2022-11-29.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-29')]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-04-21.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-21')]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2022-12-19 and 2023-06-18.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-19')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-18'))]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2023-05-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-24')]"
+  },
+  {
+    "Query": "Display claims where Claim Id greater than 271686.53.",
+    "pandas_code": "df[df['Claim_Id'] > 271686.53]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount less than 595.78.",
+    "pandas_code": "df[df['Claim_Amount'] < 595.78]"
+  },
+  {
+    "Query": "Find claims where Claim Amount at most 1145.24.",
+    "pandas_code": "df[df['Claim_Amount'] <= 1145.24]"
+  },
+  {
+    "Query": "Display claims where Loss Type is 'GP Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'GP Treatment']"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 35 and 1399.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 35.94) & (df['Claim_Amount'] <= 1399.84)]"
+  },
+  {
+    "Query": "Display claims where Claim Amount less than 866.16.",
+    "pandas_code": "df[df['Claim_Amount'] < 866.16]"
+  },
+  {
+    "Query": "Show claims where Provider Code is 'PC101350'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date before 2022-11-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-11-16')]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-04-26 and 2023-06-20.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-04-26')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-20'))]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2023-04-15.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-15')]"
+  },
+  {
+    "Query": "Provide claims where Status contains 'Void'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Void', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2023-04-18.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-18')]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-05-02.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-02')]"
+  },
+  {
+    "Query": "Get claims where Status contains 'Reject'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Reject', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2023-04-30 and 2023-07-29.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-04-30')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-29'))]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2023-01-16 and 2023-05-06.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-16')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-06'))]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2023-02-06 and 2023-04-25.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-06')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-04-25'))]"
+  },
+  {
+    "Query": "List claims where Claim Id between 268646 and 275391.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268646.61) & (df['Claim_Id'] <= 275391.0)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2022-12-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-16')]"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2022-11-17.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-17')]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 22 and 1506.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 22.41) & (df['Approved_Amount'] <= 1506.79)]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount between 191 and 1103.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 191.58) & (df['Claim_Amount'] <= 1103.42)]"
+  },
+  {
+    "Query": "List claims where Approved Amount at most 923.81.",
+    "pandas_code": "df[df['Approved_Amount'] <= 923.81]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 713 and 1228.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 713.6) & (df['Approved_Amount'] <= 1228.3)]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2022-12-22 and 2023-02-06.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-22')) & (df['Loss_Date'] <= pd.to_datetime('2023-02-06'))]"
+  },
+  {
+    "Query": "Retrieve claims where Status Code is 'DU0'.",
+    "pandas_code": "df[df['Status_Code'] == 'DU0']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2023-02-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-02-25')]"
+  },
+  {
+    "Query": "List claims where Product is 'Group Hospital and Surgical'.",
+    "pandas_code": "df[df['Product'] == 'Group Hospital and Surgical']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount equal to 97.01.",
+    "pandas_code": "df[df['Claim_Amount'] == 97.01]"
+  },
+  {
+    "Query": "List claims where Loss Type contains 'GP Tre'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'GP Tre', na=False)]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2022-11-20 and 2022-12-31.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-20')) & (df['Claim_Created_Date'] <= pd.to_datetime('2022-12-31'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2022-11-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-11-25')]"
+  },
+  {
+    "Query": "Find claims where Approved Amount at most 1127.24.",
+    "pandas_code": "df[df['Approved_Amount'] <= 1127.24]"
+  },
+  {
+    "Query": "List claims where Claim Id equal to 274386.77.",
+    "pandas_code": "df[df['Claim_Id'] == 274386.77]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2022-11-28 and 2023-06-30.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-28')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-30'))]"
+  },
+  {
+    "Query": "Get claims where Status is 'Rejected'.",
+    "pandas_code": "df[df['Status'] == 'Rejected']"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-04-16.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-16')]"
+  },
+  {
+    "Query": "Display claims where Claim Number is 'CBGDP23038619-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23038619-00']"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2022-12-04.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-04')]"
+  },
+  {
+    "Query": "Find claims where Claim Id between 263144 and 275855.",
+    "pandas_code": "df[(df['Claim_Id'] >= 263144.45) & (df['Claim_Id'] <= 275855.92)]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-05-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-13')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2022-11-29.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-29')]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 240 and 886.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 240.95) & (df['Claim_Amount'] <= 886.15)]"
+  },
+  {
+    "Query": "Show claims where Patient Name is 'Mr Wayne Chai'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Wayne Chai']"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 500 and 1546.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 500.87) & (df['Claim_Amount'] <= 1546.99)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2023-05-30.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-30')]"
+  },
+  {
+    "Query": "Display claims where Loss Type contains 'Specia'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Specia', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Id between 268958 and 275217.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268958.89) & (df['Claim_Id'] <= 275217.17)]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2023-05-14.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-14')]"
+  },
+  {
+    "Query": "List claims where Approved Amount equal to 320.33.",
+    "pandas_code": "df[df['Approved_Amount'] == 320.33]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2023-05-09.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-09')]"
+  },
+  {
+    "Query": "Get claims where Status is 'Approved'.",
+    "pandas_code": "df[df['Status'] == 'Approved']"
+  },
+  {
+    "Query": "Get claims where Claim Id between 264832 and 276201.",
+    "pandas_code": "df[(df['Claim_Id'] >= 264832.54) & (df['Claim_Id'] <= 276201.14)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-07-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-12')]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2023-03-27.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-27')]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-01-30.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-01-30')]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name contains 'Casa D'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Casa D', na=False)]"
+  },
+  {
+    "Query": "List claims where Provider Code is 'PC100408'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100408']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-07-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-04')]"
+  },
+  {
+    "Query": "Show claims where Loss Type is 'Dental'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Dental']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 265581 and 279663.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265581.25) & (df['Claim_Id'] <= 279663.91)]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 6 and 1042.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 6.08) & (df['Approved_Amount'] <= 1042.17)]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2022-11-28.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-28')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Number is 'CBGDP23038203-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23038203-00']"
+  },
+  {
+    "Query": "Provide claims where Loss Date between 2023-04-24 and 2023-06-14.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-04-24')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-14'))]"
+  },
+  {
+    "Query": "Fetch claims where Policy Number is 'BGDP221000151-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000151-01-000']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2023-04-06.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-06')]"
+  },
+  {
+    "Query": "Get claims where Loss Type is 'Specialist Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Specialist Treatment']"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 682 and 1432.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 682.07) & (df['Claim_Amount'] <= 1432.12)]"
+  },
+  {
+    "Query": "Provide claims where Loss Type contains 'Specia'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Specia', na=False)]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2023-05-07.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-07')]"
+  },
+  {
+    "Query": "Find claims where Patient Name is 'Soumya Unni'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Soumya Unni']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2023-07-10.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-10')]"
+  },
+  {
+    "Query": "Display claims where Loss Type contains 'Hospit'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Hospit', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Scheme is 'SME 1-1BPTE'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-1BPTE']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Type contains 'Hospit'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Hospit', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Type contains 'Specia'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Specia', na=False)]"
+  },
+  {
+    "Query": "Find claims where Claim Number is 'CBGDC23070459-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23070459-00']"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2022-12-31 and 2023-06-04.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-31')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-04'))]"
+  },
+  {
+    "Query": "Display claims where Scheme is 'SME 2-GP-CO'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 2-GP-CO']"
+  },
+  {
+    "Query": "List claims where Product contains 'Dental'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Dental', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 641 and 1385.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 641.68) & (df['Approved_Amount'] <= 1385.05)]"
+  },
+  {
+    "Query": "Display claims where Loss Type is 'Specialist Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Specialist Treatment']"
+  },
+  {
+    "Query": "Display claims where Claim Id equal to 275444.53.",
+    "pandas_code": "df[df['Claim_Id'] == 275444.53]"
+  },
+  {
+    "Query": "Show claims where Product is 'Group OP General Practitioner'.",
+    "pandas_code": "df[df['Product'] == 'Group OP General Practitioner']"
+  },
+  {
+    "Query": "Provide claims where Claim Amount greater than 313.93.",
+    "pandas_code": "df[df['Claim_Amount'] > 313.93]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-03-22.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-22')]"
+  },
+  {
+    "Query": "Display claims where Loss Type is 'Dental'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Dental']"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 683 and 812.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 683.32) & (df['Approved_Amount'] <= 812.21)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-02-26.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-02-26')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2023-02-03.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-03')]"
+  },
+  {
+    "Query": "Fetch claims where Provider Code is 'PC104074'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104074']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date between 2023-01-19 and 2023-01-27.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-19')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-01-27'))]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 687 and 1258.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 687.47) & (df['Approved_Amount'] <= 1258.85)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-02-02.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-02')]"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 96 and 1334.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 96.64) & (df['Approved_Amount'] <= 1334.47)]"
+  },
+  {
+    "Query": "Get claims where Claim Id less than 261590.61.",
+    "pandas_code": "df[df['Claim_Id'] < 261590.61]"
+  },
+  {
+    "Query": "Show claims where Loss Date before 2023-04-07.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-07')]"
+  },
+  {
+    "Query": "Retrieve claims where Product contains 'Group '.",
+    "pandas_code": "df[df['Product'].str.contains(r'Group ', na=False)]"
+  },
+  {
+    "Query": "Display claims where Approved Amount at least 703.11.",
+    "pandas_code": "df[df['Approved_Amount'] >= 703.11]"
+  },
+  {
+    "Query": "Display claims where Claim Amount greater than 36.24.",
+    "pandas_code": "df[df['Claim_Amount'] > 36.24]"
+  },
+  {
+    "Query": "Provide claims where Loss Date between 2023-02-21 and 2023-05-13.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-21')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-13'))]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-03-09.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-09')]"
+  },
+  {
+    "Query": "Display claims where Claim Amount between 36 and 1012.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 36.66) & (df['Claim_Amount'] <= 1012.08)]"
+  },
+  {
+    "Query": "Get claims where Claim Id greater than 262446.71.",
+    "pandas_code": "df[df['Claim_Id'] > 262446.71]"
+  },
+  {
+    "Query": "Get claims where Status is 'Payment Approval'.",
+    "pandas_code": "df[df['Status'] == 'Payment Approval']"
+  },
+  {
+    "Query": "Display claims where Claim Amount less than 42.47.",
+    "pandas_code": "df[df['Claim_Amount'] < 42.47]"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2023-04-08.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-08')]"
+  },
+  {
+    "Query": "Display claims where Provider Name contains 'Changi'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Changi', na=False)]"
+  },
+  {
+    "Query": "Find claims where Claim Amount equal to 675.32.",
+    "pandas_code": "df[df['Claim_Amount'] == 675.32]"
+  },
+  {
+    "Query": "Find claims where Claim Type is 'Reimbursement'.",
+    "pandas_code": "df[df['Claim_Type'] == 'Reimbursement']"
+  },
+  {
+    "Query": "Display claims where Policy Number is 'BGDP221000580-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000580-01-000']"
+  },
+  {
+    "Query": "Show claims where Loss Date before 2023-06-23.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-06-23')]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2023-03-16.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-16')]"
+  },
+  {
+    "Query": "List claims where Claim Amount between 532 and 1310.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 532.76) & (df['Claim_Amount'] <= 1310.54)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date on 2023-05-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-16')]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount less than 1224.32.",
+    "pandas_code": "df[df['Approved_Amount'] < 1224.32]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2023-01-25 and 2023-02-07.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-25')) & (df['Loss_Date'] <= pd.to_datetime('2023-02-07'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2023-03-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-19')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount less than 1359.39.",
+    "pandas_code": "df[df['Claim_Amount'] < 1359.39]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2022-12-08 and 2023-06-05.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-08')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-05'))]"
+  },
+  {
+    "Query": "Display claims where Loss Date on 2023-06-26.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-26')]"
+  },
+  {
+    "Query": "List claims where Approved Amount less than 988.52.",
+    "pandas_code": "df[df['Approved_Amount'] < 988.52]"
+  },
+  {
+    "Query": "Display claims where Patient Name is 'Ms Chor Hoon Ng'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Chor Hoon Ng']"
+  },
+  {
+    "Query": "Fetch claims where Provider Name is 'Others'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Others']"
+  },
+  {
+    "Query": "Show claims where Scheme is 'SME 2-500-NonPanel'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 2-500-NonPanel']"
+  },
+  {
+    "Query": "List claims where Loss Date after 2022-12-27.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-27')]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-05-13.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-13')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 222 and 928.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 222.06) & (df['Claim_Amount'] <= 928.9)]"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name is 'Mr Yuan Kuan Lawrence Law'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Yuan Kuan Lawrence Law']"
+  },
+  {
+    "Query": "Find claims where Claim Id less than 266944.9.",
+    "pandas_code": "df[df['Claim_Id'] < 266944.9]"
+  },
+  {
+    "Query": "Display claims where Loss Date on 2023-02-17.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-02-17')]"
+  },
+  {
+    "Query": "Show claims where Loss Date between 2022-11-08 and 2023-05-10.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-08')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-10'))]"
+  },
+  {
+    "Query": "List claims where Claim Created Date before 2023-04-09.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-09')]"
+  },
+  {
+    "Query": "Show claims where Claim Id between 269836 and 276894.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269836.7) & (df['Claim_Id'] <= 276894.27)]"
+  },
+  {
+    "Query": "Show claims where Product is 'Group Hospital and Surgical'.",
+    "pandas_code": "df[df['Product'] == 'Group Hospital and Surgical']"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2023-07-06.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-07-06')]"
+  },
+  {
+    "Query": "List claims where Loss Date after 2023-03-06.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-06')]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-04-19 and 2023-07-13.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-04-19')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-13'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-01-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-15')]"
+  },
+  {
+    "Query": "Fetch claims where Provider Name is 'Parkway East Hospital'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Parkway East Hospital']"
+  },
+  {
+    "Query": "List claims where Claim Number is 'CBGDC23059477-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23059477-00']"
+  },
+  {
+    "Query": "Show claims where Approved Amount at least 1214.16.",
+    "pandas_code": "df[df['Approved_Amount'] >= 1214.16]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id equal to 261629.41.",
+    "pandas_code": "df[df['Claim_Id'] == 261629.41]"
+  },
+  {
+    "Query": "Display claims where Status is 'Approved'.",
+    "pandas_code": "df[df['Status'] == 'Approved']"
+  },
+  {
+    "Query": "Provide claims where Status contains 'Reject'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Reject', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount greater than 1208.38.",
+    "pandas_code": "df[df['Approved_Amount'] > 1208.38]"
+  },
+  {
+    "Query": "Display claims where Claim Id equal to 276681.82.",
+    "pandas_code": "df[df['Claim_Id'] == 276681.82]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2023-03-22 and 2023-07-05.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-22')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-05'))]"
+  },
+  {
+    "Query": "Retrieve claims where Scheme is 'SME 3-SP-CO-Panel Only'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 3-SP-CO-Panel Only']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-05-07.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-07')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 269601 and 270756.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269601.15) & (df['Claim_Id'] <= 270756.02)]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount less than 1257.12.",
+    "pandas_code": "df[df['Approved_Amount'] < 1257.12]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date on 2022-12-31.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-12-31')]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount at least 825.22.",
+    "pandas_code": "df[df['Approved_Amount'] >= 825.22]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 755 and 1613.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 755.92) & (df['Claim_Amount'] <= 1613.94)]"
+  },
+  {
+    "Query": "Provide claims where Patient Name contains 'Ms Si '.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Si ', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Number is 'CBGDC23061891-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23061891-00']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount at least 365.03.",
+    "pandas_code": "df[df['Claim_Amount'] >= 365.03]"
+  },
+  {
+    "Query": "List claims where Claim Id less than 276603.39.",
+    "pandas_code": "df[df['Claim_Id'] < 276603.39]"
+  },
+  {
+    "Query": "Get claims where Approved Amount at least 306.85.",
+    "pandas_code": "df[df['Approved_Amount'] >= 306.85]"
+  },
+  {
+    "Query": "Show claims where Claim Id equal to 277418.61.",
+    "pandas_code": "df[df['Claim_Id'] == 277418.61]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-02-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-13')]"
+  },
+  {
+    "Query": "Find claims where Provider Name contains 'CASA D'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'CASA D', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 383 and 1418.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 383.0) & (df['Claim_Amount'] <= 1418.29)]"
+  },
+  {
+    "Query": "Find claims where Policy Number is 'BGDC221000038-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC221000038-01-000']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date before 2022-12-10.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-12-10')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount at least 1504.56.",
+    "pandas_code": "df[df['Claim_Amount'] >= 1504.56]"
+  },
+  {
+    "Query": "Display claims where Claim Number is 'CBGDC23060691-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23060691-00']"
+  },
+  {
+    "Query": "Get claims where Claim Id equal to 261874.53.",
+    "pandas_code": "df[df['Claim_Id'] == 261874.53]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-03-21.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-21')]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2022-12-12.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-12')]"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 153 and 1488.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 153.2) & (df['Approved_Amount'] <= 1488.01)]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 695 and 1071.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 695.08) & (df['Approved_Amount'] <= 1071.37)]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-02-03.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-02-03')]"
+  },
+  {
+    "Query": "List claims where Claim Id less than 276936.25.",
+    "pandas_code": "df[df['Claim_Id'] < 276936.25]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-05-05.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-05')]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-05-17.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-17')]"
+  },
+  {
+    "Query": "List claims where Claim Id between 269163 and 272527.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269163.83) & (df['Claim_Id'] <= 272527.67)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-04-27.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-27')]"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2022-11-15 and 2023-06-28.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-15')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-28'))]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-01-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-01-16')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-04-18.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-04-18')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2023-01-01.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-01')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id at most 263766.01.",
+    "pandas_code": "df[df['Claim_Id'] <= 263766.01]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2022-11-28 and 2022-12-16.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-28')) & (df['Loss_Date'] <= pd.to_datetime('2022-12-16'))]"
+  },
+  {
+    "Query": "Display claims where Claim Id at most 263577.14.",
+    "pandas_code": "df[df['Claim_Id'] <= 263577.14]"
+  },
+  {
+    "Query": "Get claims where Provider Code is 'PC101350'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Show claims where Loss Date between 2023-02-20 and 2023-02-25.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-20')) & (df['Loss_Date'] <= pd.to_datetime('2023-02-25'))]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 538 and 1085.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 538.37) & (df['Claim_Amount'] <= 1085.13)]"
+  },
+  {
+    "Query": "Find claims where Policy Number is 'BGDC211000722-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000722-01-000']"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2023-03-19.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-19')]"
+  },
+  {
+    "Query": "Find claims where Scheme is 'SME 2-GP-CO'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 2-GP-CO']"
+  },
+  {
+    "Query": "Find claims where Claim Amount at least 1335.09.",
+    "pandas_code": "df[df['Claim_Amount'] >= 1335.09]"
+  },
+  {
+    "Query": "List claims where Loss Type is 'GP Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'GP Treatment']"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2023-04-29.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-29')]"
+  },
+  {
+    "Query": "Display claims where Status Code contains 'AP2'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'AP2', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Claim Id between 268613 and 274229.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268613.96) & (df['Claim_Id'] <= 274229.06)]"
+  },
+  {
+    "Query": "Provide claims where Claim Id between 265344 and 278508.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265344.73) & (df['Claim_Id'] <= 278508.67)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2022-12-31 and 2023-03-03.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-31')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-03'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-05-06 and 2023-06-16.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-05-06')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-16'))]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-07-14.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-14')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount less than 244.77.",
+    "pandas_code": "df[df['Claim_Amount'] < 244.77]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-03-09.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-09')]"
+  },
+  {
+    "Query": "List claims where Status is 'Payment Approval'.",
+    "pandas_code": "df[df['Status'] == 'Payment Approval']"
+  },
+  {
+    "Query": "List claims where Claim Number contains 'CBGDC2'.",
+    "pandas_code": "df[df['Claim_Number'].str.contains(r'CBGDC2', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Number is 'CBGDC23070354-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23070354-00']"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-02-17.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-17')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-02-01 and 2023-06-29.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-01')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-29'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id at least 266342.07.",
+    "pandas_code": "df[df['Claim_Id'] >= 266342.07]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-05-20.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-20')]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount greater than 333.57.",
+    "pandas_code": "df[df['Approved_Amount'] > 333.57]"
+  },
+  {
+    "Query": "Find claims where Patient Name is 'Ms Yanping Chia'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Yanping Chia']"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2022-12-26.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-26')]"
+  },
+  {
+    "Query": "Show claims where Scheme is 'New Template 3 '.",
+    "pandas_code": "df[df['Scheme'] == 'New Template 3 ']"
+  },
+  {
+    "Query": "Get claims where Scheme contains 'SME 3-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 3-', na=False)]"
+  },
+  {
+    "Query": "Display claims where Approved Amount at most 822.11.",
+    "pandas_code": "df[df['Approved_Amount'] <= 822.11]"
+  },
+  {
+    "Query": "Fetch claims where Provider Name is 'KANDANG KERBAU HOSPITAL (KK Womens and Childrens Hospital)'.",
+    "pandas_code": "df[df['Provider_Name'] == 'KANDANG KERBAU HOSPITAL (KK Womens and Childrens Hospital)']"
+  },
+  {
+    "Query": "Provide claims where Claim Number contains 'CBGDC2'.",
+    "pandas_code": "df[df['Claim_Number'].str.contains(r'CBGDC2', na=False)]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 192 and 816.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 192.41) & (df['Approved_Amount'] <= 816.91)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 512 and 980.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 512.17) & (df['Approved_Amount'] <= 980.63)]"
+  },
+  {
+    "Query": "List claims where Loss Date before 2023-02-07.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-07')]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-01-06 and 2023-05-29.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-06')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-29'))]"
+  },
+  {
+    "Query": "Get claims where Scheme is 'SME 2-SP-1000'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 2-SP-1000']"
+  },
+  {
+    "Query": "Find claims where Status contains 'Void'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Void', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount less than 645.62.",
+    "pandas_code": "df[df['Claim_Amount'] < 645.62]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Type is 'Cashless'.",
+    "pandas_code": "df[df['Claim_Type'] == 'Cashless']"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 300 and 1244.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 300.19) & (df['Approved_Amount'] <= 1244.97)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2023-01-14.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-01-14')]"
+  },
+  {
+    "Query": "Display claims where Claim Id at most 270252.04.",
+    "pandas_code": "df[df['Claim_Id'] <= 270252.04]"
+  },
+  {
+    "Query": "Get claims where Status contains 'Void'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Void', na=False)]"
+  },
+  {
+    "Query": "Display claims where Patient Name is 'Ms Praveena D/O Singara Velu'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Praveena D/O Singara Velu']"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2022-12-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-12')]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2023-01-23 and 2023-06-18.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-23')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-18'))]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2023-03-07.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-07')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 46 and 1351.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 46.04) & (df['Claim_Amount'] <= 1351.74)]"
+  },
+  {
+    "Query": "List claims where Loss Date after 2023-03-17.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-17')]"
+  },
+  {
+    "Query": "Find claims where Provider Name contains 'Toa Pa'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Toa Pa', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Amount less than 101.92.",
+    "pandas_code": "df[df['Claim_Amount'] < 101.92]"
+  },
+  {
+    "Query": "Show claims where Status Code is 'PY1'.",
+    "pandas_code": "df[df['Status_Code'] == 'PY1']"
+  },
+  {
+    "Query": "Show claims where Claim Amount greater than 408.57.",
+    "pandas_code": "df[df['Claim_Amount'] > 408.57]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount greater than 152.68.",
+    "pandas_code": "df[df['Claim_Amount'] > 152.68]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 593 and 1061.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 593.32) & (df['Approved_Amount'] <= 1061.64)]"
+  },
+  {
+    "Query": "List claims where Product is 'Hospital & Surgical'.",
+    "pandas_code": "df[df['Product'] == 'Hospital & Surgical']"
+  },
+  {
+    "Query": "Provide claims where Patient Name is 'Mr Junxi Ian Han'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Junxi Ian Han']"
+  },
+  {
+    "Query": "Provide claims where Claim Amount equal to 980.68.",
+    "pandas_code": "df[df['Claim_Amount'] == 980.68]"
+  },
+  {
+    "Query": "Show claims where Claim Amount equal to 435.8.",
+    "pandas_code": "df[df['Claim_Amount'] == 435.8]"
+  },
+  {
+    "Query": "Show claims where Loss Date between 2022-11-23 and 2023-07-06.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-23')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-06'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2023-04-20.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-20')]"
+  },
+  {
+    "Query": "List claims where Claim Id at most 268098.77.",
+    "pandas_code": "df[df['Claim_Id'] <= 268098.77]"
+  },
+  {
+    "Query": "Fetch claims where Status Code is 'PY3'.",
+    "pandas_code": "df[df['Status_Code'] == 'PY3']"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-03-28.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-28')]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2023-03-04.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-04')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date on 2023-07-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-07-23')]"
+  },
+  {
+    "Query": "Find claims where Status Code contains 'AP2'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'AP2', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Id equal to 269600.23.",
+    "pandas_code": "df[df['Claim_Id'] == 269600.23]"
+  },
+  {
+    "Query": "Fetch claims where Deduction Amount equal to 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] == 0.0]"
+  },
+  {
+    "Query": "List claims where Claim Amount between 577 and 1358.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 577.77) & (df['Claim_Amount'] <= 1358.79)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date before 2023-01-02.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-01-02')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2022-11-26.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-26')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2022-12-14 and 2023-01-27.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-14')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-01-27'))]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2022-12-11.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-11')]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Code contains 'PC1016'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1016', na=False)]"
+  },
+  {
+    "Query": "List claims where Policy Number is 'BGDC211000219-02-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000219-02-000']"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-01-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-04')]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-06-14 and 2023-07-16.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-06-14')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-16'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id at least 278320.45.",
+    "pandas_code": "df[df['Claim_Id'] >= 278320.45]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date on 2023-01-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-27')]"
+  },
+  {
+    "Query": "List claims where Claim Amount at least 207.75.",
+    "pandas_code": "df[df['Claim_Amount'] >= 207.75]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2022-12-15 and 2023-05-21.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-15')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-21'))]"
+  },
+  {
+    "Query": "Find claims where Claim Number is 'CBGDC23068938-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23068938-00']"
+  },
+  {
+    "Query": "Get claims where Loss Type is 'Dental'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Dental']"
+  },
+  {
+    "Query": "Show claims where Provider Name is 'Q & M Dental Surgery (Bedok)'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Q & M Dental Surgery (Bedok)']"
+  },
+  {
+    "Query": "Display claims where Loss Date on 2022-12-08.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-08')]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-01-28.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-28')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-04-13 and 2023-06-17.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-04-13')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-17'))]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-02-05 and 2023-05-06.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-05')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-06'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2023-02-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-02-25')]"
+  },
+  {
+    "Query": "Display claims where Provider Code is 'PC101171'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101171']"
+  },
+  {
+    "Query": "Fetch claims where Status contains 'Paymen'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Paymen', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-06-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-06-24')]"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name is 'Lester Melvin Elago Edano'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Lester Melvin Elago Edano']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2022-12-18.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-12-18')]"
+  },
+  {
+    "Query": "Provide claims where Claim Id at least 264062.44.",
+    "pandas_code": "df[df['Claim_Id'] >= 264062.44]"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2023-04-04 and 2023-04-10.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-04-04')) & (df['Loss_Date'] <= pd.to_datetime('2023-04-10'))]"
+  },
+  {
+    "Query": "List claims where Approved Amount less than 287.65.",
+    "pandas_code": "df[df['Approved_Amount'] < 287.65]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2023-07-20.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-20')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date before 2023-05-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-23')]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 718 and 1252.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 718.95) & (df['Approved_Amount'] <= 1252.86)]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 492 and 976.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 492.09) & (df['Claim_Amount'] <= 976.37)]"
+  },
+  {
+    "Query": "Show claims where Loss Date between 2022-11-08 and 2023-06-05.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-08')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-05'))]"
+  },
+  {
+    "Query": "Get claims where Provider Name is 'Jurong Medical Centre'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Jurong Medical Centre']"
+  },
+  {
+    "Query": "Display claims where Loss Date on 2022-12-05.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-05')]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Code is 'PC104076'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104076']"
+  },
+  {
+    "Query": "Show claims where Claim Id at most 279385.74.",
+    "pandas_code": "df[df['Claim_Id'] <= 279385.74]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2023-03-09.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-09')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2023-03-22 and 2023-07-13.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-22')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-13'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 267474 and 276984.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267474.36) & (df['Claim_Id'] <= 276984.86)]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-04-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-13')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 266783 and 274257.",
+    "pandas_code": "df[(df['Claim_Id'] >= 266783.03) & (df['Claim_Id'] <= 274257.03)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 267457 and 275504.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267457.54) & (df['Claim_Id'] <= 275504.27)]"
+  },
+  {
+    "Query": "Find claims where Policy Number is 'BGDP221001377-00-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221001377-00-000']"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-02-22.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-22')]"
+  },
+  {
+    "Query": "List claims where Product contains 'Genera'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Genera', na=False)]"
+  },
+  {
+    "Query": "Find claims where Provider Code contains 'OTH000'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'OTH000', na=False)]"
+  },
+  {
+    "Query": "Display claims where Status Code is 'CV0'.",
+    "pandas_code": "df[df['Status_Code'] == 'CV0']"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2022-11-19 and 2023-01-17.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-19')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-01-17'))]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-03-14.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-14')]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-06-07.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-06-07')]"
+  },
+  {
+    "Query": "Get claims where Claim Type contains 'Reimbu'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Reimbu', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Id equal to 277416.0.",
+    "pandas_code": "df[df['Claim_Id'] == 277416.0]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2023-07-22.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-22')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2022-11-26 and 2022-12-08.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-26')) & (df['Claim_Created_Date'] <= pd.to_datetime('2022-12-08'))]"
+  },
+  {
+    "Query": "Show claims where Claim Id between 270182 and 275035.",
+    "pandas_code": "df[(df['Claim_Id'] >= 270182.96) & (df['Claim_Id'] <= 275035.24)]"
+  },
+  {
+    "Query": "List claims where Claim Amount between 477 and 1170.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 477.98) & (df['Claim_Amount'] <= 1170.99)]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 619 and 1478.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 619.02) & (df['Approved_Amount'] <= 1478.42)]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2022-12-11.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-11')]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2023-02-18.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-18')]"
+  },
+  {
+    "Query": "Find claims where Patient Name is 'Ms Shee Ren Lau'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Shee Ren Lau']"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 415 and 1254.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 415.08) & (df['Approved_Amount'] <= 1254.58)]"
+  },
+  {
+    "Query": "Find claims where Provider Code is 'PC100731'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100731']"
+  },
+  {
+    "Query": "Retrieve claims where Product is 'Group Dental Benefit'.",
+    "pandas_code": "df[df['Product'] == 'Group Dental Benefit']"
+  },
+  {
+    "Query": "Retrieve claims where Deduction Amount less than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] < 0.0]"
+  },
+  {
+    "Query": "Display claims where Provider Name is 'Others'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Others']"
+  },
+  {
+    "Query": "Get claims where Patient Name is 'Ms Shan Shan Ma'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Shan Shan Ma']"
+  },
+  {
+    "Query": "Display claims where Product is 'Group Specialist'.",
+    "pandas_code": "df[df['Product'] == 'Group Specialist']"
+  },
+  {
+    "Query": "Display claims where Policy Number is 'BGDP211000047-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP211000047-01-000']"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 755 and 1260.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 755.36) & (df['Claim_Amount'] <= 1260.12)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2023-03-13 and 2023-03-13.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-13')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-13'))]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2022-12-21 and 2023-07-03.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-21')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-03'))]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2022-12-27.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-27')]"
+  },
+  {
+    "Query": "Fetch claims where Scheme contains 'SME 2-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 2-', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2023-02-06.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-02-06')]"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2023-01-05 and 2023-03-12.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-05')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-12'))]"
+  },
+  {
+    "Query": "Find claims where Patient Name is 'Mr Yu Chiu Sun'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Yu Chiu Sun']"
+  },
+  {
+    "Query": "Find claims where Patient Name contains 'Ms Pra'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Pra', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date on 2023-01-26.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-26')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date before 2023-07-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-16')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-03-21 and 2023-07-22.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-21')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-22'))]"
+  },
+  {
+    "Query": "Find claims where Loss Type is 'GP Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'GP Treatment']"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 748 and 1604.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 748.75) & (df['Claim_Amount'] <= 1604.44)]"
+  },
+  {
+    "Query": "Retrieve claims where Status Code is 'CV0'.",
+    "pandas_code": "df[df['Status_Code'] == 'CV0']"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 263058 and 278189.",
+    "pandas_code": "df[(df['Claim_Id'] >= 263058.79) & (df['Claim_Id'] <= 278189.43)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date after 2023-04-15.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-15')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2023-03-02.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-02')]"
+  },
+  {
+    "Query": "Display claims where Loss Date on 2022-11-29.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-29')]"
+  },
+  {
+    "Query": "Display claims where Approved Amount at least 171.48.",
+    "pandas_code": "df[df['Approved_Amount'] >= 171.48]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2022-11-10.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-10')]"
+  },
+  {
+    "Query": "Get claims where Policy Number contains 'BGDC21'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDC21', na=False)]"
+  },
+  {
+    "Query": "Find claims where Provider Code contains 'PC1040'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1040', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2023-04-15.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-15')]"
+  },
+  {
+    "Query": "Show claims where Product is 'Specialist'.",
+    "pandas_code": "df[df['Product'] == 'Specialist']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-02-21.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-21')]"
+  },
+  {
+    "Query": "Fetch claims where Status is 'Void'.",
+    "pandas_code": "df[df['Status'] == 'Void']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-02-26.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-02-26')]"
+  },
+  {
+    "Query": "List claims where Claim Amount at least 1036.85.",
+    "pandas_code": "df[df['Claim_Amount'] >= 1036.85]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 729 and 950.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 729.29) & (df['Claim_Amount'] <= 950.18)]"
+  },
+  {
+    "Query": "List claims where Claim Amount at least 1409.51.",
+    "pandas_code": "df[df['Claim_Amount'] >= 1409.51]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2022-12-13 and 2023-05-31.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-13')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-31'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id greater than 277602.64.",
+    "pandas_code": "df[df['Claim_Id'] > 277602.64]"
+  },
+  {
+    "Query": "Find claims where Deduction Amount at most 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] <= 0.0]"
+  },
+  {
+    "Query": "Find claims where Approved Amount equal to 804.38.",
+    "pandas_code": "df[df['Approved_Amount'] == 804.38]"
+  },
+  {
+    "Query": "Find claims where Claim Amount greater than 331.84.",
+    "pandas_code": "df[df['Claim_Amount'] > 331.84]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-01-06 and 2023-01-20.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-06')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-01-20'))]"
+  },
+  {
+    "Query": "Get claims where Claim Amount at most 1159.11.",
+    "pandas_code": "df[df['Claim_Amount'] <= 1159.11]"
+  },
+  {
+    "Query": "Provide claims where Provider Name is 'Mark Loh Paediatrics'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Mark Loh Paediatrics']"
+  },
+  {
+    "Query": "Display claims where Claim Id equal to 263071.31.",
+    "pandas_code": "df[df['Claim_Id'] == 263071.31]"
+  },
+  {
+    "Query": "Retrieve claims where Deduction Amount at most 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] <= 0.0]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2022-12-04 and 2023-01-06.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-04')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-01-06'))]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2022-12-21.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-21')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2023-01-20 and 2023-06-19.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-20')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-19'))]"
+  },
+  {
+    "Query": "Fetch claims where Patient Name contains 'Mr Sho'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Sho', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2023-01-04.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-04')]"
+  },
+  {
+    "Query": "Get claims where Status is 'Duplicate'.",
+    "pandas_code": "df[df['Status'] == 'Duplicate']"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-03-04.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-04')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount greater than 1079.06.",
+    "pandas_code": "df[df['Claim_Amount'] > 1079.06]"
+  },
+  {
+    "Query": "Find claims where Claim Id less than 279072.31.",
+    "pandas_code": "df[df['Claim_Id'] < 279072.31]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 312 and 1319.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 312.57) & (df['Approved_Amount'] <= 1319.93)]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name is 'Alliance Medinet Pte Ltd'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Alliance Medinet Pte Ltd']"
+  },
+  {
+    "Query": "Find claims where Approved Amount at least 921.78.",
+    "pandas_code": "df[df['Approved_Amount'] >= 921.78]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date between 2023-04-04 and 2023-04-09.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-04-04')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-04-09'))]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 380 and 892.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 380.94) & (df['Claim_Amount'] <= 892.0)]"
+  },
+  {
+    "Query": "Provide claims where Provider Name contains 'NATION'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'NATION', na=False)]"
+  },
+  {
+    "Query": "Get claims where Provider Code is 'PC103185'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103185']"
+  },
+  {
+    "Query": "Get claims where Status Code is 'PY3'.",
+    "pandas_code": "df[df['Status_Code'] == 'PY3']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2023-01-01.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-01')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Number is 'CBGDC23061249-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23061249-00']"
+  },
+  {
+    "Query": "Find claims where Claim Created Date on 2023-04-14.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-04-14')]"
+  },
+  {
+    "Query": "Provide claims where Scheme contains 'SME 1-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 1-', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Id between 264864 and 276297.",
+    "pandas_code": "df[(df['Claim_Id'] >= 264864.75) & (df['Claim_Id'] <= 276297.66)]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2022-12-27.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-27')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2023-02-18.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-02-18')]"
+  },
+  {
+    "Query": "Display claims where Scheme is 'SME 1-1000-NonPanel'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-1000-NonPanel']"
+  },
+  {
+    "Query": "Retrieve claims where Status Code is 'RJ2'.",
+    "pandas_code": "df[df['Status_Code'] == 'RJ2']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-05-06.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-06')]"
+  },
+  {
+    "Query": "Get claims where Claim Id between 262984 and 271507.",
+    "pandas_code": "df[(df['Claim_Id'] >= 262984.19) & (df['Claim_Id'] <= 271507.81)]"
+  },
+  {
+    "Query": "List claims where Scheme contains 'Templa'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'Templa', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-02-25.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-02-25')]"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2023-04-02.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-02')]"
+  },
+  {
+    "Query": "Find claims where Status Code is 'DU0'.",
+    "pandas_code": "df[df['Status_Code'] == 'DU0']"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2022-11-09 and 2023-07-21.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-09')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-21'))]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-05-22.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-22')]"
+  },
+  {
+    "Query": "Get claims where Provider Code contains 'PC1011'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1011', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date on 2023-03-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-13')]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 71 and 807.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 71.41) & (df['Approved_Amount'] <= 807.72)]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2023-01-06 and 2023-06-11.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-06')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-11'))]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2023-04-19.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-19')]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 230 and 1238.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 230.34) & (df['Approved_Amount'] <= 1238.65)]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-02-17 and 2023-06-12.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-17')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-12'))]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-01-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-01-12')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount at least 951.03.",
+    "pandas_code": "df[df['Claim_Amount'] >= 951.03]"
+  },
+  {
+    "Query": "Show claims where Patient Name contains 'Mr Zon'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Zon', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2023-05-29.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-29')]"
+  },
+  {
+    "Query": "Display claims where Claim Id less than 268567.72.",
+    "pandas_code": "df[df['Claim_Id'] < 268567.72]"
+  },
+  {
+    "Query": "Get claims where Approved Amount at least 339.36.",
+    "pandas_code": "df[df['Approved_Amount'] >= 339.36]"
+  },
+  {
+    "Query": "Provide claims where Status Code contains 'RJ2'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'RJ2', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-01-18 and 2023-07-18.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-18')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-18'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id greater than 277660.02.",
+    "pandas_code": "df[df['Claim_Id'] > 277660.02]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2022-11-17.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-17')]"
+  },
+  {
+    "Query": "Provide claims where Claim Type is 'Cashless'.",
+    "pandas_code": "df[df['Claim_Type'] == 'Cashless']"
+  },
+  {
+    "Query": "List claims where Product contains 'Group '.",
+    "pandas_code": "df[df['Product'].str.contains(r'Group ', na=False)]"
+  },
+  {
+    "Query": "Get claims where Status Code contains 'PY3'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'PY3', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 442 and 910.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 442.2) & (df['Claim_Amount'] <= 910.41)]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 490 and 1162.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 490.76) & (df['Approved_Amount'] <= 1162.59)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount greater than 1402.02.",
+    "pandas_code": "df[df['Approved_Amount'] > 1402.02]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 106 and 1218.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 106.26) & (df['Approved_Amount'] <= 1218.98)]"
+  },
+  {
+    "Query": "Display claims where Claim Type is 'CHS'.",
+    "pandas_code": "df[df['Claim_Type'] == 'CHS']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Type is 'Specialist Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Specialist Treatment']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2023-01-07.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-01-07')]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount equal to 1046.2.",
+    "pandas_code": "df[df['Claim_Amount'] == 1046.2]"
+  },
+  {
+    "Query": "Show claims where Deduction Amount less than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] < 0.0]"
+  },
+  {
+    "Query": "Provide claims where Patient Name contains 'Mr Thi'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Thi', na=False)]"
+  },
+  {
+    "Query": "List claims where Loss Date before 2022-12-19.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-19')]"
+  },
+  {
+    "Query": "Find claims where Claim Amount less than 978.2.",
+    "pandas_code": "df[df['Claim_Amount'] < 978.2]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 87 and 1476.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 87.11) & (df['Approved_Amount'] <= 1476.78)]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-04-23.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-23')]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 166 and 1040.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 166.65) & (df['Approved_Amount'] <= 1040.08)]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2023-03-17 and 2023-05-06.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-17')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-06'))]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2022-11-11 and 2022-11-28.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-11')) & (df['Loss_Date'] <= pd.to_datetime('2022-11-28'))]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 550 and 1525.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 550.77) & (df['Approved_Amount'] <= 1525.72)]"
+  },
+  {
+    "Query": "Fetch claims where Patient Name is 'Ms Chee Mei Wong'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Chee Mei Wong']"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-06-02.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-02')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-05-30.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-30')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date before 2023-05-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-24')]"
+  },
+  {
+    "Query": "Find claims where Policy Number is 'BGDC211000256-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000256-01-000']"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 71 and 1033.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 71.35) & (df['Approved_Amount'] <= 1033.61)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Number is 'CBGDC23060859-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23060859-00']"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-02-03 and 2023-07-18.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-03')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-18'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 767 and 1214.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 767.1) & (df['Claim_Amount'] <= 1214.31)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount at least 820.79.",
+    "pandas_code": "df[df['Claim_Amount'] >= 820.79]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2022-11-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-13')]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount greater than 1255.78.",
+    "pandas_code": "df[df['Claim_Amount'] > 1255.78]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2023-05-31.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-31')]"
+  },
+  {
+    "Query": "Display claims where Provider Name contains 'Other '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Other ', na=False)]"
+  },
+  {
+    "Query": "Find claims where Patient Name is 'Ms Cindy Leng Leng Koh'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Cindy Leng Leng Koh']"
+  },
+  {
+    "Query": "Provide claims where Approved Amount between 424 and 988.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 424.79) & (df['Approved_Amount'] <= 988.09)]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 345 and 1320.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 345.25) & (df['Approved_Amount'] <= 1320.5)]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-01-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-01-23')]"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2023-03-11.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-11')]"
+  },
+  {
+    "Query": "Find claims where Claim Amount at most 272.01.",
+    "pandas_code": "df[df['Claim_Amount'] <= 272.01]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 303 and 1466.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 303.68) & (df['Claim_Amount'] <= 1466.84)]"
+  },
+  {
+    "Query": "Display claims where Claim Amount less than 403.66.",
+    "pandas_code": "df[df['Claim_Amount'] < 403.66]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 265116 and 275374.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265116.45) & (df['Claim_Id'] <= 275374.74)]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2022-12-21.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-21')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2023-05-25 and 2023-07-31.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-05-25')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-31'))]"
+  },
+  {
+    "Query": "Get claims where Product is 'Hospital & Surgical'.",
+    "pandas_code": "df[df['Product'] == 'Hospital & Surgical']"
+  },
+  {
+    "Query": "Display claims where Claim Id between 264690 and 277814.",
+    "pandas_code": "df[(df['Claim_Id'] >= 264690.39) & (df['Claim_Id'] <= 277814.68)]"
+  },
+  {
+    "Query": "Show claims where Approved Amount less than 988.12.",
+    "pandas_code": "df[df['Approved_Amount'] < 988.12]"
+  },
+  {
+    "Query": "Display claims where Loss Date on 2023-07-30.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-07-30')]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-02-10 and 2023-03-22.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-10')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-22'))]"
+  },
+  {
+    "Query": "Provide claims where Scheme contains 'Templa'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'Templa', na=False)]"
+  },
+  {
+    "Query": "Get claims where Provider Code is 'PC100731'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100731']"
+  },
+  {
+    "Query": "Display claims where Claim Number is 'CBGDP23038431-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23038431-00']"
+  },
+  {
+    "Query": "List claims where Claim Created Date before 2023-01-17.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-01-17')]"
+  },
+  {
+    "Query": "Show claims where Policy Number is 'BGDC211000256-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000256-01-000']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date before 2023-03-10.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-10')]"
+  },
+  {
+    "Query": "List claims where Claim Id between 264755 and 274097.",
+    "pandas_code": "df[(df['Claim_Id'] >= 264755.38) & (df['Claim_Id'] <= 274097.83)]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 370 and 789.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 370.04) & (df['Approved_Amount'] <= 789.52)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount at most 744.47.",
+    "pandas_code": "df[df['Approved_Amount'] <= 744.47]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount equal to 506.56.",
+    "pandas_code": "df[df['Approved_Amount'] == 506.56]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 108 and 971.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 108.65) & (df['Claim_Amount'] <= 971.93)]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 280 and 1101.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 280.21) & (df['Claim_Amount'] <= 1101.23)]"
+  },
+  {
+    "Query": "Fetch claims where Patient Name contains 'Ms Wei'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Wei', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2022-11-18.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-11-18')]"
+  },
+  {
+    "Query": "Show claims where Claim Type contains 'Reimbu'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Reimbu', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Type is 'Dental'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Dental']"
+  },
+  {
+    "Query": "Fetch claims where Policy Number is 'BGDC211000341-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000341-01-000']"
+  },
+  {
+    "Query": "Display claims where Claim Amount between 397 and 1282.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 397.97) & (df['Claim_Amount'] <= 1282.77)]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 104 and 1385.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 104.07) & (df['Claim_Amount'] <= 1385.37)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2023-02-24 and 2023-05-08.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-24')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-08'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2022-11-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-11-15')]"
+  },
+  {
+    "Query": "Show claims where Claim Id between 267158 and 276470.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267158.34) & (df['Claim_Id'] <= 276470.23)]"
+  },
+  {
+    "Query": "List claims where Claim Amount between 295 and 1297.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 295.17) & (df['Claim_Amount'] <= 1297.65)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-07-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-19')]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2022-12-08.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-08')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 268898 and 273493.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268898.74) & (df['Claim_Id'] <= 273493.46)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-08-01.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-08-01')]"
+  },
+  {
+    "Query": "List claims where Claim Id between 263116 and 272363.",
+    "pandas_code": "df[(df['Claim_Id'] >= 263116.72) & (df['Claim_Id'] <= 272363.19)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2022-11-13 and 2023-06-14.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-13')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-14'))]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 609 and 1155.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 609.27) & (df['Approved_Amount'] <= 1155.36)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2023-05-01.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-01')]"
+  },
+  {
+    "Query": "Display claims where Claim Id equal to 269837.16.",
+    "pandas_code": "df[df['Claim_Id'] == 269837.16]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount at least 818.58.",
+    "pandas_code": "df[df['Claim_Amount'] >= 818.58]"
+  },
+  {
+    "Query": "List claims where Approved Amount at most 208.89.",
+    "pandas_code": "df[df['Approved_Amount'] <= 208.89]"
+  },
+  {
+    "Query": "Find claims where Product contains 'Group '.",
+    "pandas_code": "df[df['Product'].str.contains(r'Group ', na=False)]"
+  },
+  {
+    "Query": "Show claims where Policy Number is 'BGDP221001107-00-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221001107-00-000']"
+  },
+  {
+    "Query": "Fetch claims where Policy Number is 'BGDC221000136-00-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC221000136-00-000']"
+  },
+  {
+    "Query": "List claims where Status Code contains 'DU0'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'DU0', na=False)]"
+  },
+  {
+    "Query": "List claims where Claim Id between 266975 and 275391.",
+    "pandas_code": "df[(df['Claim_Id'] >= 266975.4) & (df['Claim_Id'] <= 275391.76)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2022-11-13.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-13')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-02-22.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-02-22')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Number contains 'CBGDC2'.",
+    "pandas_code": "df[df['Claim_Number'].str.contains(r'CBGDC2', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-06-11.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-11')]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount at least 1068.0.",
+    "pandas_code": "df[df['Approved_Amount'] >= 1068.0]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2023-03-08.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-08')]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount between 435 and 881.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 435.44) & (df['Claim_Amount'] <= 881.63)]"
+  },
+  {
+    "Query": "Provide claims where Provider Code contains 'PC1004'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1004', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-06-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-27')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2023-05-07.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-07')]"
+  },
+  {
+    "Query": "List claims where Patient Name contains 'Ms Jay'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Jay', na=False)]"
+  },
+  {
+    "Query": "Find claims where Provider Code is 'PC101195'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101195']"
+  },
+  {
+    "Query": "Find claims where Patient Name contains 'Mr Kan'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Kan', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 383 and 980.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 383.9) & (df['Claim_Amount'] <= 980.01)]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 620 and 1513.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 620.48) & (df['Approved_Amount'] <= 1513.08)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2023-03-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-04')]"
+  },
+  {
+    "Query": "Show claims where Provider Name contains 'Manadr'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Manadr', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name is 'Mr Caleb Tan'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Caleb Tan']"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-07-20.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-20')]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-01-13.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-01-13')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2023-06-22 and 2023-07-25.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-06-22')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-25'))]"
+  },
+  {
+    "Query": "Provide claims where Loss Date after 2023-05-20.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-20')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date between 2022-12-17 and 2023-05-09.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-17')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-09'))]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2023-01-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-27')]"
+  },
+  {
+    "Query": "Get claims where Policy Number contains 'BGDC22'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDC22', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date after 2023-04-30.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-30')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2023-06-23.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-06-23')]"
+  },
+  {
+    "Query": "Show claims where Patient Name contains 'Xiaohu'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Xiaohu', na=False)]"
+  },
+  {
+    "Query": "Display claims where Status Code contains 'PY1'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'PY1', na=False)]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-05-08.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-08')]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-02-19.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-19')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-05-05.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-05')]"
+  },
+  {
+    "Query": "Provide claims where Patient Name contains 'Mr Yin'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Yin', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2022-11-11 and 2022-12-03.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-11')) & (df['Claim_Created_Date'] <= pd.to_datetime('2022-12-03'))]"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2022-12-08.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-08')]"
+  },
+  {
+    "Query": "List claims where Claim Number contains 'CBGDP2'.",
+    "pandas_code": "df[df['Claim_Number'].str.contains(r'CBGDP2', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Type is 'Cashless'.",
+    "pandas_code": "df[df['Claim_Type'] == 'Cashless']"
+  },
+  {
+    "Query": "Get claims where Provider Code is 'DR100002'.",
+    "pandas_code": "df[df['Provider_Code'] == 'DR100002']"
+  },
+  {
+    "Query": "Fetch claims where Status is 'Rejected'.",
+    "pandas_code": "df[df['Status'] == 'Rejected']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-04-02.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-02')]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-06-25.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-25')]"
+  },
+  {
+    "Query": "Provide claims where Claim Number is 'CBGDC23070380-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23070380-00']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-06-14.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-14')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 478 and 1058.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 478.99) & (df['Claim_Amount'] <= 1058.75)]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 50 and 1272.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 50.1) & (df['Claim_Amount'] <= 1272.28)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date between 2022-12-01 and 2022-12-26.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-01')) & (df['Claim_Created_Date'] <= pd.to_datetime('2022-12-26'))]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-02-26 and 2023-05-25.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-26')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-25'))]"
+  },
+  {
+    "Query": "Display claims where Claim Id equal to 274422.99.",
+    "pandas_code": "df[df['Claim_Id'] == 274422.99]"
+  },
+  {
+    "Query": "Show claims where Provider Code is 'PC104076'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104076']"
+  },
+  {
+    "Query": "Display claims where Provider Name contains 'Doctor'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Doctor', na=False)]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-04-02 and 2023-07-01.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-04-02')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-01'))]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2023-02-09.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-02-09')]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2023-05-14.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-14')]"
+  },
+  {
+    "Query": "Display claims where Claim Amount at most 920.62.",
+    "pandas_code": "df[df['Claim_Amount'] <= 920.62]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id at least 273262.56.",
+    "pandas_code": "df[df['Claim_Id'] >= 273262.56]"
+  },
+  {
+    "Query": "Show claims where Claim Id at least 261832.31.",
+    "pandas_code": "df[df['Claim_Id'] >= 261832.31]"
+  },
+  {
+    "Query": "Find claims where Claim Id at least 277140.13.",
+    "pandas_code": "df[df['Claim_Id'] >= 277140.13]"
+  },
+  {
+    "Query": "Provide claims where Provider Code is 'PC100466'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100466']"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 740 and 1424.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 740.55) & (df['Approved_Amount'] <= 1424.45)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Type contains 'Reimbu'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Reimbu', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Scheme is 'SME 1-1BPTE'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-1BPTE']"
+  },
+  {
+    "Query": "List claims where Claim Number is 'CBGDC23062156-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23062156-00']"
+  },
+  {
+    "Query": "Fetch claims where Provider Code is 'PC102378'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC102378']"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2023-06-24.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-06-24')]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2023-03-19.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-19')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Type contains 'CHS'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'CHS', na=False)]"
+  },
+  {
+    "Query": "Display claims where Approved Amount equal to 135.76.",
+    "pandas_code": "df[df['Approved_Amount'] == 135.76]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id at most 272410.35.",
+    "pandas_code": "df[df['Claim_Id'] <= 272410.35]"
+  },
+  {
+    "Query": "Get claims where Provider Code is 'PC100541'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100541']"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name is 'TAN TOCK SENG HOSPITAL (OUTPATIENT)'.",
+    "pandas_code": "df[df['Provider_Name'] == 'TAN TOCK SENG HOSPITAL (OUTPATIENT)']"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 564 and 1137.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 564.03) & (df['Approved_Amount'] <= 1137.1)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-04-08.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-08')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2023-04-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-04-28')]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date on 2022-12-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-12-25')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2023-03-19.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-19')]"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name is 'Mr Ling Hui Julian Wong'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Ling Hui Julian Wong']"
+  },
+  {
+    "Query": "Provide claims where Claim Id at most 269888.35.",
+    "pandas_code": "df[df['Claim_Id'] <= 269888.35]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-03-19.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-19')]"
+  },
+  {
+    "Query": "Fetch claims where Scheme is 'SME 2-GP-CO'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 2-GP-CO']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-03-18.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-18')]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2022-11-19.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-19')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2023-01-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-23')]"
+  },
+  {
+    "Query": "Show claims where Provider Code is 'PC100408'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100408']"
+  },
+  {
+    "Query": "List claims where Approved Amount equal to 838.28.",
+    "pandas_code": "df[df['Approved_Amount'] == 838.28]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date on 2023-01-21.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-21')]"
+  },
+  {
+    "Query": "Display claims where Claim Type contains 'Cashle'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Cashle', na=False)]"
+  },
+  {
+    "Query": "Show claims where Loss Date between 2023-02-04 and 2023-02-04.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-04')) & (df['Loss_Date'] <= pd.to_datetime('2023-02-04'))]"
+  },
+  {
+    "Query": "List claims where Provider Code is 'PC100469'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100469']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2022-12-10.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-12-10')]"
+  },
+  {
+    "Query": "List claims where Status is 'Void'.",
+    "pandas_code": "df[df['Status'] == 'Void']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2022-12-12 and 2023-01-29.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-12')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-01-29'))]"
+  },
+  {
+    "Query": "Provide claims where Loss Date between 2023-01-05 and 2023-03-05.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-05')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-05'))]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 64 and 1338.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 64.54) & (df['Claim_Amount'] <= 1338.71)]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount equal to 1377.1.",
+    "pandas_code": "df[df['Claim_Amount'] == 1377.1]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2022-11-30.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-30')]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 577 and 1158.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 577.25) & (df['Approved_Amount'] <= 1158.77)]"
+  },
+  {
+    "Query": "Show claims where Loss Date before 2023-05-24.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-24')]"
+  },
+  {
+    "Query": "Find claims where Status Code contains 'DU0'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'DU0', na=False)]"
+  },
+  {
+    "Query": "Show claims where Loss Date between 2023-04-18 and 2023-06-28.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-04-18')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-28'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 266756 and 271053.",
+    "pandas_code": "df[(df['Claim_Id'] >= 266756.42) & (df['Claim_Id'] <= 271053.47)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2023-05-11 and 2023-06-05.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-05-11')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-05'))]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount less than 384.81.",
+    "pandas_code": "df[df['Approved_Amount'] < 384.81]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-04-12 and 2023-06-12.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-04-12')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-12'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2022-12-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-12-04')]"
+  },
+  {
+    "Query": "Get claims where Provider Code is 'PC104077'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104077']"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount equal to 1438.51.",
+    "pandas_code": "df[df['Approved_Amount'] == 1438.51]"
+  },
+  {
+    "Query": "Show claims where Claim Id greater than 266640.82.",
+    "pandas_code": "df[df['Claim_Id'] > 266640.82]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 261625 and 275010.",
+    "pandas_code": "df[(df['Claim_Id'] >= 261625.4) & (df['Claim_Id'] <= 275010.71)]"
+  },
+  {
+    "Query": "Get claims where Claim Number is 'CBGDP23038041-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23038041-00']"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-06-18.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-06-18')]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-05-02 and 2023-06-29.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-05-02')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-29'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2022-11-22.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-22')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-04-11.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-11')]"
+  },
+  {
+    "Query": "Provide claims where Patient Name is 'Mr Ramesh Edwin'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Ramesh Edwin']"
+  },
+  {
+    "Query": "Provide claims where Claim Amount between 605 and 1521.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 605.62) & (df['Claim_Amount'] <= 1521.94)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2023-04-30.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-30')]"
+  },
+  {
+    "Query": "Display claims where Patient Name contains 'Ms Qiu'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Qiu', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-07-26.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-26')]"
+  },
+  {
+    "Query": "Provide claims where Claim Id at least 271823.35.",
+    "pandas_code": "df[df['Claim_Id'] >= 271823.35]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2022-12-11 and 2023-05-02.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-11')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-02'))]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount equal to 1292.63.",
+    "pandas_code": "df[df['Approved_Amount'] == 1292.63]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 343 and 1349.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 343.78) & (df['Approved_Amount'] <= 1349.69)]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-06-14.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-06-14')]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 143 and 1360.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 143.29) & (df['Approved_Amount'] <= 1360.85)]"
+  },
+  {
+    "Query": "Show claims where Policy Number contains 'BGDP22'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDP22', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 16 and 813.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 16.49) & (df['Approved_Amount'] <= 813.92)]"
+  },
+  {
+    "Query": "Show claims where Loss Date between 2023-06-28 and 2023-07-23.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-06-28')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-23'))]"
+  },
+  {
+    "Query": "List claims where Claim Amount at most 319.98.",
+    "pandas_code": "df[df['Claim_Amount'] <= 319.98]"
+  },
+  {
+    "Query": "Show claims where Claim Type is 'Reimbursement'.",
+    "pandas_code": "df[df['Claim_Type'] == 'Reimbursement']"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-03-28.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-28')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date on 2023-04-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-04-19')]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-05-27.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-27')]"
+  },
+  {
+    "Query": "List claims where Claim Number is 'CBGDP23038860-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23038860-00']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date between 2022-11-15 and 2022-12-02.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-15')) & (df['Claim_Created_Date'] <= pd.to_datetime('2022-12-02'))]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-03-09 and 2023-07-16.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-09')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-16'))]"
+  },
+  {
+    "Query": "Show claims where Patient Name is 'Mr Kishore Krishnamoorthy'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Kishore Krishnamoorthy']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2023-05-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-28')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 501 and 1440.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 501.29) & (df['Claim_Amount'] <= 1440.63)]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-03-25.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-25')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2023-03-27.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-27')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2022-12-30.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-30')]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2022-11-16 and 2023-02-15.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-16')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-02-15'))]"
+  },
+  {
+    "Query": "Show claims where Status is 'Duplicate'.",
+    "pandas_code": "df[df['Status'] == 'Duplicate']"
+  },
+  {
+    "Query": "Show claims where Loss Date before 2023-03-03.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-03')]"
+  },
+  {
+    "Query": "Show claims where Patient Name is 'Ms Yen Ping Elaine Ang'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Yen Ping Elaine Ang']"
+  },
+  {
+    "Query": "Show claims where Provider Name contains 'Other '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Other ', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Id between 261525 and 274149.",
+    "pandas_code": "df[(df['Claim_Id'] >= 261525.58) & (df['Claim_Id'] <= 274149.58)]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2023-07-30.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-30')]"
+  },
+  {
+    "Query": "Get claims where Status contains 'Approv'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Approv', na=False)]"
+  },
+  {
+    "Query": "Show claims where Approved Amount less than 1260.95.",
+    "pandas_code": "df[df['Approved_Amount'] < 1260.95]"
+  },
+  {
+    "Query": "Get claims where Claim Amount less than 1511.75.",
+    "pandas_code": "df[df['Claim_Amount'] < 1511.75]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-04-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-16')]"
+  },
+  {
+    "Query": "List claims where Claim Id between 265593 and 273894.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265593.35) & (df['Claim_Id'] <= 273894.34)]"
+  },
+  {
+    "Query": "Get claims where Claim Id at most 269518.68.",
+    "pandas_code": "df[df['Claim_Id'] <= 269518.68]"
+  },
+  {
+    "Query": "List claims where Loss Type is 'Dental'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Dental']"
+  },
+  {
+    "Query": "Find claims where Product is 'Group Hospital and Surgical'.",
+    "pandas_code": "df[df['Product'] == 'Group Hospital and Surgical']"
+  },
+  {
+    "Query": "Provide claims where Status Code is 'RJ2'.",
+    "pandas_code": "df[df['Status_Code'] == 'RJ2']"
+  },
+  {
+    "Query": "Show claims where Claim Id equal to 278511.34.",
+    "pandas_code": "df[df['Claim_Id'] == 278511.34]"
+  },
+  {
+    "Query": "Find claims where Claim Amount at most 1494.0.",
+    "pandas_code": "df[df['Claim_Amount'] <= 1494.0]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 675 and 865.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 675.83) & (df['Approved_Amount'] <= 865.68)]"
+  },
+  {
+    "Query": "Display claims where Provider Code contains 'PC1005'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1005', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount at most 805.88.",
+    "pandas_code": "df[df['Approved_Amount'] <= 805.88]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 23 and 1128.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 23.49) & (df['Claim_Amount'] <= 1128.24)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2022-11-11.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-11')]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount less than 582.98.",
+    "pandas_code": "df[df['Approved_Amount'] < 582.98]"
+  },
+  {
+    "Query": "List claims where Provider Name contains 'Nuffie'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Nuffie', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Amount between 816 and 1431.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 816.1) & (df['Claim_Amount'] <= 1431.56)]"
+  },
+  {
+    "Query": "Display claims where Policy Number is 'BGDC211000009-02-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000009-02-000']"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 53 and 1301.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 53.52) & (df['Claim_Amount'] <= 1301.73)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-06-03.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-03')]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2023-06-26.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-06-26')]"
+  },
+  {
+    "Query": "List claims where Product is 'Group Specialist'.",
+    "pandas_code": "df[df['Product'] == 'Group Specialist']"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 46 and 1170.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 46.29) & (df['Approved_Amount'] <= 1170.77)]"
+  },
+  {
+    "Query": "Show claims where Status Code is 'DU0'.",
+    "pandas_code": "df[df['Status_Code'] == 'DU0']"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2022-11-20 and 2023-04-03.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-20')) & (df['Loss_Date'] <= pd.to_datetime('2023-04-03'))]"
+  },
+  {
+    "Query": "List claims where Claim Id at most 269359.64.",
+    "pandas_code": "df[df['Claim_Id'] <= 269359.64]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date on 2022-12-09.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-12-09')]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-05-07.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-07')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-04-17.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-17')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2023-01-29.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-29')]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-03-10.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-10')]"
+  },
+  {
+    "Query": "Show claims where Scheme is 'SME 1-GP'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-GP']"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2022-12-25 and 2023-06-10.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-25')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-10'))]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2022-12-21 and 2023-05-16.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-21')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-16'))]"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 561 and 1430.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 561.99) & (df['Approved_Amount'] <= 1430.07)]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2022-12-24.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-24')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2022-12-04.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-04')]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-01-31 and 2023-07-05.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-31')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-05'))]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 717 and 1537.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 717.05) & (df['Claim_Amount'] <= 1537.83)]"
+  },
+  {
+    "Query": "Retrieve claims where Status Code is 'PY1'.",
+    "pandas_code": "df[df['Status_Code'] == 'PY1']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2023-03-14 and 2023-07-13.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-14')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-13'))]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2023-02-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-15')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Type is 'CHS'.",
+    "pandas_code": "df[df['Claim_Type'] == 'CHS']"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 127 and 1066.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 127.25) & (df['Claim_Amount'] <= 1066.66)]"
+  },
+  {
+    "Query": "Display claims where Claim Amount between 482 and 1085.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 482.6) & (df['Claim_Amount'] <= 1085.86)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2023-02-22 and 2023-05-14.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-22')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-14'))]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 50 and 1604.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 50.61) & (df['Claim_Amount'] <= 1604.08)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 263578 and 276523.",
+    "pandas_code": "df[(df['Claim_Id'] >= 263578.87) & (df['Claim_Id'] <= 276523.05)]"
+  },
+  {
+    "Query": "Get claims where Policy Number is 'BGDC221000003-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC221000003-01-000']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date between 2023-01-20 and 2023-02-16.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-20')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-02-16'))]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2022-12-11.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-11')]"
+  },
+  {
+    "Query": "Find claims where Scheme contains 'SME 1-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 1-', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 268721 and 275229.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268721.09) & (df['Claim_Id'] <= 275229.12)]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name is 'Manadr Pte Ltd'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Manadr Pte Ltd']"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date before 2023-01-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-01-27')]"
+  },
+  {
+    "Query": "Provide claims where Scheme is 'SME 1-SP-CO-1500'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-SP-CO-1500']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id less than 269552.61.",
+    "pandas_code": "df[df['Claim_Id'] < 269552.61]"
+  },
+  {
+    "Query": "Find claims where Policy Number contains 'BGDP21'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDP21', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount at least 156.69.",
+    "pandas_code": "df[df['Approved_Amount'] >= 156.69]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount equal to 169.76.",
+    "pandas_code": "df[df['Claim_Amount'] == 169.76]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2023-04-10.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-10')]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2023-07-09.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-07-09')]"
+  },
+  {
+    "Query": "Show claims where Claim Id at most 262573.76.",
+    "pandas_code": "df[df['Claim_Id'] <= 262573.76]"
+  },
+  {
+    "Query": "Provide claims where Claim Number is 'CBGDP23037990-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23037990-00']"
+  },
+  {
+    "Query": "Display claims where Scheme contains 'SME 1-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 1-', na=False)]"
+  },
+  {
+    "Query": "Find claims where Status Code is 'CV0'.",
+    "pandas_code": "df[df['Status_Code'] == 'CV0']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2023-05-10 and 2023-05-15.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-05-10')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-15'))]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2023-02-26.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-26')]"
+  },
+  {
+    "Query": "List claims where Status Code is 'PY1'.",
+    "pandas_code": "df[df['Status_Code'] == 'PY1']"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2022-11-23.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-23')]"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name is 'Ms Yu Liu'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Yu Liu']"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2023-01-08 and 2023-01-18.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-08')) & (df['Loss_Date'] <= pd.to_datetime('2023-01-18'))]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-04-05 and 2023-06-04.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-04-05')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-04'))]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 685 and 1287.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 685.01) & (df['Claim_Amount'] <= 1287.44)]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2022-12-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-12-16')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-01-04 and 2023-02-15.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-04')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-02-15'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 269474 and 271169.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269474.78) & (df['Claim_Id'] <= 271169.82)]"
+  },
+  {
+    "Query": "List claims where Claim Number is 'CBGDP23043911-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23043911-00']"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2022-11-22.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-22')]"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 127 and 1470.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 127.79) & (df['Approved_Amount'] <= 1470.73)]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-03-17.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-17')]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount at most 978.1.",
+    "pandas_code": "df[df['Claim_Amount'] <= 978.1]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount at most 401.59.",
+    "pandas_code": "df[df['Approved_Amount'] <= 401.59]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-04-12.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-12')]"
+  },
+  {
+    "Query": "Find claims where Claim Id at least 270763.65.",
+    "pandas_code": "df[df['Claim_Id'] >= 270763.65]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-03-21.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-21')]"
+  },
+  {
+    "Query": "Provide claims where Status is 'Void'.",
+    "pandas_code": "df[df['Status'] == 'Void']"
+  },
+  {
+    "Query": "Provide claims where Provider Code is 'PC104116'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104116']"
+  },
+  {
+    "Query": "Display claims where Claim Id at most 267699.05.",
+    "pandas_code": "df[df['Claim_Id'] <= 267699.05]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-07-02.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-02')]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2022-12-21.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-21')]"
+  },
+  {
+    "Query": "Show claims where Provider Code is 'PC104077'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104077']"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2023-02-18 and 2023-07-07.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-18')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-07'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2022-12-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-15')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-01-23 and 2023-07-08.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-23')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-08'))]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2022-11-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-11-12')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount at most 1226.14.",
+    "pandas_code": "df[df['Claim_Amount'] <= 1226.14]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2022-11-21 and 2023-06-13.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-21')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-13'))]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2022-12-13 and 2023-02-28.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-13')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-02-28'))]"
+  },
+  {
+    "Query": "Display claims where Loss Date on 2023-04-05.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-05')]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-06-15.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-06-15')]"
+  },
+  {
+    "Query": "Provide claims where Claim Number is 'CBGDC23069778-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23069778-00']"
+  },
+  {
+    "Query": "Fetch claims where Provider Name contains 'Jurong'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Jurong', na=False)]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 706 and 963.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 706.74) & (df['Approved_Amount'] <= 963.44)]"
+  },
+  {
+    "Query": "Fetch claims where Deduction Amount greater than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] > 0.0]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2023-01-31.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-31')]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-04-28.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-28')]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-05-15.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-15')]"
+  },
+  {
+    "Query": "Get claims where Deduction Amount less than 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] < 0.0]"
+  },
+  {
+    "Query": "Provide claims where Claim Id greater than 262361.15.",
+    "pandas_code": "df[df['Claim_Id'] > 262361.15]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-03-20 and 2023-03-20.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-20')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-20'))]"
+  },
+  {
+    "Query": "Fetch claims where Policy Number contains 'BGDC21'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDC21', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount greater than 663.69.",
+    "pandas_code": "df[df['Approved_Amount'] > 663.69]"
+  },
+  {
+    "Query": "Display claims where Approved Amount at most 5.31.",
+    "pandas_code": "df[df['Approved_Amount'] <= 5.31]"
+  },
+  {
+    "Query": "Show claims where Loss Type is 'Hospitalisation'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Hospitalisation']"
+  },
+  {
+    "Query": "Get claims where Approved Amount less than 495.82.",
+    "pandas_code": "df[df['Approved_Amount'] < 495.82]"
+  },
+  {
+    "Query": "Fetch claims where Provider Code is 'PC101668'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101668']"
+  },
+  {
+    "Query": "Show claims where Approved Amount at least 1159.22.",
+    "pandas_code": "df[df['Approved_Amount'] >= 1159.22]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-02-03.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-02-03')]"
+  },
+  {
+    "Query": "Find claims where Provider Name contains 'Parkwa'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Parkwa', na=False)]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-05-13.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-13')]"
+  },
+  {
+    "Query": "Find claims where Patient Name is 'Mr Zong Luck Chua'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Zong Luck Chua']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-04-01.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-01')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date on 2022-11-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-28')]"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 30 and 1196.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 30.68) & (df['Claim_Amount'] <= 1196.94)]"
+  },
+  {
+    "Query": "Get claims where Claim Id equal to 271026.84.",
+    "pandas_code": "df[df['Claim_Id'] == 271026.84]"
+  },
+  {
+    "Query": "Fetch claims where Provider Name contains 'Bukit '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Bukit ', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 269246 and 277051.",
+    "pandas_code": "df[(df['Claim_Id'] >= 269246.69) & (df['Claim_Id'] <= 277051.24)]"
+  },
+  {
+    "Query": "Show claims where Approved Amount at least 1052.34.",
+    "pandas_code": "df[df['Approved_Amount'] >= 1052.34]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2022-11-14 and 2023-05-30.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-14')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-30'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Number is 'CBGDC23061564-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23061564-00']"
+  },
+  {
+    "Query": "Provide claims where Status Code is 'PY3'.",
+    "pandas_code": "df[df['Status_Code'] == 'PY3']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-03-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-23')]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 603 and 884.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 603.14) & (df['Approved_Amount'] <= 884.46)]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2022-12-02.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-02')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-02-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-27')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-03-08 and 2023-05-07.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-08')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-07'))]"
+  },
+  {
+    "Query": "Show claims where Status is 'Paid'.",
+    "pandas_code": "df[df['Status'] == 'Paid']"
+  },
+  {
+    "Query": "List claims where Status is 'Paid'.",
+    "pandas_code": "df[df['Status'] == 'Paid']"
+  },
+  {
+    "Query": "List claims where Claim Id between 268663 and 279593.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268663.6) & (df['Claim_Id'] <= 279593.27)]"
+  },
+  {
+    "Query": "List claims where Claim Id at most 278274.8.",
+    "pandas_code": "df[df['Claim_Id'] <= 278274.8]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount greater than 1093.68.",
+    "pandas_code": "df[df['Approved_Amount'] > 1093.68]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 618 and 1559.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 618.6) & (df['Claim_Amount'] <= 1559.68)]"
+  },
+  {
+    "Query": "Display claims where Claim Type contains 'CHS'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'CHS', na=False)]"
+  },
+  {
+    "Query": "Find claims where Claim Number is 'CBGDP23043911-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23043911-00']"
+  },
+  {
+    "Query": "Fetch claims where Claim Id equal to 264222.04.",
+    "pandas_code": "df[df['Claim_Id'] == 264222.04]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 761 and 1005.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 761.53) & (df['Claim_Amount'] <= 1005.06)]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2022-12-29.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-29')]"
+  },
+  {
+    "Query": "Show claims where Claim Amount less than 1556.7.",
+    "pandas_code": "df[df['Claim_Amount'] < 1556.7]"
+  },
+  {
+    "Query": "Get claims where Product contains 'Hospit'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Hospit', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id at least 263845.28.",
+    "pandas_code": "df[df['Claim_Id'] >= 263845.28]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-02-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-02-28')]"
+  },
+  {
+    "Query": "Get claims where Provider Name is 'Other Overseas Dental Clinic'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Other Overseas Dental Clinic']"
+  },
+  {
+    "Query": "List claims where Approved Amount between 641 and 1253.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 641.03) & (df['Approved_Amount'] <= 1253.83)]"
+  },
+  {
+    "Query": "Show claims where Patient Name is 'Mr Malvin Fedyano'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Malvin Fedyano']"
+  },
+  {
+    "Query": "Provide claims where Policy Number is 'BGDC211000269-02-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000269-02-000']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2022-11-23 and 2023-03-29.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-23')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-29'))]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount greater than 341.8.",
+    "pandas_code": "df[df['Approved_Amount'] > 341.8]"
+  },
+  {
+    "Query": "Find claims where Approved Amount greater than 514.93.",
+    "pandas_code": "df[df['Approved_Amount'] > 514.93]"
+  },
+  {
+    "Query": "Show claims where Claim Id between 267667 and 279703.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267667.22) & (df['Claim_Id'] <= 279703.78)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 531 and 934.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 531.88) & (df['Claim_Amount'] <= 934.86)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 270122 and 271959.",
+    "pandas_code": "df[(df['Claim_Id'] >= 270122.17) & (df['Claim_Id'] <= 271959.69)]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-06-03.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-03')]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 631 and 1186.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 631.17) & (df['Approved_Amount'] <= 1186.84)]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 520 and 1197.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 520.81) & (df['Approved_Amount'] <= 1197.97)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-01-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-13')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2023-04-22.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-22')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2023-06-24 and 2023-07-26.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-06-24')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-26'))]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-04-07.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-07')]"
+  },
+  {
+    "Query": "Show claims where Claim Id at least 272458.82.",
+    "pandas_code": "df[df['Claim_Id'] >= 272458.82]"
+  },
+  {
+    "Query": "Get claims where Provider Name contains 'TAN TO'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'TAN TO', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Id between 263047 and 276074.",
+    "pandas_code": "df[(df['Claim_Id'] >= 263047.71) & (df['Claim_Id'] <= 276074.01)]"
+  },
+  {
+    "Query": "Show claims where Policy Number is 'BGDC221000044-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC221000044-01-000']"
+  },
+  {
+    "Query": "Fetch claims where Scheme contains 'Per Di'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'Per Di', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Provider Code is 'OTH0000001'.",
+    "pandas_code": "df[df['Provider_Code'] == 'OTH0000001']"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2022-11-08.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-08')]"
+  },
+  {
+    "Query": "Get claims where Policy Number contains 'BGDP22'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDP22', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2023-03-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-28')]"
+  },
+  {
+    "Query": "Find claims where Claim Id between 266994 and 277636.",
+    "pandas_code": "df[(df['Claim_Id'] >= 266994.28) & (df['Claim_Id'] <= 277636.86)]"
+  },
+  {
+    "Query": "Find claims where Policy Number contains 'BGDC22'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDC22', na=False)]"
+  },
+  {
+    "Query": "Show claims where Status contains 'Void'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Void', na=False)]"
+  },
+  {
+    "Query": "Show claims where Deduction Amount at most 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] <= 0.0]"
+  },
+  {
+    "Query": "Provide claims where Provider Code contains 'PC1037'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1037', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2022-11-20 and 2023-01-07.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-20')) & (df['Loss_Date'] <= pd.to_datetime('2023-01-07'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 262394 and 278697.",
+    "pandas_code": "df[(df['Claim_Id'] >= 262394.86) & (df['Claim_Id'] <= 278697.01)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2023-07-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-28')]"
+  },
+  {
+    "Query": "Display claims where Policy Number is 'BGDP221000371-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000371-01-000']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2023-02-20.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-02-20')]"
+  },
+  {
+    "Query": "Get claims where Scheme contains 'SME 2-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 2-', na=False)]"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 445 and 1236.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 445.64) & (df['Approved_Amount'] <= 1236.99)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-06-04 and 2023-07-13.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-06-04')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-13'))]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2023-07-15.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-07-15')]"
+  },
+  {
+    "Query": "Provide claims where Claim Id less than 271812.62.",
+    "pandas_code": "df[df['Claim_Id'] < 271812.62]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2023-03-17 and 2023-06-06.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-17')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-06'))]"
+  },
+  {
+    "Query": "List claims where Provider Name is 'Guang jun tcm clinic'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Guang jun tcm clinic']"
+  },
+  {
+    "Query": "Show claims where Provider Name contains 'Q & M '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Q & M ', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2022-12-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-15')]"
+  },
+  {
+    "Query": "Find claims where Provider Name contains 'NTUC D'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'NTUC D', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 445 and 1107.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 445.68) & (df['Approved_Amount'] <= 1107.19)]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount between 762 and 1391.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 762.87) & (df['Claim_Amount'] <= 1391.0)]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2022-11-08.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-08')]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2023-04-05.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-05')]"
+  },
+  {
+    "Query": "Get claims where Claim Id between 266702 and 271555.",
+    "pandas_code": "df[(df['Claim_Id'] >= 266702.3) & (df['Claim_Id'] <= 271555.12)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2022-12-08.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-08')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 267343 and 275410.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267343.39) & (df['Claim_Id'] <= 275410.04)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date between 2023-03-09 and 2023-06-06.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-09')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-06'))]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-07-10.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-07-10')]"
+  },
+  {
+    "Query": "Provide claims where Patient Name contains 'Mr Lin'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Lin', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Policy Number is 'BGDP221001107-00-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221001107-00-000']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-04-22.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-22')]"
+  },
+  {
+    "Query": "Find claims where Deduction Amount at least 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] >= 0.0]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2023-07-06.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-06')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2022-12-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-12-04')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2022-11-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-11-28')]"
+  },
+  {
+    "Query": "Fetch claims where Scheme is 'SME 1-1000-NonPanel'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-1000-NonPanel']"
+  },
+  {
+    "Query": "Fetch claims where Policy Number is 'BGDC211000300-02-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000300-02-000']"
+  },
+  {
+    "Query": "Get claims where Provider Name is 'Physio Down Under Pte ltd'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Physio Down Under Pte ltd']"
+  },
+  {
+    "Query": "Retrieve claims where Product is 'Group Hospital and Surgical'.",
+    "pandas_code": "df[df['Product'] == 'Group Hospital and Surgical']"
+  },
+  {
+    "Query": "Get claims where Patient Name contains 'Ms Lay'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Lay', na=False)]"
+  },
+  {
+    "Query": "Find claims where Claim Id less than 263636.51.",
+    "pandas_code": "df[df['Claim_Id'] < 263636.51]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount greater than 529.39.",
+    "pandas_code": "df[df['Approved_Amount'] > 529.39]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-02-06.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-02-06')]"
+  },
+  {
+    "Query": "Find claims where Loss Type is 'Specialist Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Specialist Treatment']"
+  },
+  {
+    "Query": "Show claims where Product contains 'Genera'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Genera', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Policy Number is 'BGDP221000331-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000331-01-000']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2023-05-01.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-01')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date after 2023-02-05.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-02-05')]"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 716 and 1428.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 716.45) & (df['Approved_Amount'] <= 1428.99)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 784 and 837.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 784.58) & (df['Claim_Amount'] <= 837.03)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-05-10.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-10')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2023-05-31.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-31')]"
+  },
+  {
+    "Query": "List claims where Loss Date before 2023-03-06.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-03-06')]"
+  },
+  {
+    "Query": "List claims where Loss Date after 2023-04-21.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-21')]"
+  },
+  {
+    "Query": "Display claims where Loss Date on 2023-03-16.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-16')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2022-11-30 and 2023-04-25.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-30')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-04-25'))]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-01-10.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-10')]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 418 and 1207.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 418.85) & (df['Approved_Amount'] <= 1207.41)]"
+  },
+  {
+    "Query": "Find claims where Approved Amount at least 502.65.",
+    "pandas_code": "df[df['Approved_Amount'] >= 502.65]"
+  },
+  {
+    "Query": "List claims where Claim Created Date before 2023-04-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-19')]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-05-22.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-22')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 707 and 938.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 707.52) & (df['Claim_Amount'] <= 938.37)]"
+  },
+  {
+    "Query": "Show claims where Patient Name is 'Mr Kelvin Law'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Kelvin Law']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 268384 and 278135.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268384.39) & (df['Claim_Id'] <= 278135.85)]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-05-29.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-29')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2022-12-11 and 2023-01-29.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-11')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-01-29'))]"
+  },
+  {
+    "Query": "List claims where Loss Type contains 'Dental'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Dental', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Patient Name is 'Ms Cindy Leng Leng Koh'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Cindy Leng Leng Koh']"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2023-04-15.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-15')]"
+  },
+  {
+    "Query": "List claims where Provider Name is 'Other Overseas Hospital'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Other Overseas Hospital']"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-05-12.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-12')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-03-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-28')]"
+  },
+  {
+    "Query": "List claims where Approved Amount greater than 594.67.",
+    "pandas_code": "df[df['Approved_Amount'] > 594.67]"
+  },
+  {
+    "Query": "List claims where Loss Date after 2022-11-15.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-15')]"
+  },
+  {
+    "Query": "Show claims where Approved Amount at most 779.58.",
+    "pandas_code": "df[df['Approved_Amount'] <= 779.58]"
+  },
+  {
+    "Query": "Display claims where Claim Id equal to 277685.19.",
+    "pandas_code": "df[df['Claim_Id'] == 277685.19]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2023-05-19 and 2023-07-17.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-05-19')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-17'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2023-02-20.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-20')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date before 2022-12-15.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-15')]"
+  },
+  {
+    "Query": "Display claims where Policy Number is 'BGDP221000592-00-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000592-00-000']"
+  },
+  {
+    "Query": "Provide claims where Scheme is 'Per Disability Template 1'.",
+    "pandas_code": "df[df['Scheme'] == 'Per Disability Template 1']"
+  },
+  {
+    "Query": "List claims where Loss Date before 2022-12-23.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-23')]"
+  },
+  {
+    "Query": "List claims where Provider Name contains 'Bukit '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Bukit ', na=False)]"
+  },
+  {
+    "Query": "List claims where Provider Code is 'PC100731'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100731']"
+  },
+  {
+    "Query": "Get claims where Product is 'Specialist'.",
+    "pandas_code": "df[df['Product'] == 'Specialist']"
+  },
+  {
+    "Query": "Get claims where Claim Id between 267958 and 273978.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267958.55) & (df['Claim_Id'] <= 273978.3)]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-05-20.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-20')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2022-12-31 and 2023-07-27.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-31')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-27'))]"
+  },
+  {
+    "Query": "Find claims where Scheme is 'Per Disability Template 2'.",
+    "pandas_code": "df[df['Scheme'] == 'Per Disability Template 2']"
+  },
+  {
+    "Query": "Provide claims where Loss Type contains 'Hospit'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Hospit', na=False)]"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2023-04-15 and 2023-07-16.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-04-15')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-16'))]"
+  },
+  {
+    "Query": "Fetch claims where Patient Name contains 'Ms Jia'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Jia', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Policy Number contains 'BGDP22'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDP22', na=False)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date before 2023-04-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-13')]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 70 and 1562.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 70.51) & (df['Claim_Amount'] <= 1562.84)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2023-07-06.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-06')]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 97 and 1564.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 97.61) & (df['Claim_Amount'] <= 1564.79)]"
+  },
+  {
+    "Query": "Find claims where Approved Amount greater than 1337.09.",
+    "pandas_code": "df[df['Approved_Amount'] > 1337.09]"
+  },
+  {
+    "Query": "Retrieve claims where Product is 'Group Specialist'.",
+    "pandas_code": "df[df['Product'] == 'Group Specialist']"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount greater than 353.09.",
+    "pandas_code": "df[df['Approved_Amount'] > 353.09]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount equal to 18.45.",
+    "pandas_code": "df[df['Claim_Amount'] == 18.45]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 43 and 850.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 43.6) & (df['Claim_Amount'] <= 850.51)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id at most 266940.73.",
+    "pandas_code": "df[df['Claim_Id'] <= 266940.73]"
+  },
+  {
+    "Query": "Find claims where Loss Date between 2023-05-25 and 2023-06-11.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-05-25')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-11'))]"
+  },
+  {
+    "Query": "Get claims where Claim Id between 268573 and 278276.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268573.59) & (df['Claim_Id'] <= 278276.39)]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-05-11.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-11')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Type is 'Reimbursement'.",
+    "pandas_code": "df[df['Claim_Type'] == 'Reimbursement']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2023-06-22.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-22')]"
+  },
+  {
+    "Query": "Provide claims where Patient Name contains 'Ms Pea'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Pea', na=False)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date on 2023-06-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-06-13')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2022-11-17 and 2022-12-12.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-17')) & (df['Claim_Created_Date'] <= pd.to_datetime('2022-12-12'))]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2023-02-15.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-02-15')]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name is 'ADS CLINIC'.",
+    "pandas_code": "df[df['Provider_Name'] == 'ADS CLINIC']"
+  },
+  {
+    "Query": "Retrieve claims where Status is 'Rejected'.",
+    "pandas_code": "df[df['Status'] == 'Rejected']"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 152 and 1410.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 152.69) & (df['Claim_Amount'] <= 1410.24)]"
+  },
+  {
+    "Query": "Provide claims where Provider Name is 'Manadr Pte Ltd'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Manadr Pte Ltd']"
+  },
+  {
+    "Query": "Fetch claims where Provider Code contains 'PC1016'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1016', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2022-12-21 and 2023-01-25.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-21')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-01-25'))]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date between 2023-01-18 and 2023-06-08.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-18')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-08'))]"
+  },
+  {
+    "Query": "Display claims where Provider Code contains 'PC1016'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1016', na=False)]"
+  },
+  {
+    "Query": "Show claims where Loss Date before 2023-02-04.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-04')]"
+  },
+  {
+    "Query": "Find claims where Product is 'Specialist'.",
+    "pandas_code": "df[df['Product'] == 'Specialist']"
+  },
+  {
+    "Query": "Find claims where Provider Code is 'PC102812'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC102812']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-07-24.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-24')]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 397 and 1513.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 397.12) & (df['Claim_Amount'] <= 1513.13)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount equal to 656.62.",
+    "pandas_code": "df[df['Claim_Amount'] == 656.62]"
+  },
+  {
+    "Query": "Show claims where Status Code is 'CV0'.",
+    "pandas_code": "df[df['Status_Code'] == 'CV0']"
+  },
+  {
+    "Query": "Display claims where Provider Code is 'PC103209'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103209']"
+  },
+  {
+    "Query": "Display claims where Claim Amount at most 40.8.",
+    "pandas_code": "df[df['Claim_Amount'] <= 40.8]"
+  },
+  {
+    "Query": "List claims where Claim Created Date on 2023-07-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-07-19')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2022-11-21.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-21')]"
+  },
+  {
+    "Query": "Fetch claims where Patient Name contains 'Mudita'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mudita', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Provider Name contains 'Other '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Other ', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Number is 'CBGDC23070602-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23070602-00']"
+  },
+  {
+    "Query": "Get claims where Claim Amount equal to 524.29.",
+    "pandas_code": "df[df['Claim_Amount'] == 524.29]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2023-04-25.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-25')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2022-11-27.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-27')]"
+  },
+  {
+    "Query": "List claims where Claim Id less than 266361.32.",
+    "pandas_code": "df[df['Claim_Id'] < 266361.32]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2023-01-14 and 2023-05-24.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-14')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-24'))]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2023-01-17.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-17')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2022-11-19 and 2023-02-24.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-19')) & (df['Loss_Date'] <= pd.to_datetime('2023-02-24'))]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 650 and 1110.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 650.7) & (df['Approved_Amount'] <= 1110.46)]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2023-05-21.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-21')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2022-12-16 and 2023-02-13.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-16')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-02-13'))]"
+  },
+  {
+    "Query": "Display claims where Provider Code is 'PC100001'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "Provide claims where Patient Name contains 'Mr Cho'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Cho', na=False)]"
+  },
+  {
+    "Query": "Show claims where Patient Name is 'Ms Chor Hoon Ng'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Chor Hoon Ng']"
+  },
+  {
+    "Query": "Display claims where Patient Name is 'Ms Chui Hoon Toh'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Chui Hoon Toh']"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 324 and 930.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 324.33) & (df['Approved_Amount'] <= 930.53)]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-04-03 and 2023-06-24.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-04-03')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-24'))]"
+  },
+  {
+    "Query": "List claims where Claim Number is 'CBGDP23037704-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23037704-00']"
+  },
+  {
+    "Query": "Retrieve claims where Provider Code is 'PC100001'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2023-04-05.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-05')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id less than 279230.91.",
+    "pandas_code": "df[df['Claim_Id'] < 279230.91]"
+  },
+  {
+    "Query": "Provide claims where Claim Id between 268602 and 270949.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268602.56) & (df['Claim_Id'] <= 270949.65)]"
+  },
+  {
+    "Query": "List claims where Scheme contains 'SME 2-'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'SME 2-', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2023-02-28.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-28')]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-06-16.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-06-16')]"
+  },
+  {
+    "Query": "Find claims where Status is 'Void'.",
+    "pandas_code": "df[df['Status'] == 'Void']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2023-05-14.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-14')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2023-01-03 and 2023-06-24.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-03')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-24'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount at least 1495.82.",
+    "pandas_code": "df[df['Claim_Amount'] >= 1495.82]"
+  },
+  {
+    "Query": "List claims where Claim Created Date on 2023-02-07.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-02-07')]"
+  },
+  {
+    "Query": "Get claims where Claim Number is 'CBGDC23061810-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23061810-00']"
+  },
+  {
+    "Query": "Show claims where Approved Amount at least 203.98.",
+    "pandas_code": "df[df['Approved_Amount'] >= 203.98]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2023-01-16 and 2023-06-27.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-16')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-27'))]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2022-11-11.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-11')]"
+  },
+  {
+    "Query": "Show claims where Patient Name contains 'Ms Jia'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Jia', na=False)]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-04-04.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-04')]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2023-02-16 and 2023-07-09.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-16')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-09'))]"
+  },
+  {
+    "Query": "Find claims where Claim Id at least 262169.34.",
+    "pandas_code": "df[df['Claim_Id'] >= 262169.34]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-06-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-19')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2023-05-19.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-19')]"
+  },
+  {
+    "Query": "Get claims where Provider Name is 'KANDANG KERBAU HOSPITAL (KK Womens and Childrens Hospital)'.",
+    "pandas_code": "df[df['Provider_Name'] == 'KANDANG KERBAU HOSPITAL (KK Womens and Childrens Hospital)']"
+  },
+  {
+    "Query": "Find claims where Scheme is 'New Template 1'.",
+    "pandas_code": "df[df['Scheme'] == 'New Template 1']"
+  },
+  {
+    "Query": "Show claims where Loss Date between 2022-12-22 and 2023-07-23.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-22')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-23'))]"
+  },
+  {
+    "Query": "Find claims where Scheme is 'SME 2-SP-1000'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 2-SP-1000']"
+  },
+  {
+    "Query": "Show claims where Claim Amount greater than 1481.6.",
+    "pandas_code": "df[df['Claim_Amount'] > 1481.6]"
+  },
+  {
+    "Query": "Get claims where Loss Date after 2023-03-26.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-26')]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 661 and 1191.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 661.68) & (df['Approved_Amount'] <= 1191.89)]"
+  },
+  {
+    "Query": "Retrieve claims where Product contains 'Specia'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Specia', na=False)]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2022-12-27.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-27')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Number is 'CBGDP23038561-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23038561-00']"
+  },
+  {
+    "Query": "Get claims where Approved Amount greater than 1299.02.",
+    "pandas_code": "df[df['Approved_Amount'] > 1299.02]"
+  },
+  {
+    "Query": "Get claims where Policy Number contains 'BGDP21'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDP21', na=False)]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 547 and 1160.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 547.8) & (df['Approved_Amount'] <= 1160.15)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2023-02-06.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-02-06')]"
+  },
+  {
+    "Query": "Display claims where Claim Amount at most 33.24.",
+    "pandas_code": "df[df['Claim_Amount'] <= 33.24]"
+  },
+  {
+    "Query": "Find claims where Provider Code is 'PC104073'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104073']"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 592 and 1255.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 592.01) & (df['Approved_Amount'] <= 1255.87)]"
+  },
+  {
+    "Query": "Show claims where Claim Id between 268171 and 271268.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268171.82) & (df['Claim_Id'] <= 271268.91)]"
+  },
+  {
+    "Query": "Get claims where Claim Id between 268619 and 271223.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268619.21) & (df['Claim_Id'] <= 271223.97)]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 52 and 1031.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 52.77) & (df['Approved_Amount'] <= 1031.44)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2022-12-04 and 2022-12-30.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-04')) & (df['Claim_Created_Date'] <= pd.to_datetime('2022-12-30'))]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 539 and 1226.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 539.02) & (df['Approved_Amount'] <= 1226.28)]"
+  },
+  {
+    "Query": "Provide claims where Claim Id between 265102 and 273602.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265102.08) & (df['Claim_Id'] <= 273602.05)]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 656 and 1173.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 656.04) & (df['Approved_Amount'] <= 1173.81)]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-05-15 and 2023-06-15.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-05-15')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-15'))]"
+  },
+  {
+    "Query": "Fetch claims where Provider Code is 'PC104076'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104076']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-01-29.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-29')]"
+  },
+  {
+    "Query": "List claims where Patient Name is 'Ms Jia Min Neo'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Jia Min Neo']"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 245 and 1211.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 245.87) & (df['Approved_Amount'] <= 1211.09)]"
+  },
+  {
+    "Query": "Show claims where Claim Amount at least 1140.36.",
+    "pandas_code": "df[df['Claim_Amount'] >= 1140.36]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount between 557 and 1074.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 557.09) & (df['Approved_Amount'] <= 1074.8)]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 421 and 885.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 421.13) & (df['Claim_Amount'] <= 885.25)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount less than 485.8.",
+    "pandas_code": "df[df['Approved_Amount'] < 485.8]"
+  },
+  {
+    "Query": "Provide claims where Claim Id between 265230 and 270637.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265230.99) & (df['Claim_Id'] <= 270637.75)]"
+  },
+  {
+    "Query": "List claims where Provider Code contains 'PC1040'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1040', na=False)]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-01-31.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-01-31')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Number is 'CBGDC23060217-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23060217-00']"
+  },
+  {
+    "Query": "List claims where Scheme is 'SME 3-SP-Panel Only'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 3-SP-Panel Only']"
+  },
+  {
+    "Query": "Retrieve claims where Status is 'Void'.",
+    "pandas_code": "df[df['Status'] == 'Void']"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name is 'Other TCM Clinic'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Other TCM Clinic']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2022-11-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-11-24')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date on 2023-07-07.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-07-07')]"
+  },
+  {
+    "Query": "Retrieve claims where Product is 'Hospital & Surgical'.",
+    "pandas_code": "df[df['Product'] == 'Hospital & Surgical']"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name is 'Mr Kelvin Joon Thoe Hui'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Kelvin Joon Thoe Hui']"
+  },
+  {
+    "Query": "List claims where Approved Amount at least 156.2.",
+    "pandas_code": "df[df['Approved_Amount'] >= 156.2]"
+  },
+  {
+    "Query": "Get claims where Claim Id between 268308 and 277676.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268308.66) & (df['Claim_Id'] <= 277676.01)]"
+  },
+  {
+    "Query": "Provide claims where Claim Id between 265295 and 274872.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265295.45) & (df['Claim_Id'] <= 274872.4)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2023-01-11.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-11')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id at least 265026.95.",
+    "pandas_code": "df[df['Claim_Id'] >= 265026.95]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2022-11-17.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-17')]"
+  },
+  {
+    "Query": "Show claims where Patient Name is 'Mr Shoon Peng'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Shoon Peng']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-01-12 and 2023-03-26.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-12')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-26'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 506 and 915.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 506.18) & (df['Claim_Amount'] <= 915.51)]"
+  },
+  {
+    "Query": "Show claims where Loss Date after 2023-03-06.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-06')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 265636 and 275484.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265636.9) & (df['Claim_Id'] <= 275484.77)]"
+  },
+  {
+    "Query": "Show claims where Claim Amount at least 1252.1.",
+    "pandas_code": "df[df['Claim_Amount'] >= 1252.1]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 263867 and 272103.",
+    "pandas_code": "df[(df['Claim_Id'] >= 263867.56) & (df['Claim_Id'] <= 272103.69)]"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2022-12-09.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-09')]"
+  },
+  {
+    "Query": "Display claims where Scheme is 'SME 3-SP-Panel Only'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 3-SP-Panel Only']"
+  },
+  {
+    "Query": "Show claims where Provider Name contains 'NATION'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'NATION', na=False)]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2023-03-18.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-03-18')]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 188 and 820.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 188.31) & (df['Approved_Amount'] <= 820.38)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2023-02-08 and 2023-03-02.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-08')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-02'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Type contains 'Cashle'.",
+    "pandas_code": "df[df['Claim_Type'].str.contains(r'Cashle', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 380 and 835.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 380.91) & (df['Claim_Amount'] <= 835.71)]"
+  },
+  {
+    "Query": "Get claims where Claim Amount at least 122.84.",
+    "pandas_code": "df[df['Claim_Amount'] >= 122.84]"
+  },
+  {
+    "Query": "Fetch claims where Product contains 'Dental'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Dental', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 36 and 1571.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 36.17) & (df['Claim_Amount'] <= 1571.36)]"
+  },
+  {
+    "Query": "Show claims where Approved Amount at most 874.43.",
+    "pandas_code": "df[df['Approved_Amount'] <= 874.43]"
+  },
+  {
+    "Query": "List claims where Scheme contains 'Per Di'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'Per Di', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 90 and 1194.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 90.34) & (df['Claim_Amount'] <= 1194.77)]"
+  },
+  {
+    "Query": "Fetch claims where Policy Number contains 'BGDC22'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDC22', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 328 and 854.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 328.87) & (df['Claim_Amount'] <= 854.56)]"
+  },
+  {
+    "Query": "Find claims where Policy Number contains 'BGDP22'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDP22', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Amount at least 1251.73.",
+    "pandas_code": "df[df['Claim_Amount'] >= 1251.73]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date on 2023-02-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-02-12')]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name is 'Other Overseas Hospital'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Other Overseas Hospital']"
+  },
+  {
+    "Query": "Display claims where Claim Id between 267407 and 274147.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267407.55) & (df['Claim_Id'] <= 274147.85)]"
+  },
+  {
+    "Query": "Find claims where Approved Amount between 190 and 1238.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 190.67) & (df['Approved_Amount'] <= 1238.43)]"
+  },
+  {
+    "Query": "Get claims where Approved Amount at least 1193.73.",
+    "pandas_code": "df[df['Approved_Amount'] >= 1193.73]"
+  },
+  {
+    "Query": "Get claims where Claim Id between 266310 and 271658.",
+    "pandas_code": "df[(df['Claim_Id'] >= 266310.58) & (df['Claim_Id'] <= 271658.87)]"
+  },
+  {
+    "Query": "Find claims where Approved Amount at most 830.6.",
+    "pandas_code": "df[df['Approved_Amount'] <= 830.6]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2023-01-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-25')]"
+  },
+  {
+    "Query": "List claims where Claim Amount less than 703.85.",
+    "pandas_code": "df[df['Claim_Amount'] < 703.85]"
+  },
+  {
+    "Query": "Provide claims where Loss Date between 2023-05-20 and 2023-07-09.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-05-20')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-09'))]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2022-11-15 and 2023-02-27.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-15')) & (df['Loss_Date'] <= pd.to_datetime('2023-02-27'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 761 and 1375.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 761.94) & (df['Claim_Amount'] <= 1375.97)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2023-04-03.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-03')]"
+  },
+  {
+    "Query": "Find claims where Patient Name contains 'Mr Cal'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Cal', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2023-02-24 and 2023-05-07.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-24')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-05-07'))]"
+  },
+  {
+    "Query": "List claims where Loss Type is 'Specialist Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Specialist Treatment']"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-03-26.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-26')]"
+  },
+  {
+    "Query": "Get claims where Provider Name is 'Other TCM Clinic'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Other TCM Clinic']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-07-22.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-07-22')]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 332 and 1185.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 332.33) & (df['Approved_Amount'] <= 1185.26)]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount at most 845.25.",
+    "pandas_code": "df[df['Approved_Amount'] <= 845.25]"
+  },
+  {
+    "Query": "Display claims where Provider Code is 'PC103191'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC103191']"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 44 and 1065.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 44.13) & (df['Claim_Amount'] <= 1065.08)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date on 2023-03-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-19')]"
+  },
+  {
+    "Query": "Get claims where Provider Name contains 'Q & M '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Q & M ', na=False)]"
+  },
+  {
+    "Query": "Find claims where Status Code is 'PY3'.",
+    "pandas_code": "df[df['Status_Code'] == 'PY3']"
+  },
+  {
+    "Query": "Retrieve claims where Provider Name contains 'SINGAP'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'SINGAP', na=False)]"
+  },
+  {
+    "Query": "Get claims where Approved Amount greater than 216.47.",
+    "pandas_code": "df[df['Approved_Amount'] > 216.47]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2023-05-01.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-01')]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2022-12-03.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-12-03')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2023-06-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-06-28')]"
+  },
+  {
+    "Query": "Display claims where Scheme is 'Template 1'.",
+    "pandas_code": "df[df['Scheme'] == 'Template 1']"
+  },
+  {
+    "Query": "Retrieve claims where Status Code contains 'CV0'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'CV0', na=False)]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date on 2023-05-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-23')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date before 2023-05-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-24')]"
+  },
+  {
+    "Query": "Fetch claims where Product contains 'Hospit'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Hospit', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id equal to 266143.93.",
+    "pandas_code": "df[df['Claim_Id'] == 266143.93]"
+  },
+  {
+    "Query": "Display claims where Patient Name is 'Ms Fong Peng Annie Chew'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Fong Peng Annie Chew']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2023-01-01.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-01-01')]"
+  },
+  {
+    "Query": "Find claims where Patient Name is 'Ms Quinn Yanxi Lau'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Quinn Yanxi Lau']"
+  },
+  {
+    "Query": "Get claims where Claim Type is 'CHS'.",
+    "pandas_code": "df[df['Claim_Type'] == 'CHS']"
+  },
+  {
+    "Query": "List claims where Approved Amount less than 417.38.",
+    "pandas_code": "df[df['Approved_Amount'] < 417.38]"
+  },
+  {
+    "Query": "Get claims where Claim Amount at least 1230.97.",
+    "pandas_code": "df[df['Claim_Amount'] >= 1230.97]"
+  },
+  {
+    "Query": "Provide claims where Status is 'Paid'.",
+    "pandas_code": "df[df['Status'] == 'Paid']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2022-11-08.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-08')]"
+  },
+  {
+    "Query": "Get claims where Claim Number is 'CBGDP23037782-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDP23037782-00']"
+  },
+  {
+    "Query": "List claims where Status contains 'Reject'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Reject', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Amount equal to 240.28.",
+    "pandas_code": "df[df['Claim_Amount'] == 240.28]"
+  },
+  {
+    "Query": "Show claims where Approved Amount equal to 203.41.",
+    "pandas_code": "df[df['Approved_Amount'] == 203.41]"
+  },
+  {
+    "Query": "Display claims where Approved Amount greater than 326.77.",
+    "pandas_code": "df[df['Approved_Amount'] > 326.77]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2022-12-09 and 2023-05-13.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-09')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-13'))]"
+  },
+  {
+    "Query": "List claims where Patient Name contains 'Mr Jia'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Jia', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Policy Number is 'BGDC211000522-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000522-01-000']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date before 2023-07-19.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-19')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date after 2023-07-06.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-06')]"
+  },
+  {
+    "Query": "Retrieve claims where Status Code contains 'DU0'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'DU0', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2023-07-02.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-07-02')]"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name contains 'Mr Jia'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Jia', na=False)]"
+  },
+  {
+    "Query": "Get claims where Provider Name contains 'Changi'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Changi', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date before 2023-03-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-25')]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 604 and 1468.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 604.75) & (df['Approved_Amount'] <= 1468.97)]"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2023-06-07.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-07')]"
+  },
+  {
+    "Query": "Provide claims where Policy Number is 'BGDC211000256-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000256-01-000']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-03-05 and 2023-03-17.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-05')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-17'))]"
+  },
+  {
+    "Query": "Get claims where Claim Id between 262143 and 271262.",
+    "pandas_code": "df[(df['Claim_Id'] >= 262143.36) & (df['Claim_Id'] <= 271262.42)]"
+  },
+  {
+    "Query": "Get claims where Deduction Amount equal to 0.0.",
+    "pandas_code": "df[df['Deduction_Amount'] == 0.0]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2022-11-11 and 2023-05-07.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-11-11')) & (df['Loss_Date'] <= pd.to_datetime('2023-05-07'))]"
+  },
+  {
+    "Query": "Provide claims where Product contains 'Specia'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Specia', na=False)]"
+  },
+  {
+    "Query": "Find claims where Claim Id between 266716 and 272907.",
+    "pandas_code": "df[(df['Claim_Id'] >= 266716.53) & (df['Claim_Id'] <= 272907.81)]"
+  },
+  {
+    "Query": "Provide claims where Loss Date after 2023-05-06.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-06')]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 357 and 1248.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 357.0) & (df['Approved_Amount'] <= 1248.51)]"
+  },
+  {
+    "Query": "Show claims where Scheme contains 'Templa'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'Templa', na=False)]"
+  },
+  {
+    "Query": "Show claims where Patient Name is 'Mr Som Nath Kapoor'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Som Nath Kapoor']"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-02-02.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-02')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2023-07-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-15')]"
+  },
+  {
+    "Query": "List claims where Loss Date between 2023-03-01 and 2023-04-16.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-01')) & (df['Loss_Date'] <= pd.to_datetime('2023-04-16'))]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-02-04.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-04')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id at least 277064.17.",
+    "pandas_code": "df[df['Claim_Id'] >= 277064.17]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount less than 1137.0.",
+    "pandas_code": "df[df['Claim_Amount'] < 1137.0]"
+  },
+  {
+    "Query": "Provide claims where Scheme is 'SME 1-GP'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-GP']"
+  },
+  {
+    "Query": "Get claims where Claim Amount greater than 494.45.",
+    "pandas_code": "df[df['Claim_Amount'] > 494.45]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount greater than 55.58.",
+    "pandas_code": "df[df['Claim_Amount'] > 55.58]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2023-03-09.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-09')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2023-02-26.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-02-26')]"
+  },
+  {
+    "Query": "Display claims where Provider Code is 'PC101195'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101195']"
+  },
+  {
+    "Query": "Display claims where Claim Amount at least 224.34.",
+    "pandas_code": "df[df['Claim_Amount'] >= 224.34]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2022-11-17 and 2023-04-09.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-17')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-04-09'))]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date between 2023-02-04 and 2023-06-25.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-04')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-25'))]"
+  },
+  {
+    "Query": "Find claims where Patient Name contains 'Mr Tim'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Tim', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 738 and 1320.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 738.87) & (df['Claim_Amount'] <= 1320.13)]"
+  },
+  {
+    "Query": "List claims where Claim Amount less than 243.5.",
+    "pandas_code": "df[df['Claim_Amount'] < 243.5]"
+  },
+  {
+    "Query": "Get claims where Claim Amount at least 909.6.",
+    "pandas_code": "df[df['Claim_Amount'] >= 909.6]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id at least 271321.14.",
+    "pandas_code": "df[df['Claim_Id'] >= 271321.14]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 414 and 899.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 414.04) & (df['Approved_Amount'] <= 899.69)]"
+  },
+  {
+    "Query": "Find claims where Scheme contains 'New Te'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'New Te', na=False)]"
+  },
+  {
+    "Query": "List claims where Patient Name is 'Mr Shangxiang Yu'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Shangxiang Yu']"
+  },
+  {
+    "Query": "Provide claims where Policy Number is 'BGDC221000044-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC221000044-01-000']"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-07-16.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-16')]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2023-02-27.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-27')]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 88 and 878.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 88.19) & (df['Approved_Amount'] <= 878.0)]"
+  },
+  {
+    "Query": "Retrieve claims where Status Code is 'PY3'.",
+    "pandas_code": "df[df['Status_Code'] == 'PY3']"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2022-11-29.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-29')]"
+  },
+  {
+    "Query": "Get claims where Claim Id less than 264491.81.",
+    "pandas_code": "df[df['Claim_Id'] < 264491.81]"
+  },
+  {
+    "Query": "Find claims where Claim Amount at least 759.91.",
+    "pandas_code": "df[df['Claim_Amount'] >= 759.91]"
+  },
+  {
+    "Query": "Provide claims where Patient Name is 'Ms Aileen Grace Respicio'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Aileen Grace Respicio']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount at most 856.36.",
+    "pandas_code": "df[df['Claim_Amount'] <= 856.36]"
+  },
+  {
+    "Query": "Provide claims where Claim Id less than 277120.04.",
+    "pandas_code": "df[df['Claim_Id'] < 277120.04]"
+  },
+  {
+    "Query": "Display claims where Claim Id at most 279713.27.",
+    "pandas_code": "df[df['Claim_Id'] <= 279713.27]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2023-07-02.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-02')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2023-06-18.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-18')]"
+  },
+  {
+    "Query": "Show claims where Loss Date between 2023-01-15 and 2023-06-05.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-15')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-05'))]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2023-01-07 and 2023-03-28.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-07')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-28'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 322 and 991.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 322.65) & (df['Claim_Amount'] <= 991.06)]"
+  },
+  {
+    "Query": "Get claims where Claim Amount less than 819.45.",
+    "pandas_code": "df[df['Claim_Amount'] < 819.45]"
+  },
+  {
+    "Query": "Display claims where Claim Id at least 278897.76.",
+    "pandas_code": "df[df['Claim_Id'] >= 278897.76]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date after 2023-06-22.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-06-22')]"
+  },
+  {
+    "Query": "List claims where Approved Amount at least 1356.1.",
+    "pandas_code": "df[df['Approved_Amount'] >= 1356.1]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2022-12-19.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-19')]"
+  },
+  {
+    "Query": "Show claims where Status is 'Payment Approval'.",
+    "pandas_code": "df[df['Status'] == 'Payment Approval']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date on 2023-01-16.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-01-16')]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2023-07-23.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-23')]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-07-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-15')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-03-20.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-20')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id between 265428 and 273165.",
+    "pandas_code": "df[(df['Claim_Id'] >= 265428.25) & (df['Claim_Id'] <= 273165.91)]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 519 and 1148.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 519.52) & (df['Claim_Amount'] <= 1148.86)]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2022-11-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-27')]"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2023-01-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-23')]"
+  },
+  {
+    "Query": "Provide claims where Patient Name contains 'Mr Shi'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Shi', na=False)]"
+  },
+  {
+    "Query": "Get claims where Approved Amount greater than 1037.51.",
+    "pandas_code": "df[df['Approved_Amount'] > 1037.51]"
+  },
+  {
+    "Query": "List claims where Claim Id between 267073 and 271837.",
+    "pandas_code": "df[(df['Claim_Id'] >= 267073.63) & (df['Claim_Id'] <= 271837.78)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date on 2023-04-26.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-04-26')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-07-04 and 2023-07-16.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-07-04')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-16'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Id at most 279059.15.",
+    "pandas_code": "df[df['Claim_Id'] <= 279059.15]"
+  },
+  {
+    "Query": "Display claims where Approved Amount less than 1101.4.",
+    "pandas_code": "df[df['Approved_Amount'] < 1101.4]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 246 and 1044.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 246.88) & (df['Approved_Amount'] <= 1044.3)]"
+  },
+  {
+    "Query": "Get claims where Claim Id greater than 267878.36.",
+    "pandas_code": "df[df['Claim_Id'] > 267878.36]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2022-12-18.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-12-18')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-04-29.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-29')]"
+  },
+  {
+    "Query": "Get claims where Approved Amount equal to 1055.7.",
+    "pandas_code": "df[df['Approved_Amount'] == 1055.7]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount at most 611.21.",
+    "pandas_code": "df[df['Claim_Amount'] <= 611.21]"
+  },
+  {
+    "Query": "List claims where Status is 'Duplicate'.",
+    "pandas_code": "df[df['Status'] == 'Duplicate']"
+  },
+  {
+    "Query": "List claims where Claim Amount at most 397.54.",
+    "pandas_code": "df[df['Claim_Amount'] <= 397.54]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2022-11-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-11-27')]"
+  },
+  {
+    "Query": "Get claims where Approved Amount at most 338.62.",
+    "pandas_code": "df[df['Approved_Amount'] <= 338.62]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount between 576 and 1009.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 576.38) & (df['Claim_Amount'] <= 1009.72)]"
+  },
+  {
+    "Query": "Find claims where Status is 'Rejected'.",
+    "pandas_code": "df[df['Status'] == 'Rejected']"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2022-11-17.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-11-17')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount at most 590.23.",
+    "pandas_code": "df[df['Claim_Amount'] <= 590.23]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 651 and 1527.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 651.45) & (df['Claim_Amount'] <= 1527.46)]"
+  },
+  {
+    "Query": "List claims where Loss Date after 2022-11-23.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-23')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date on 2023-07-18.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-07-18')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2023-04-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-27')]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2022-11-17.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-17')]"
+  },
+  {
+    "Query": "Get claims where Provider Name contains 'Physio'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Physio', na=False)]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 469 and 1325.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 469.4) & (df['Approved_Amount'] <= 1325.62)]"
+  },
+  {
+    "Query": "Fetch claims where Provider Name contains 'Guang '.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'Guang ', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 156 and 1492.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 156.38) & (df['Approved_Amount'] <= 1492.11)]"
+  },
+  {
+    "Query": "Get claims where Scheme contains 'New Te'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'New Te', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2023-01-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-25')]"
+  },
+  {
+    "Query": "Find claims where Scheme is 'SME 1-GP'.",
+    "pandas_code": "df[df['Scheme'] == 'SME 1-GP']"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 531 and 1347.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 531.94) & (df['Approved_Amount'] <= 1347.91)]"
+  },
+  {
+    "Query": "Display claims where Approved Amount greater than 1518.46.",
+    "pandas_code": "df[df['Approved_Amount'] > 1518.46]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-03-08.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-08')]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 453 and 869.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 453.51) & (df['Approved_Amount'] <= 869.95)]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount at most 741.81.",
+    "pandas_code": "df[df['Approved_Amount'] <= 741.81]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 44 and 1083.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 44.55) & (df['Claim_Amount'] <= 1083.07)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount greater than 1585.83.",
+    "pandas_code": "df[df['Claim_Amount'] > 1585.83]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount between 538 and 1495.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 538.08) & (df['Approved_Amount'] <= 1495.33)]"
+  },
+  {
+    "Query": "Provide claims where Claim Amount at least 600.31.",
+    "pandas_code": "df[df['Claim_Amount'] >= 600.31]"
+  },
+  {
+    "Query": "Get claims where Claim Amount less than 46.46.",
+    "pandas_code": "df[df['Claim_Amount'] < 46.46]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date before 2023-01-13.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-01-13')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 314 and 1090.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 314.58) & (df['Claim_Amount'] <= 1090.62)]"
+  },
+  {
+    "Query": "Get claims where Claim Type is 'Reimbursement'.",
+    "pandas_code": "df[df['Claim_Type'] == 'Reimbursement']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2023-06-03.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-03')]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2023-05-20 and 2023-06-28.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-05-20')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-28'))]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2023-04-20.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-20')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date after 2023-02-23.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-02-23')]"
+  },
+  {
+    "Query": "Show claims where Loss Date before 2022-11-17.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-17')]"
+  },
+  {
+    "Query": "Find claims where Claim Id at most 272792.35.",
+    "pandas_code": "df[df['Claim_Id'] <= 272792.35]"
+  },
+  {
+    "Query": "List claims where Provider Code is 'PC104074'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104074']"
+  },
+  {
+    "Query": "Fetch claims where Status Code contains 'AP2'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'AP2', na=False)]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2022-12-17.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-17')]"
+  },
+  {
+    "Query": "Get claims where Product contains 'Dental'.",
+    "pandas_code": "df[df['Product'].str.contains(r'Dental', na=False)]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-01-06.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-06')]"
+  },
+  {
+    "Query": "Display claims where Provider Code is 'PC104116'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC104116']"
+  },
+  {
+    "Query": "Show claims where Claim Created Date before 2023-05-26.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-05-26')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-03-10.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-10')]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-05-07.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-07')]"
+  },
+  {
+    "Query": "Display claims where Loss Date after 2023-07-26.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-26')]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2022-12-09 and 2023-08-01.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-09')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-08-01'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-02-26 and 2023-07-07.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-26')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-07'))]"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name is 'Mr Yin Hoi Zachary Wong'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Yin Hoi Zachary Wong']"
+  },
+  {
+    "Query": "List claims where Loss Date between 2022-12-14 and 2023-03-26.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-14')) & (df['Loss_Date'] <= pd.to_datetime('2023-03-26'))]"
+  },
+  {
+    "Query": "List claims where Claim Id between 266951 and 279518.",
+    "pandas_code": "df[(df['Claim_Id'] >= 266951.22) & (df['Claim_Id'] <= 279518.36)]"
+  },
+  {
+    "Query": "Get claims where Approved Amount between 542 and 1402.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 542.48) & (df['Approved_Amount'] <= 1402.04)]"
+  },
+  {
+    "Query": "Display claims where Claim Amount at least 599.96.",
+    "pandas_code": "df[df['Claim_Amount'] >= 599.96]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-06-06.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-06-06')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-06-19.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-19')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date on 2023-07-21.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-07-21')]"
+  },
+  {
+    "Query": "Show claims where Claim Id between 270453 and 274716.",
+    "pandas_code": "df[(df['Claim_Id'] >= 270453.95) & (df['Claim_Id'] <= 274716.7)]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date before 2022-12-30.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2022-12-30')]"
+  },
+  {
+    "Query": "Show claims where Approved Amount equal to 1254.57.",
+    "pandas_code": "df[df['Approved_Amount'] == 1254.57]"
+  },
+  {
+    "Query": "List claims where Approved Amount less than 775.28.",
+    "pandas_code": "df[df['Approved_Amount'] < 775.28]"
+  },
+  {
+    "Query": "Display claims where Approved Amount less than 924.08.",
+    "pandas_code": "df[df['Approved_Amount'] < 924.08]"
+  },
+  {
+    "Query": "Provide claims where Loss Date after 2023-05-29.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-05-29')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date between 2023-01-04 and 2023-03-17.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-04')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-17'))]"
+  },
+  {
+    "Query": "Display claims where Claim Number is 'CBGDC23062453-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23062453-00']"
+  },
+  {
+    "Query": "Find claims where Claim Amount between 645 and 1276.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 645.05) & (df['Claim_Amount'] <= 1276.69)]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount between 765 and 1501.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 765.66) & (df['Approved_Amount'] <= 1501.6)]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2023-03-22.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-22')]"
+  },
+  {
+    "Query": "Display claims where Patient Name contains 'Ms Yi '.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Yi ', na=False)]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-03-30 and 2023-04-03.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-30')) & (df['Loss_Date'] <= pd.to_datetime('2023-04-03'))]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount between 303 and 1079.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 303.74) & (df['Approved_Amount'] <= 1079.37)]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-03-01.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-01')]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 670 and 910.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 670.95) & (df['Approved_Amount'] <= 910.41)]"
+  },
+  {
+    "Query": "Display claims where Approved Amount between 614 and 836.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 614.52) & (df['Approved_Amount'] <= 836.18)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date on 2023-06-27.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-27')]"
+  },
+  {
+    "Query": "Show claims where Approved Amount between 85 and 1164.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 85.29) & (df['Approved_Amount'] <= 1164.25)]"
+  },
+  {
+    "Query": "Provide claims where Patient Name is 'Ms Jia En Liong'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Jia En Liong']"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2023-04-28 and 2023-06-22.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-04-28')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-22'))]"
+  },
+  {
+    "Query": "Show claims where Approved Amount at most 474.46.",
+    "pandas_code": "df[df['Approved_Amount'] <= 474.46]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date on 2023-01-31.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-01-31')]"
+  },
+  {
+    "Query": "Fetch claims where Claim Id less than 265836.39.",
+    "pandas_code": "df[df['Claim_Id'] < 265836.39]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Type is 'GP Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'GP Treatment']"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-04-09 and 2023-04-21.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-04-09')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-04-21'))]"
+  },
+  {
+    "Query": "Show claims where Claim Amount at most 283.48.",
+    "pandas_code": "df[df['Claim_Amount'] <= 283.48]"
+  },
+  {
+    "Query": "Display claims where Loss Date between 2023-01-17 and 2023-06-02.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-17')) & (df['Loss_Date'] <= pd.to_datetime('2023-06-02'))]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date before 2023-07-23.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-23')]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-03-10.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-03-10')]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-07-12.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-07-12')]"
+  },
+  {
+    "Query": "List claims where Approved Amount between 188 and 1198.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 188.03) & (df['Approved_Amount'] <= 1198.22)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 266136 and 272787.",
+    "pandas_code": "df[(df['Claim_Id'] >= 266136.17) & (df['Claim_Id'] <= 272787.39)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount at most 883.41.",
+    "pandas_code": "df[df['Claim_Amount'] <= 883.41]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date before 2023-07-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-07-25')]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount at most 1098.68.",
+    "pandas_code": "df[df['Approved_Amount'] <= 1098.68]"
+  },
+  {
+    "Query": "Show claims where Status Code is 'RJ2'.",
+    "pandas_code": "df[df['Status_Code'] == 'RJ2']"
+  },
+  {
+    "Query": "Show claims where Loss Type contains 'Specia'.",
+    "pandas_code": "df[df['Loss_Type'].str.contains(r'Specia', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Amount less than 92.61.",
+    "pandas_code": "df[df['Claim_Amount'] < 92.61]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2023-02-09 and 2023-07-05.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-02-09')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-05'))]"
+  },
+  {
+    "Query": "Display claims where Loss Date before 2023-02-13.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-13')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2023-02-18.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-18')]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-01-04.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-04')]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-04-23.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-23')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-02-09.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-02-09')]"
+  },
+  {
+    "Query": "List claims where Patient Name is 'Ms Hoon Ng'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Hoon Ng']"
+  },
+  {
+    "Query": "Provide claims where Status Code is 'AP2'.",
+    "pandas_code": "df[df['Status_Code'] == 'AP2']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2023-06-17.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-17')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-07-05.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-05')]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2023-06-11.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-06-11')]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 702 and 882.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 702.87) & (df['Claim_Amount'] <= 882.81)]"
+  },
+  {
+    "Query": "Display claims where Scheme contains 'Templa'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'Templa', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2022-12-02 and 2023-04-16.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2022-12-02')) & (df['Loss_Date'] <= pd.to_datetime('2023-04-16'))]"
+  },
+  {
+    "Query": "Show claims where Provider Name is 'Kang An TCM Pte Ltd'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Kang An TCM Pte Ltd']"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-02-17 and 2023-03-25.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-02-17')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-25'))]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-01-26.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-26')]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2022-12-15.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-12-15')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id between 264800 and 278542.",
+    "pandas_code": "df[(df['Claim_Id'] >= 264800.34) & (df['Claim_Id'] <= 278542.17)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2023-05-03.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-03')]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Code contains 'PC1031'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1031', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date between 2023-01-25 and 2023-07-28.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-01-25')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-28'))]"
+  },
+  {
+    "Query": "Get claims where Patient Name is 'Ms Jie Xin Jacinth Tan'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Jie Xin Jacinth Tan']"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date on 2023-03-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-24')]"
+  },
+  {
+    "Query": "Show claims where Loss Date before 2023-06-08.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-06-08')]"
+  },
+  {
+    "Query": "Show claims where Claim Amount less than 700.83.",
+    "pandas_code": "df[df['Claim_Amount'] < 700.83]"
+  },
+  {
+    "Query": "Find claims where Approved Amount at most 1083.93.",
+    "pandas_code": "df[df['Approved_Amount'] <= 1083.93]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount at least 942.98.",
+    "pandas_code": "df[df['Approved_Amount'] >= 942.98]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2022-11-12 and 2023-07-14.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-11-12')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-14'))]"
+  },
+  {
+    "Query": "Get claims where Loss Date between 2023-03-23 and 2023-07-11.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-23')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-11'))]"
+  },
+  {
+    "Query": "Show claims where Claim Id less than 262543.01.",
+    "pandas_code": "df[df['Claim_Id'] < 262543.01]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-04-21.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-21')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2023-03-05 and 2023-04-08.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-03-05')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-04-08'))]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date between 2022-12-11 and 2023-01-05.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-11')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-01-05'))]"
+  },
+  {
+    "Query": "Provide claims where Policy Number contains 'BGDP22'.",
+    "pandas_code": "df[df['Policy_Number'].str.contains(r'BGDP22', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-04-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-04-12')]"
+  },
+  {
+    "Query": "Provide claims where Loss Date after 2023-06-11.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-06-11')]"
+  },
+  {
+    "Query": "Display claims where Patient Name contains 'Ms Cha'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Cha', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-01-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-04')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 317 and 1104.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 317.48) & (df['Claim_Amount'] <= 1104.37)]"
+  },
+  {
+    "Query": "Display claims where Claim Amount between 143 and 1351.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 143.78) & (df['Claim_Amount'] <= 1351.24)]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-01-06.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-01-06')]"
+  },
+  {
+    "Query": "Fetch claims where Provider Code contains 'PC1031'.",
+    "pandas_code": "df[df['Provider_Code'].str.contains(r'PC1031', na=False)]"
+  },
+  {
+    "Query": "Display claims where Approved Amount greater than 619.79.",
+    "pandas_code": "df[df['Approved_Amount'] > 619.79]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount at least 387.75.",
+    "pandas_code": "df[df['Approved_Amount'] >= 387.75]"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2023-07-30.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-07-30')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-06-15.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-06-15')]"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-01-08.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-01-08')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date on 2022-12-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2022-12-27')]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2023-07-12.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-07-12')]"
+  },
+  {
+    "Query": "Display claims where Status Code is 'PY3'.",
+    "pandas_code": "df[df['Status_Code'] == 'PY3']"
+  },
+  {
+    "Query": "Fetch claims where Patient Name is 'Ms Wei Ying Angeline Ng'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Wei Ying Angeline Ng']"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount equal to 1015.3.",
+    "pandas_code": "df[df['Approved_Amount'] == 1015.3]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-06-28.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-06-28')]"
+  },
+  {
+    "Query": "Display claims where Approved Amount equal to 593.15.",
+    "pandas_code": "df[df['Approved_Amount'] == 593.15]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount less than 1048.27.",
+    "pandas_code": "df[df['Approved_Amount'] < 1048.27]"
+  },
+  {
+    "Query": "List claims where Claim Created Date between 2023-01-28 and 2023-07-29.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-01-28')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-29'))]"
+  },
+  {
+    "Query": "Get claims where Claim Amount at least 142.89.",
+    "pandas_code": "df[df['Claim_Amount'] >= 142.89]"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name is 'Mr Madhusudhanan Mayavu'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Mr Madhusudhanan Mayavu']"
+  },
+  {
+    "Query": "Fetch claims where Policy Number is 'BGDC211000027-02-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDC211000027-02-000']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2022-12-04 and 2023-03-10.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-04')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-03-10'))]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date before 2023-04-05.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-05')]"
+  },
+  {
+    "Query": "Find claims where Loss Date after 2022-11-20.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2022-11-20')]"
+  },
+  {
+    "Query": "Get claims where Claim Created Date after 2023-03-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-27')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date after 2023-04-11.",
+    "pandas_code": "df[df['Loss_Date'] > pd.to_datetime('2023-04-11')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2023-02-09.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-09')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-05-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-05-25')]"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name contains 'Ms Sze'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Sze', na=False)]"
+  },
+  {
+    "Query": "Find claims where Claim Id greater than 270598.94.",
+    "pandas_code": "df[df['Claim_Id'] > 270598.94]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount greater than 1583.16.",
+    "pandas_code": "df[df['Claim_Amount'] > 1583.16]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2022-12-21.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-21')]"
+  },
+  {
+    "Query": "List claims where Loss Type is 'Hospitalisation'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Hospitalisation']"
+  },
+  {
+    "Query": "Fetch claims where Claim Created Date after 2023-05-27.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-27')]"
+  },
+  {
+    "Query": "Retrieve claims where Patient Name contains 'Ms Yu '.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Ms Yu ', na=False)]"
+  },
+  {
+    "Query": "Get claims where Claim Amount at most 1127.28.",
+    "pandas_code": "df[df['Claim_Amount'] <= 1127.28]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2023-05-07.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-07')]"
+  },
+  {
+    "Query": "Show claims where Claim Amount at most 1097.21.",
+    "pandas_code": "df[df['Claim_Amount'] <= 1097.21]"
+  },
+  {
+    "Query": "Retrieve claims where Status contains 'Paymen'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Paymen', na=False)]"
+  },
+  {
+    "Query": "Display claims where Claim Amount between 504 and 1479.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 504.44) & (df['Claim_Amount'] <= 1479.02)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date before 2023-03-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-03-16')]"
+  },
+  {
+    "Query": "Get claims where Loss Date before 2023-01-02.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-02')]"
+  },
+  {
+    "Query": "Show claims where Claim Amount between 27 and 860.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 27.01) & (df['Claim_Amount'] <= 860.73)]"
+  },
+  {
+    "Query": "List claims where Approved Amount at most 1331.41.",
+    "pandas_code": "df[df['Approved_Amount'] <= 1331.41]"
+  },
+  {
+    "Query": "Display claims where Loss Date on 2023-07-10.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-07-10')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date between 2023-03-14 and 2023-07-31.",
+    "pandas_code": "df[(df['Loss_Date'] >= pd.to_datetime('2023-03-14')) & (df['Loss_Date'] <= pd.to_datetime('2023-07-31'))]"
+  },
+  {
+    "Query": "Display claims where Approved Amount less than 527.54.",
+    "pandas_code": "df[df['Approved_Amount'] < 527.54]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount between 576 and 1100.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 576.63) & (df['Approved_Amount'] <= 1100.88)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Type is 'Specialist Treatment'.",
+    "pandas_code": "df[df['Loss_Type'] == 'Specialist Treatment']"
+  },
+  {
+    "Query": "Get claims where Claim Id less than 278561.9.",
+    "pandas_code": "df[df['Claim_Id'] < 278561.9]"
+  },
+  {
+    "Query": "Retrieve claims where Provider Code is 'PC102378'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC102378']"
+  },
+  {
+    "Query": "Display claims where Claim Created Date after 2023-01-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-04')]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount greater than 384.23.",
+    "pandas_code": "df[df['Approved_Amount'] > 384.23]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date after 2023-06-04.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-04')]"
+  },
+  {
+    "Query": "Get claims where Approved Amount greater than 770.43.",
+    "pandas_code": "df[df['Approved_Amount'] > 770.43]"
+  },
+  {
+    "Query": "Find claims where Loss Date before 2022-11-21.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2022-11-21')]"
+  },
+  {
+    "Query": "Get claims where Claim Amount between 805 and 1118.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 805.03) & (df['Claim_Amount'] <= 1118.31)]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2023-04-17 and 2023-07-18.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2023-04-17')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-07-18'))]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2023-05-27.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-05-27')]"
+  },
+  {
+    "Query": "Display claims where Claim Id between 268857 and 273238.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268857.29) & (df['Claim_Id'] <= 273238.24)]"
+  },
+  {
+    "Query": "Fetch claims where Provider Name is 'SINGHEALTH POLYCLINICS'.",
+    "pandas_code": "df[df['Provider_Name'] == 'SINGHEALTH POLYCLINICS']"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2023-04-19.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-19')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 556 and 1050.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 556.54) & (df['Claim_Amount'] <= 1050.48)]"
+  },
+  {
+    "Query": "Retrieve claims where Approved Amount at most 1218.35.",
+    "pandas_code": "df[df['Approved_Amount'] <= 1218.35]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Amount between 596 and 1128.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 596.94) & (df['Claim_Amount'] <= 1128.93)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount between 226 and 906.",
+    "pandas_code": "df[(df['Claim_Amount'] >= 226.23) & (df['Claim_Amount'] <= 906.73)]"
+  },
+  {
+    "Query": "Show claims where Loss Date before 2023-04-28.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-04-28')]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2023-06-07.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-06-07')]"
+  },
+  {
+    "Query": "Fetch claims where Scheme contains 'New Te'.",
+    "pandas_code": "df[df['Scheme'].str.contains(r'New Te', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Claim Amount equal to 1075.68.",
+    "pandas_code": "df[df['Claim_Amount'] == 1075.68]"
+  },
+  {
+    "Query": "List claims where Claim Created Date before 2023-04-15.",
+    "pandas_code": "df[df['Claim_Created_Date'] < pd.to_datetime('2023-04-15')]"
+  },
+  {
+    "Query": "Fetch claims where Approved Amount at most 1102.92.",
+    "pandas_code": "df[df['Approved_Amount'] <= 1102.92]"
+  },
+  {
+    "Query": "Find claims where Claim Id less than 263651.41.",
+    "pandas_code": "df[df['Claim_Id'] < 263651.41]"
+  },
+  {
+    "Query": "Find claims where Provider Name is 'Others'.",
+    "pandas_code": "df[df['Provider_Name'] == 'Others']"
+  },
+  {
+    "Query": "Show claims where Status contains 'Paid'.",
+    "pandas_code": "df[df['Status'].str.contains(r'Paid', na=False)]"
+  },
+  {
+    "Query": "Fetch claims where Provider Code is 'PC100001'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC100001']"
+  },
+  {
+    "Query": "Get claims where Loss Date on 2023-05-06.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-05-06')]"
+  },
+  {
+    "Query": "List claims where Status Code contains 'PY1'.",
+    "pandas_code": "df[df['Status_Code'].str.contains(r'PY1', na=False)]"
+  },
+  {
+    "Query": "Find claims where Approved Amount less than 675.01.",
+    "pandas_code": "df[df['Approved_Amount'] < 675.01]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Id greater than 278162.45.",
+    "pandas_code": "df[df['Claim_Id'] > 278162.45]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date between 2022-12-16 and 2023-04-26.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-16')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-04-26'))]"
+  },
+  {
+    "Query": "Display claims where Claim Created Date between 2022-12-27 and 2023-06-22.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-27')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-22'))]"
+  },
+  {
+    "Query": "List claims where Policy Number is 'BGDP221000711-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221000711-01-000']"
+  },
+  {
+    "Query": "Display claims where Provider Code is 'PC101350'.",
+    "pandas_code": "df[df['Provider_Code'] == 'PC101350']"
+  },
+  {
+    "Query": "Get claims where Patient Name is 'Ms Aurelie Tarnier'.",
+    "pandas_code": "df[df['Patient_Name'] == 'Ms Aurelie Tarnier']"
+  },
+  {
+    "Query": "Display claims where Patient Name contains 'Mr Tan'.",
+    "pandas_code": "df[df['Patient_Name'].str.contains(r'Mr Tan', na=False)]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-03-22.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-03-22')]"
+  },
+  {
+    "Query": "Retrieve claims where Claim Created Date after 2023-05-25.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-05-25')]"
+  },
+  {
+    "Query": "List claims where Claim Id between 268950 and 277878.",
+    "pandas_code": "df[(df['Claim_Id'] >= 268950.15) & (df['Claim_Id'] <= 277878.43)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date after 2022-12-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2022-12-12')]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date between 2022-12-21 and 2023-06-15.",
+    "pandas_code": "df[(df['Claim_Created_Date'] >= pd.to_datetime('2022-12-21')) & (df['Claim_Created_Date'] <= pd.to_datetime('2023-06-15'))]"
+  },
+  {
+    "Query": "Get claims where Provider Name contains 'NTUC D'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'NTUC D', na=False)]"
+  },
+  {
+    "Query": "List claims where Claim Created Date on 2023-07-16.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-07-16')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-04-29.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-04-29')]"
+  },
+  {
+    "Query": "List claims where Loss Date before 2023-07-26.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-07-26')]"
+  },
+  {
+    "Query": "Provide claims where Approved Amount between 750 and 784.",
+    "pandas_code": "df[(df['Approved_Amount'] >= 750.36) & (df['Approved_Amount'] <= 784.03)]"
+  },
+  {
+    "Query": "Get claims where Claim Id less than 270029.61.",
+    "pandas_code": "df[df['Claim_Id'] < 270029.61]"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2023-04-13.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2023-04-13')]"
+  },
+  {
+    "Query": "Find claims where Loss Date on 2022-11-10.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-10')]"
+  },
+  {
+    "Query": "Retrieve claims where Loss Date before 2023-02-27.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-02-27')]"
+  },
+  {
+    "Query": "Show claims where Loss Date on 2022-11-15.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-15')]"
+  },
+  {
+    "Query": "Find claims where Claim Created Date after 2023-01-24.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-01-24')]"
+  },
+  {
+    "Query": "List claims where Claim Number is 'CBGDC23070205-00'.",
+    "pandas_code": "df[df['Claim_Number'] == 'CBGDC23070205-00']"
+  },
+  {
+    "Query": "Find claims where Claim Amount at least 891.79.",
+    "pandas_code": "df[df['Claim_Amount'] >= 891.79]"
+  },
+  {
+    "Query": "List claims where Loss Date on 2022-11-08.",
+    "pandas_code": "df[df['Loss_Date'] == pd.to_datetime('2022-11-08')]"
+  },
+  {
+    "Query": "Show claims where Provider Name contains 'TAN TO'.",
+    "pandas_code": "df[df['Provider_Name'].str.contains(r'TAN TO', na=False)]"
+  },
+  {
+    "Query": "Show claims where Claim Created Date after 2023-08-01.",
+    "pandas_code": "df[df['Claim_Created_Date'] > pd.to_datetime('2023-08-01')]"
+  },
+  {
+    "Query": "Provide claims where Claim Created Date on 2023-03-12.",
+    "pandas_code": "df[df['Claim_Created_Date'] == pd.to_datetime('2023-03-12')]"
+  },
+  {
+    "Query": "Find claims where Claim Id between 264672 and 279727.",
+    "pandas_code": "df[(df['Claim_Id'] >= 264672.85) & (df['Claim_Id'] <= 279727.38)]"
+  },
+  {
+    "Query": "Fetch claims where Loss Date before 2023-01-15.",
+    "pandas_code": "df[df['Loss_Date'] < pd.to_datetime('2023-01-15')]"
+  },
+  {
+    "Query": "Get claims where Policy Number is 'BGDP221001358-00-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP221001358-00-000']"
+  },
+  {
+    "Query": "List claims where Policy Number is 'BGDP211000428-01-000'.",
+    "pandas_code": "df[df['Policy_Number'] == 'BGDP211000428-01-000']"
+  },
+  
+    {
+        "english": "What is the total claim amount?",
+        "pandas_code": " df['Claim_Amount'].sum()",
+        "intent": "aggregate",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average claim amount?",
+        "pandas_code": " df['Claim_Amount'].mean()",
+        "intent": "aggregate",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the median claim amount?",
+        "pandas_code": " df['Claim_Amount'].median()",
+        "intent": "aggregate",
+        "operation": "median"
+    },
+    {
+        "english": "What is the standard deviation of claim amount?",
+        "pandas_code": " df['Claim_Amount'].std()",
+        "intent": "aggregate",
+        "operation": "std"
+    },
+    {
+        "english": "What is the minimum claim amount?",
+        "pandas_code": " df['Claim_Amount'].min()",
+        "intent": "aggregate",
+        "operation": "min"
+    },
+    {
+        "english": "What is the maximum claim amount?",
+        "pandas_code": " df['Claim_Amount'].max()",
+        "intent": "aggregate",
+        "operation": "max"
+    },
+    {
+        "english": "What is the total approved amount?",
+        "pandas_code": " df['Approved_Amount'].sum()",
+        "intent": "aggregate",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average approved amount?",
+        "pandas_code": " df['Approved_Amount'].mean()",
+        "intent": "aggregate",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the median approved amount?",
+        "pandas_code": " df['Approved_Amount'].median()",
+        "intent": "aggregate",
+        "operation": "median"
+    },
+    {
+        "english": "What is the standard deviation of approved amount?",
+        "pandas_code": " df['Approved_Amount'].std()",
+        "intent": "aggregate",
+        "operation": "std"
+    },
+    {
+        "english": "What is the minimum approved amount?",
+        "pandas_code": " df['Approved_Amount'].min()",
+        "intent": "aggregate",
+        "operation": "min"
+    },
+    {
+        "english": "What is the maximum approved amount?",
+        "pandas_code": " df['Approved_Amount'].max()",
+        "intent": "aggregate",
+        "operation": "max"
+    },
+    {
+        "english": "What is the total deduction amount?",
+        "pandas_code": " df['Deduction_Amount'].sum()",
+        "intent": "aggregate",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average deduction amount?",
+        "pandas_code": " df['Deduction_Amount'].mean()",
+        "intent": "aggregate",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the median deduction amount?",
+        "pandas_code": " df['Deduction_Amount'].median()",
+        "intent": "aggregate",
+        "operation": "median"
+    },
+    {
+        "english": "What is the standard deviation of deduction amount?",
+        "pandas_code": " df['Deduction_Amount'].std()",
+        "intent": "aggregate",
+        "operation": "std"
+    },
+    {
+        "english": "What is the minimum deduction amount?",
+        "pandas_code": " df['Deduction_Amount'].min()",
+        "intent": "aggregate",
+        "operation": "min"
+    },
+    {
+        "english": "What is the maximum deduction amount?",
+        "pandas_code": " df['Deduction_Amount'].max()",
+        "intent": "aggregate",
+        "operation": "max"
+    },
+    {
+        "english": "What is the total claim amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Claim_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average claim amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum claim amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Claim_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum claim amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Claim_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of claim amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Claim_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median claim amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Claim_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total approved amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average approved amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum approved amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Approved_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum approved amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of approved amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Approved_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median approved amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Approved_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total deduction amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average deduction amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum deduction amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum deduction amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of deduction amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Deduction_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median deduction amount per provider name?",
+        "pandas_code": " df.groupby('Provider_Name')['Deduction_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total claim amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average claim amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum claim amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum claim amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Claim_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of claim amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Claim_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median claim amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Claim_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total approved amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average approved amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum approved amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum approved amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of approved amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Approved_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median approved amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Approved_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total deduction amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average deduction amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum deduction amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Deduction_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum deduction amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Deduction_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of deduction amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Deduction_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median deduction amount per loss type?",
+        "pandas_code": " df.groupby('Loss_Type')['Deduction_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total claim amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average claim amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum claim amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum claim amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Claim_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of claim amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Claim_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median claim amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Claim_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total approved amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average approved amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum approved amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum approved amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of approved amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Approved_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median approved amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Approved_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total deduction amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Deduction_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average deduction amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum deduction amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum deduction amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of deduction amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Deduction_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median deduction amount per claim type?",
+        "pandas_code": " df.groupby('Claim_Type')['Deduction_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total claim amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Claim_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average claim amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Claim_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum claim amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Claim_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum claim amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Claim_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of claim amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Claim_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median claim amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Claim_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total approved amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Approved_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average approved amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Approved_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum approved amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Approved_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum approved amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Approved_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of approved amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Approved_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median approved amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Approved_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total deduction amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Deduction_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average deduction amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Deduction_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum deduction amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Deduction_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum deduction amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Deduction_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of deduction amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Deduction_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median deduction amount per scheme?",
+        "pandas_code": " df.groupby('Scheme')['Deduction_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total claim amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Claim_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average claim amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Claim_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum claim amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Claim_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum claim amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Claim_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of claim amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Claim_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median claim amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Claim_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total approved amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Approved_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average approved amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Approved_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum approved amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Approved_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum approved amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Approved_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of approved amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Approved_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median approved amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Approved_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "What is the total deduction amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Deduction_Amount'].sum().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "What is the average deduction amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Deduction_Amount'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the maximum deduction amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Deduction_Amount'].max().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "max"
+    },
+    {
+        "english": "What is the minimum deduction amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Deduction_Amount'].min().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "min"
+    },
+    {
+        "english": "What is the standard deviation of deduction amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Deduction_Amount'].std().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "std"
+    },
+    {
+        "english": "What is the median deduction amount per icd?",
+        "pandas_code": " df.groupby('ICD')['Deduction_Amount'].median().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "median"
+    },
+    {
+        "english": "How many unique policy number exist?",
+        "pandas_code": " df['Policy_Number'].nunique()",
+        "intent": "unique_value",
+        "operation": "unique_count"
+    },
+    {
+        "english": "How many unique icd exist?",
+        "pandas_code": " df['ICD'].nunique()",
+        "intent": "unique_value",
+        "operation": "unique_count"
+    },
+    {
+        "english": "How many unique patient name exist?",
+        "pandas_code": " df['Patient_Name'].nunique()",
+        "intent": "unique_value",
+        "operation": "unique_count"
+    },
+    {
+        "english": "How many unique provider name exist?",
+        "pandas_code": " df['Provider_Name'].nunique()",
+        "intent": "unique_value",
+        "operation": "unique_count"
+    },
+    {
+        "english": "How many unique status exist?",
+        "pandas_code": " df['Status'].nunique()",
+        "intent": "unique_value",
+        "operation": "unique_count"
+    },
+    {
+        "english": "What is the icd for claim number CDFT23423523-65?",
+        "pandas_code": " df.loc[df['Claim_Number'] == 'CDFT23423523-65','ICD']",
+        "intent": "lookup",
+        "operation": "filter"
+    },
+    {
+        "english": "What is the icd disease for claim number CDFT23423523-65?",
+        "pandas_code": " df.loc[df['Claim_Number'] == 'CDFT23423523-65','Icd Disease']",
+        "intent": "lookup",
+        "operation": "filter"
+    },
+    {
+        "english": "What is the provider pandas_code for claim number CDFT23423523-65?",
+        "pandas_code": " df.loc[df['Claim_Number'] == 'CDFT23423523-65','Provider_pandas_code']",
+        "intent": "lookup",
+        "operation": "filter"
+    },
+    {
+        "english": "What is the provider name for claim number CDFT23423523-65?",
+        "pandas_code": " df.loc[df['Claim_Number'] == 'CDFT23423523-65','Provider_Name']",
+        "intent": "lookup",
+        "operation": "filter"
+    },
+    {
+        "english": "What is the gender for claim number CDFT23423523-65?",
+        "pandas_code": " df.loc[df['Claim_Number'] == 'CDFT23423523-65','Gender']",
+        "intent": "lookup",
+        "operation": "filter"
+    },
+    {
+        "english": "What is the patient name for claim number CDFT23423523-65?",
+        "pandas_code": " df.loc[df['Claim_Number'] == 'CDFT23423523-65','Patient_Name']",
+        "intent": "lookup",
+        "operation": "filter"
+    },
+    {
+        "english": "What is the policy number for claim number CDFT23423523-65?",
+        "pandas_code": " df.loc[df['Claim_Number'] == 'CDFT23423523-65','Policy_Number']",
+        "intent": "lookup",
+        "operation": "filter"
+    },
+    {
+        "english": "What is the scheme for claim number CDFT23423523-65?",
+        "pandas_code": " df.loc[df['Claim_Number'] == 'CDFT23423523-65','Scheme']",
+        "intent": "lookup",
+        "operation": "filter"
+    },
+    {
+        "english": "What is the loss type for claim number CDFT23423523-65?",
+        "pandas_code": " df.loc[df['Claim_Number'] == 'CDFT23423523-65','Loss_Type']",
+        "intent": "lookup",
+        "operation": "filter"
+    },
+    {
+        "english": "What is the claim type for claim number CDFT23423523-65?",
+        "pandas_code": " df.loc[df['Claim_Number'] == 'CDFT23423523-65','Claim_Type']",
+        "intent": "lookup",
+        "operation": "filter"
+    },
+    {
+        "english": "What is the deduction amount for claim number CDFT23423523-65?",
+        "pandas_code": " df.loc[df['Claim_Number'] == 'CDFT23423523-65','Deduction_Amount']",
+        "intent": "lookup",
+        "operation": "filter"
+    },
+    {
+        "english": "What is the approved amount for claim number CDFT23423523-65?",
+        "pandas_code": " df.loc[df['Claim_Number'] == 'CDFT23423523-65','Approved_Amount']",
+        "intent": "lookup",
+        "operation": "filter"
+    },
+    {
+        "english": "What is the claim amount for claim number CDFT23423523-65?",
+        "pandas_code": " df.loc[df['Claim_Number'] == 'CDFT23423523-65','Claim_Amount']",
+        "intent": "lookup",
+        "operation": "filter"
+    },
+    {
+        "english": "What is the settlement ageing of each provider?",
+        "pandas_code": " df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index()",
+        "intent": "aggregate_group",
+        "operation": "mean"
+    },
+    {
+        "english": "What is the settlement ratio of each provider?",
+        "pandas_code": " df.groupby('Provider_Name').apply(lambda x: (x['Approved_Amount'].sum()/x['Claim_Amount'].sum())*100).reset_index(name='Settlement_Ratio')",
+        "intent": "aggregate_group",
+        "operation": "sum"
+    },
+    {
+        "english": "How many claims are settled within 1 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] <= 1] ",
+        "intent": "count",
+        "operation": "count"
+    },
+    {
+        "english": "How many claims are settled after 1 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] > 1] ",
+        "intent": "count",
+        "operation": "count"
+    },
+    {
+        "english": "How many claims are settled within 10 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] <= 10]  ",
+        "intent": "count",
+        "operation": "count"
+    },
+    {
+        "english": "How many claims are settled after 10 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] > 10]  ",
+        "intent": "count",
+        "operation": "count"
+    },
+    {
+        "english": "How many claims are settled within 30 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] <= 30]  ",
+        "intent": "count",
+        "operation": "count"
+    },
+    {
+        "english": "How many claims are settled after 30 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] > 30]  ",
+        "intent": "count",
+        "operation": "count"
+    },
+    {
+        "english": "How many claims are settled within 50 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] <= 50]  ",
+        "intent": "count",
+        "operation": "count"
+    },
+    {
+        "english": "How many claims are settled after 50 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] > 50]  ",
+        "intent": "count",
+        "operation": "count"
+    },
+    {
+        "english": "How many claims are settled within 100 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] <= 100]  ",
+        "intent": "count",
+        "operation": "count"
+    },
+    {
+        "english": "How many claims are settled after 100 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] > 100]  ",
+        "intent": "count",
+        "operation": "count"
+    },
+    {
+        "english": "How many claims are settled within 270 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] <= 270]  ",
+        "intent": "count",
+        "operation": "count"
+    },
+    {
+        "english": "How many claims are settled after 270 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] > 270]  ",
+        "intent": "count",
+        "operation": "count"
+    },
+    {
+        "english": "How many claims are settled within 1000 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] <= 1000]  ",
+        "intent": "count",
+        "operation": "count"
+    },
+    {
+        "english": "How many claims are settled after 1000 days?",
+        "pandas_code": " df[df['Settlement_Ageing'] > 1000]  ",
+        "intent": "count",
+        "operation": "count"
+    },
+    
+    {
+        "english": "Show provider-wise ICD-wise claims where approved amount > 50000",
+        "pandas_code": " df[df['Approved_Amount'] > 50000].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims where claim amount > 100000 and status is approved",
+        "pandas_code": " df[(df['Claim_Amount'] > 100000) & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims where paid amount < 10000",
+        "pandas_code": " df[df['Paid_Amount'] < 10000].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims where claim amount is between 20000 and 50000",
+        "pandas_code": " df[df['Claim_Amount'].between(20000,50000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims for loss type 'Accident' with approved amount > 50000",
+        "pandas_code": " df[(df['Loss_Type'] == 'Accident') & (df['Approved_Amount'] > 50000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List ICD-wise provider claims for product 'Arogya Sanjeevani' in the last quarter",
+        "pandas_code": " df[df['Product'] == 'Arogya Sanjeevani'].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims for policy number 'POL12345' and approved amount > 25000",
+        "pandas_code": " df[(df['Policy_Number'] == 'POL12345') & (df['Approved_Amount'] > 25000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims for male patients with approved amount > 50000",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Approved_Amount'] > 50000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims for female patients in the last quarter",
+        "pandas_code": " df[df['Gender'] == 'Female'].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for nationality 'Sample' and claim amount > 20000.",
+        "pandas_code": " df[(df['Nationality'] == 'Sample') & (df['Claim_Amount'] > 20000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims where claim amount > 50000, approved amount > 40000, and status approved.",
+        "pandas_code": " df[(df['Claim_Amount'] > 50000) & (df['Approved_Amount'] > 40000) & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List ICD-wise provider claims for product 'Narayana Group Health', loss type 'Hospitalization', approved in the last quarter, and nationality 'Sample'.",
+        "pandas_code": " df[(df['Product'] == 'Narayana Group Health') & (df['Loss_Type'] == 'Hospitalization') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider and ICD-wise claims with claim amount > 100000, paid amount < 50000, policy 'POL12345', and status approved.",
+        "pandas_code": " df[(df['Claim_Amount'] > 100000) & (df['Paid_Amount'] < 50000) & (df['Policy_Number'] == 'POL12345') & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims per provider and ICD where claim amount is between 20000 and 100000, loss type 'Accident', and approved amount > 50000.",
+        "pandas_code": " df[df['Claim_Amount'].between(20000,100000) & (df['Loss_Type'] == 'Accident') & (df['Approved_Amount'] > 50000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider-wise ICD-wise claims where claim is registered in last 6 months, approved in last quarter, and paid amount > 30000.",
+        "pandas_code": " df[df['Paid_Amount'] > 30000].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims where approved amount > 75000 and claim status is pending.",
+        "pandas_code": " df[(df['Approved_Amount'] > 75000) & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List ICD-wise provider claims with claim amount > 50000 and paid amount < 40000 in last financial year.",
+        "pandas_code": " df[(df['Claim_Amount'] > 50000) & (df['Paid_Amount'] < 40000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims for loss type 'Critical Illness' and approved amount > 60000.",
+        "pandas_code": " df[(df['Loss_Type'] == 'Critical Illness') & (df['Approved_Amount'] > 60000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims per provider and ICD where claim amount > 30000, policy 'POL56789', and status rejected.",
+        "pandas_code": " df[(df['Claim_Amount'] > 30000) & (df['Policy_Number'] == 'POL56789') & (df['Status'] == 'Rejected')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims for male patients, scheme 'Narayana Plus', approved amount > 25000, and loss type 'Hospitalization'.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Scheme'] == 'Narayana Plus') & (df['Approved_Amount'] > 25000) & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims where claim amount > 40000, approved amount > 35000, and claim is registered last quarter.",
+        "pandas_code": " df[(df['Claim_Amount'] > 40000) & (df['Approved_Amount'] > 35000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for product 'Arogya Sanjeevani' and loss type 'Accident' with paid amount > 20000.",
+        "pandas_code": " df[(df['Product'] == 'Arogya Sanjeevani') & (df['Loss_Type'] == 'Accident') & (df['Paid_Amount'] > 20000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims where status is rejected and claim amount > 50000.",
+        "pandas_code": " df[(df['Status'] == 'Rejected') & (df['Claim_Amount'] > 50000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims for nationality 'Sample' and approved amount between 20000 and 70000.",
+        "pandas_code": " df[(df['Nationality'] == 'Sample') & (df['Approved_Amount'].between(20000,70000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims where claim amount > 100000, paid amount > 50000, and status approved.",
+        "pandas_code": " df[(df['Claim_Amount'] > 100000) & (df['Paid_Amount'] > 50000) & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims where claim amount < 20000 and claim is pending.",
+        "pandas_code": " df[(df['Claim_Amount'] < 20000) & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims for loss type 'Critical Illness' and claim amount > 80000.",
+        "pandas_code": " df[(df['Loss_Type'] == 'Critical Illness') & (df['Claim_Amount'] > 80000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims for male patients, approved amount > 40000, and policy 'POL12345'.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Approved_Amount'] > 40000) & (df['Policy_Number'] == 'POL12345')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims where claim amount between 50000 and 100000, status approved, and paid amount < 60000.",
+        "pandas_code": " df[(df['Claim_Amount'].between(50000,100000)) & (df['Status'] == 'Approved') & (df['Paid_Amount'] < 60000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give ICD-wise provider claims for female patients with approved amount > 35000 and claim amount > 40000.",
+        "pandas_code": " df[(df['Gender'] == 'Female') & (df['Approved_Amount'] > 35000) & (df['Claim_Amount'] > 40000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims for product 'Narayana Group Health' and status approved with paid amount > 30000.",
+        "pandas_code": " df[(df['Product'] == 'Narayana Group Health') & (df['Status'] == 'Approved') & (df['Paid_Amount'] > 30000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims for male patients, scheme 'Arogya Sanjeevani', loss type 'Accident', and claim amount > 25000.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Scheme'] == 'Arogya Sanjeevani') & (df['Loss_Type'] == 'Accident') & (df['Claim_Amount'] > 25000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims for last financial year where claim amount > 50000, approved amount > 40000, and status paid.",
+        "pandas_code": " df[(df['Claim_Amount'] > 50000) & (df['Approved_Amount'] > 40000) & (df['Status'] == 'Paid')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for last quarter with claim amount between 30000 and 70000 and loss type 'Hospitalization'.",
+        "pandas_code": " df[df['Claim_Amount'].between(30000,70000) & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims with approved amount > 60000, paid amount < 50000, and nationality 'Sample'.",
+        "pandas_code": " df[(df['Approved_Amount'] > 60000) & (df['Paid_Amount'] < 50000) & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims where claim is pending, amount > 20000, and policy 'POL56789'.",
+        "pandas_code": " df[(df['Status'] == 'Pending') & (df['Claim_Amount'] > 20000) & (df['Policy_Number'] == 'POL56789')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims for female patients, loss type 'Critical Illness', and approved amount > 30000.",
+        "pandas_code": " df[(df['Gender'] == 'Female') & (df['Loss_Type'] == 'Critical Illness') & (df['Approved_Amount'] > 30000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for male patients with paid amount > 40000 and claim amount between 50000 and 100000.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Paid_Amount'] > 40000) & df['Claim_Amount'].between(50000,100000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims where claim amount > 50000, approved amount > 45000, and status rejected.",
+        "pandas_code": " df[(df['Claim_Amount'] > 50000) & (df['Approved_Amount'] > 45000) & (df['Status'] == 'Rejected')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims where loss type is 'Accident', policy 'POL12345', and claim amount > 60000.",
+        "pandas_code": " df[(df['Loss_Type'] == 'Accident') & (df['Policy_Number'] == 'POL12345') & (df['Claim_Amount'] > 60000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims for last 3 months with status approved and approved amount > 25000.",
+        "pandas_code": "last_3_months = pd.Timestamp.now() - pd.DateOffset(months=3);  df[(df['Claim_Approved_Date'] >= last_3_months) & (df['Status'] == 'Approved') & (df['Approved_Amount'] > 25000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for product 'Arogya Sanjeevani', claim amount > 40000, and paid amount > 30000.",
+        "pandas_code": " df[(df['Product'] == 'Arogya Sanjeevani') & (df['Claim_Amount'] > 40000) & (df['Paid_Amount'] > 30000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims for male patients, approved amount > 50000, and last quarter registration.",
+        "pandas_code": "last_quarter_start = pd.Timestamp.now() - pd.offsets.QuarterBegin(startingMonth=1);  df[(df['Gender'] == 'Male') & (df['Approved_Amount'] > 50000) & (df['Claim_Created_Date'] >= last_quarter_start)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims where claim amount > 100000, paid amount < 60000, and status pending.",
+        "pandas_code": " df[(df['Claim_Amount'] > 100000) & (df['Paid_Amount'] < 60000) & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims for female patients, loss type 'Hospitalization', and claim amount > 45000.",
+        "pandas_code": " df[(df['Gender'] == 'Female') & (df['Loss_Type'] == 'Hospitalization') & (df['Claim_Amount'] > 45000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for last financial year with claim amount between 30000 and 70000, approved amount > 35000.",
+        "pandas_code": "last_fin_year_start = pd.Timestamp(pd.Timestamp.now().year - 1,4,1); last_fin_year_end = pd.Timestamp(pd.Timestamp.now().year,3,31);  df[(df['Claim_Amount'].between(30000,70000)) & (df['Approved_Amount'] > 35000) & (df['Claim_Approved_Date'].between(last_fin_year_start,last_fin_year_end))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims where claim amount > 50000, policy 'POL12345', and status approved.",
+        "pandas_code": " df[(df['Claim_Amount'] > 50000) & (df['Policy_Number'] == 'POL12345') & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims for nationality 'Sample', claim amount > 40000, and approved amount > 35000.",
+        "pandas_code": " df[(df['Nationality'] == 'Sample') & (df['Claim_Amount'] > 40000) & (df['Approved_Amount'] > 35000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims for male patients, product 'Narayana Group Health', approved amount > 30000.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Product'] == 'Narayana Group Health') & (df['Approved_Amount'] > 30000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for female patients with loss type 'Critical Illness', claim amount > 50000, and paid amount < 45000.",
+        "pandas_code": " df[(df['Gender'] == 'Female') & (df['Loss_Type'] == 'Critical Illness') & (df['Claim_Amount'] > 50000) & (df['Paid_Amount'] < 45000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims where claim amount > 60000, approved amount > 55000, status approved, and last quarter registered.",
+        "pandas_code": " df[(df['Claim_Amount'] > 60000) & (df['Approved_Amount'] > 55000) & (df['Status'] == 'Approved') & (df['Claim_Created_Date'] >= last_quarter_start)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims for loss type 'Accident', claim amount between 20000 and 70000, and policy 'POL56789'.",
+        "pandas_code": " df[(df['Loss_Type'] == 'Accident') & (df['Claim_Amount'].between(20000,70000)) & (df['Policy_Number'] == 'POL56789')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims where claim amount > 50000, approved amount > 40000, paid amount < 30000, and status rejected.",
+        "pandas_code": " df[(df['Claim_Amount'] > 50000) & (df['Approved_Amount'] > 40000) & (df['Paid_Amount'] < 30000) & (df['Status'] == 'Rejected')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for male patients, last financial year, claim amount > 45000, and approved amount > 35000.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Claim_Amount'] > 45000) & (df['Approved_Amount'] > 35000) & (df['Claim_Approved_Date'].between(last_fin_year_start,last_fin_year_end))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims where product 'Arogya Sanjeevani', loss type 'Hospitalization', claim amount > 40000, and paid amount > 30000.",
+        "pandas_code": " df[(df['Product'] == 'Arogya Sanjeevani') & (df['Loss_Type'] == 'Hospitalization') & (df['Claim_Amount'] > 40000) & (df['Paid_Amount'] > 30000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims for female patients, approved amount > 50000, last quarter registration, and claim status approved.",
+        "pandas_code": " df[(df['Gender'] == 'Female') & (df['Approved_Amount'] > 50000) & (df['Claim_Created_Date'] >= last_quarter_start) & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims where claim amount > 60000, policy 'POL12345', loss type 'Critical Illness', and status pending.",
+        "pandas_code": " df[(df['Claim_Amount'] > 60000) & (df['Policy_Number'] == 'POL12345') & (df['Loss_Type'] == 'Critical Illness') & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for male patients, claim amount > 50000, approved amount > 45000, paid amount < 40000, last financial year.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Claim_Amount'] > 50000) & (df['Approved_Amount'] > 45000) & (df['Paid_Amount'] < 40000) & (df['Claim_Approved_Date'].between(last_fin_year_start,last_fin_year_end))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims where claim amount > 70000, loss type 'Accident', status approved, and product 'Narayana Group Health'.",
+        "pandas_code": " df[(df['Claim_Amount'] > 70000) & (df['Loss_Type'] == 'Accident') & (df['Status'] == 'Approved') & (df['Product'] == 'Narayana Group Health')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims for female patients, policy 'POL56789', approved amount > 40000, and last quarter.",
+        "pandas_code": " df[(df['Gender'] == 'Female') & (df['Policy_Number'] == 'POL56789') & (df['Approved_Amount'] > 40000) & (df['Claim_Created_Date'] >= last_quarter_start)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims where claim amount > 80000, approved amount > 60000, paid amount > 50000, and status approved.",
+        "pandas_code": " df[(df['Claim_Amount'] > 80000) & (df['Approved_Amount'] > 60000) & (df['Paid_Amount'] > 50000) & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for male patients, loss type 'Hospitalization', claim amount > 40000, approved amount > 35000, last financial year.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Loss_Type'] == 'Hospitalization') & (df['Claim_Amount'] > 40000) & (df['Approved_Amount'] > 35000) & (df['Claim_Approved_Date'].between(last_fin_year_start,last_fin_year_end))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims where claim amount > 50000, approved amount > 45000, policy 'POL12345', and status rejected.",
+        "pandas_code": " df[(df['Claim_Amount'] > 50000) & (df['Approved_Amount'] > 45000) & (df['Policy_Number'] == 'POL12345') & (df['Status'] == 'Rejected')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims for female patients, last quarter, claim amount > 60000, paid amount < 50000, and approved amount > 40000.",
+        "pandas_code": " df[(df['Gender'] == 'Female') & (df['Claim_Created_Date'] >= last_quarter_start) & (df['Claim_Amount'] > 60000) & (df['Paid_Amount'] < 50000) & (df['Approved_Amount'] > 40000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims where product 'Arogya Sanjeevani', claim amount > 70000, approved amount > 60000, paid amount > 50000, and loss type 'Critical Illness'.",
+        "pandas_code": " df[(df['Product'] == 'Arogya Sanjeevani') & (df['Claim_Amount'] > 70000) & (df['Approved_Amount'] > 60000) & (df['Paid_Amount'] > 50000) & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for male patients, claim amount > 50000, approved amount > 45000, policy 'POL12345', status approved, and last financial year.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Claim_Amount'] > 50000) & (df['Approved_Amount'] > 45000) & (df['Policy_Number'] == 'POL12345') & (df['Status'] == 'Approved') & (df['Claim_Approved_Date'].between(last_fin_year_start,last_fin_year_end))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims where claim amount > 60000, paid amount < 50000, product 'Narayana Group Health', and loss type 'Accident'.",
+        "pandas_code": " df[(df['Claim_Amount'] > 60000) & (df['Paid_Amount'] < 50000) & (df['Product'] == 'Narayana Group Health') & (df['Loss_Type'] == 'Accident')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims for female patients, approved amount > 50000, last quarter, claim status approved, and policy 'POL56789'.",
+        "pandas_code": " df[(df['Gender'] == 'Female') & (df['Approved_Amount'] > 50000) & (df['Claim_Created_Date'] >= last_quarter_start) & (df['Status'] == 'Approved') & (df['Policy_Number'] == 'POL56789')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims where claim amount > 80000, approved amount > 70000, paid amount > 60000, status approved, and loss type 'Hospitalization'.",
+        "pandas_code": " df[(df['Claim_Amount'] > 80000) & (df['Approved_Amount'] > 70000) & (df['Paid_Amount'] > 60000) & (df['Status'] == 'Approved') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for male patients, last financial year, claim amount > 70000, approved amount > 60000, paid amount < 50000, and loss type 'Critical Illness'.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Claim_Amount'] > 70000) & (df['Approved_Amount'] > 60000) & (df['Paid_Amount'] < 50000) & (df['Loss_Type'] == 'Critical Illness') & (df['Claim_Approved_Date'].between(last_fin_year_start,last_fin_year_end))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims where claim amount > 90000, approved amount > 80000, policy 'POL12345', status approved, product 'Arogya Sanjeevani'.",
+        "pandas_code": " df[(df['Claim_Amount'] > 90000) & (df['Approved_Amount'] > 80000) & (df['Policy_Number'] == 'POL12345') & (df['Status'] == 'Approved') & (df['Product'] == 'Arogya Sanjeevani')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims for female patients, claim amount > 60000, approved amount > 50000, paid amount > 40000, last quarter, loss type 'Accident'.",
+        "pandas_code": " df[(df['Gender'] == 'Female') & (df['Claim_Amount'] > 60000) & (df['Approved_Amount'] > 50000) & (df['Paid_Amount'] > 40000) & (df['Claim_Created_Date'] >= last_quarter_start) & (df['Loss_Type'] == 'Accident')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims where claim amount > 70000, approved amount > 60000, paid amount < 50000, status approved, last financial year, and product 'Narayana Group Health'.",
+        "pandas_code": " df[(df['Claim_Amount'] > 70000) & (df['Approved_Amount'] > 60000) & (df['Paid_Amount'] < 50000) & (df['Status'] == 'Approved') & (df['Claim_Approved_Date'].between(last_fin_year_start,last_fin_year_end)) & (df['Product'] == 'Narayana Group Health')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for male patients, claim amount > 80000, approved amount > 70000, paid amount > 60000, loss type 'Hospitalization', policy 'POL56789', last quarter.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Claim_Amount'] > 80000) & (df['Approved_Amount'] > 70000) & (df['Paid_Amount'] > 60000) & (df['Loss_Type'] == 'Hospitalization') & (df['Policy_Number'] == 'POL56789') & (df['Claim_Created_Date'] >= last_quarter_start)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise claims count by status where claim amount > 100000.",
+        "pandas_code": " df[df['Claim_Amount'] > 100000].groupby(['Provider_Name','Status']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List top 10 providers with highest total claim amounts.",
+        "pandas_code": " df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10).reset_index(name='Total_Claim_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show monthly trend of claim counts for the last year.",
+        "pandas_code": "df['Claim_Month'] = pd.to_datetime(df['Claim_Created_Date']).dt.to_period('M');  df.groupby('Claim_Month').size().reset_index(name='Monthly_Claims')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Find average claim amount by loss type for approved claims.",
+        "pandas_code": " df[df['Status'] == 'Approved'].groupby('Loss_Type')['Claim_Amount'].mean().reset_index(name='Avg_Claim_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with settlement ageing greater than 30 days.",
+        "pandas_code": " df[df['Settlement_Ageing'] > 30].groupby(['Provider_Name','ICD']).size().reset_index(name='Delayed_Claims_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims where deduction amount is more than 20% of claim amount.",
+        "pandas_code": " df[(df['Deduction_Amount'] / df['Claim_Amount']) > 0.2].groupby(['Provider_Name','ICD']).size().reset_index(name='High_Deduction_Claims')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show gender-wise distribution of claims by product.",
+        "pandas_code": " df.groupby(['Product','Gender']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Find providers with highest rejection rate.",
+        "pandas_code": "status_counts = df.groupby(['Provider_Name','Status']).size().unstack(fill_value=0); status_counts['Rejection_Rate'] = status_counts['Rejected'] / (status_counts['Approved'] + status_counts['Rejected'] + status_counts['Pending']);  status_counts['Rejection_Rate'].nlargest(10).reset_index()",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show average approved amount by ICD code for critical illness claims.",
+        "pandas_code": " df[df['Loss_Type'] == 'Critical Illness'].groupby('ICD')['Approved_Amount'].mean().reset_index(name='Avg_Approved_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims that were approved but not paid within 15 days of approval.",
+        "pandas_code": "df['Claim_Approved_Date'] = pd.to_datetime(df['Claim_Approved_Date']); df['Claim_Paid_Date'] = pd.to_datetime(df['Claim_Paid_Date']);  df[(df['Status'] == 'Paid') & ((df['Claim_Paid_Date'] - df['Claim_Approved_Date']).dt.days > 15)].groupby(['Provider_Name','ICD']).size().reset_index(name='Delayed_Payment_Claims')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show top 5 most common ICD codes across all claims.",
+        "pandas_code": " df['ICD'].value_counts().head(5).reset_index()",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Find average hospitalization duration by provider.",
+        "pandas_code": "df['Date_of_Admission'] = pd.to_datetime(df['Date_of_Admission']); df['Date_of_Discharge'] = pd.to_datetime(df['Date_of_Discharge']); df['Hospitalization_Days'] = (df['Date_of_Discharge'] - df['Date_of_Admission']).dt.days;  df.groupby('Provider_Name')['Hospitalization_Days'].mean().reset_index(name='Avg_Hospitalization_Days')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with approved amount less than 50% of claim amount.",
+        "pandas_code": " df[(df['Approved_Amount'] / df['Claim_Amount']) < 0.5].groupby(['Provider_Name','ICD']).size().reset_index(name='Low_Approval_Claims')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List quarterly claim trends by product for the current year.",
+        "pandas_code": "current_year = pd.Timestamp.now().year; df['Claim_Quarter'] = pd.to_datetime(df['Claim_Created_Date']).dt.quarter;  df[pd.to_datetime(df['Claim_Created_Date']).dt.year == current_year].groupby(['Product','Claim_Quarter']).size().reset_index(name='Quarterly_Claims')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show providers with highest average claim amounts.",
+        "pandas_code": " df.groupby('Provider_Name')['Claim_Amount'].mean().nlargest(10).reset_index(name='Avg_Claim_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Find claims where patient nationality is different from country of treatment.",
+        "pandas_code": " df[df['Nationality'] != df['Country']].groupby(['Provider_Name','ICD']).size().reset_index(name='Foreign_National_Claims')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show status-wise claim distribution for last 6 months.",
+        "pandas_code": "six_months_ago = pd.Timestamp.now() - pd.DateOffset(months=6);  df[pd.to_datetime(df['Claim_Created_Date']) >= six_months_ago].groupby('Status').size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with same-day admission and discharge.",
+        "pandas_code": "df['Date_of_Admission'] = pd.to_datetime(df['Date_of_Admission']); df['Date_of_Discharge'] = pd.to_datetime(df['Date_of_Discharge']);  df[df['Date_of_Admission'] == df['Date_of_Discharge']].groupby(['Provider_Name','ICD']).size().reset_index(name='Same_Day_Claims')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show average deduction percentage by loss type.",
+        "pandas_code": "df['Deduction_Percentage'] = (df['Deduction_Amount'] / df['Claim_Amount']) * 100;  df.groupby('Loss_Type')['Deduction_Percentage'].mean().reset_index(name='Avg_Deduction_Percentage')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Find providers with fastest claim approval (average days from creation to approval).",
+        "pandas_code": "df['Claim_Created_Date'] = pd.to_datetime(df['Claim_Created_Date']); df['Claim_Approved_Date'] = pd.to_datetime(df['Claim_Approved_Date']); df['Approval_Days'] = (df['Claim_Approved_Date'] - df['Claim_Created_Date']).dt.days;  df[df['Status'].isin(['Approved','Paid'])].groupby('Provider_Name')['Approval_Days'].mean().nsmallest(10).reset_index(name='Avg_Approval_Days')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims ratio (approved vs rejected) by product.",
+        "pandas_code": "status_by_product = df[df['Status'].isin(['Approved','Rejected'])].groupby(['Product','Status']).size().unstack(fill_value=0); status_by_product['Approval_Ratio'] = status_by_product['Approved'] / (status_by_product['Approved'] + status_by_product['Rejected']);  status_by_product['Approval_Ratio'].reset_index()",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with highest difference between claim amount and approved amount.",
+        "pandas_code": "df['Amount_Difference'] = df['Claim_Amount'] - df['Approved_Amount'];  df.nlargest(10, 'Amount_Difference')[['Claim_Number','Provider_Name','Claim_Amount','Approved_Amount','Amount_Difference']]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show monthly average claim amount by provider for the last year.",
+        "pandas_code": "current_year = pd.Timestamp.now().year; last_year = current_year - 1; df['Claim_Month'] = pd.to_datetime(df['Claim_Created_Date']).dt.month;  df[pd.to_datetime(df['Claim_Created_Date']).dt.year == last_year].groupby(['Provider_Name','Claim_Month'])['Claim_Amount'].mean().reset_index(name='Monthly_Avg_Claim_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Find most common ICD codes for male vs female patients.",
+        "pandas_code": " df.groupby(['Gender','ICD']).size().reset_index(name='Claim_Count').sort_values(['Gender','Claim_Count'], ascending=[True,False])",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims where paid amount is zero but status is approved.",
+        "pandas_code": " df[(df['Paid_Amount'] == 0) & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Unpaid_Approved_Claims')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List providers with highest number of pending claims older than 30 days.",
+        "pandas_code": "df['Days_Pending'] = (pd.Timestamp.now() - pd.to_datetime(df['Claim_Created_Date'])).dt.days;  df[(df['Status'] == 'Pending') & (df['Days_Pending'] > 30)].groupby('Provider_Name').size().nlargest(10).reset_index(name='Old_Pending_Claims')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show seasonal trend of claims by loss type.",
+        "pandas_code": "df['Claim_Month'] = pd.to_datetime(df['Claim_Created_Date']).dt.month;  df.groupby(['Loss_Type','Claim_Month']).size().reset_index(name='Monthly_Claims')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Find correlation between claim amount and hospitalization days.",
+        "pandas_code": "df['Date_of_Admission'] = pd.to_datetime(df['Date_of_Admission']); df['Date_of_Discharge'] = pd.to_datetime(df['Date_of_Discharge']); df['Hospitalization_Days'] = (df['Date_of_Discharge'] - df['Date_of_Admission']).dt.days; correlation = df[['Claim_Amount','Hospitalization_Days']].corr().iloc[0,1];  pd.DataFrame({'Correlation': [correlation]})",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims distribution by relation to policy holder.",
+        "pandas_code": " df.groupby('Relation_Nm').size().reset_index(name='Claim_Count').sort_values('Claim_Count', ascending=False)",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List top 5 diseases (ICD descriptions) with highest average claim amounts.",
+        "pandas_code": " df.groupby('Icd Disease')['Claim_Amount'].mean().nlargest(5).reset_index(name='Avg_Claim_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims where claim amount > 50000 and status is approved and approved amount > 40000.",
+        "pandas_code": " df[(df['Claim_Amount'] > 50000) & (df['Status'] == 'Approved') & (df['Approved_Amount'] > 40000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims for male patients with claim amount > 60000 and approved amount > 50000 and loss type 'Hospitalization'.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Claim_Amount'] > 60000) & (df['Approved_Amount'] > 50000) & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for product 'Arogya Sanjeevani' with claim amount between 30000-80000 and approved amount > 40000 and status approved.",
+        "pandas_code": " df[(df['Product'] == 'Arogya Sanjeevani') & (df['Claim_Amount'].between(30000,80000)) & (df['Approved_Amount'] > 40000) & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider-wise claims for policy 'POL12345' with claim amount > 70000 and paid amount < 60000 and approved amount > 50000.",
+        "pandas_code": " df[(df['Policy_Number'] == 'POL12345') & (df['Claim_Amount'] > 70000) & (df['Paid_Amount'] < 60000) & (df['Approved_Amount'] > 50000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider and ICD-wise claims for female patients with nationality 'Sample' and claim amount > 45000 and approved amount > 35000.",
+        "pandas_code": " df[(df['Gender'] == 'Female') & (df['Nationality'] == 'Sample') & (df['Claim_Amount'] > 45000) & (df['Approved_Amount'] > 35000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List ICD-wise provider claims for loss type 'Critical Illness' with claim amount > 80000 and paid amount > 60000 and status paid.",
+        "pandas_code": " df[(df['Loss_Type'] == 'Critical Illness') & (df['Claim_Amount'] > 80000) & (df['Paid_Amount'] > 60000) & (df['Status'] == 'Paid')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise claims for scheme 'Narayana Plus' with claim amount between 40000-90000 and approved amount > 30000 and deduction amount < 10000.",
+        "pandas_code": " df[(df['Scheme'] == 'Narayana Plus') & (df['Claim_Amount'].between(40000,90000)) & (df['Approved_Amount'] > 30000) & (df['Deduction_Amount'] < 10000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims for male patients with product 'Narayana Group Health' and claim amount > 50000 and settlement ageing < 30 days.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Product'] == 'Narayana Group Health') & (df['Claim_Amount'] > 50000) & (df['Settlement_Ageing'] < 30)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims with status rejected and claim amount > 60000 and approved amount > 0 and paid amount = 0.",
+        "pandas_code": " df[(df['Status'] == 'Rejected') & (df['Claim_Amount'] > 60000) & (df['Approved_Amount'] > 0) & (df['Paid_Amount'] == 0)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider-wise claims for loss type 'Accident' with claim amount > 70000 and approved amount between 50000-80000 and status approved.",
+        "pandas_code": " df[(df['Loss_Type'] == 'Accident') & (df['Claim_Amount'] > 70000) & (df['Approved_Amount'].between(50000,80000)) & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider and ICD-wise claims for female patients with policy 'POL56789' and claim amount > 55000 and paid amount > 40000 and hospitalization days > 5.",
+        "pandas_code": "df['Hospitalization_Days'] = (pd.to_datetime(df['Date_of_Discharge']) - pd.to_datetime(df['Date_of_Admission'])).dt.days;  df[(df['Gender'] == 'Female') & (df['Policy_Number'] == 'POL56789') & (df['Claim_Amount'] > 55000) & (df['Paid_Amount'] > 40000) & (df['Hospitalization_Days'] > 5)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give ICD-wise provider claims with nationality 'Sample' and claim amount > 60000 and approved amount > 45000 and deduction amount > 5000.",
+        "pandas_code": " df[(df['Nationality'] == 'Sample') & (df['Claim_Amount'] > 60000) & (df['Approved_Amount'] > 45000) & (df['Deduction_Amount'] > 5000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise claims for product 'Arogya Sanjeevani' with loss type 'Hospitalization' and claim amount between 50000-100000 and status approved and paid amount > 30000.",
+        "pandas_code": " df[(df['Product'] == 'Arogya Sanjeevani') & (df['Loss_Type'] == 'Hospitalization') & (df['Claim_Amount'].between(50000,100000)) & (df['Status'] == 'Approved') & (df['Paid_Amount'] > 30000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims for male patients with scheme 'Narayana Plus' and claim amount > 65000 and approved amount > 55000 and settlement ageing between 15-45 days.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Scheme'] == 'Narayana Plus') & (df['Claim_Amount'] > 65000) & (df['Approved_Amount'] > 55000) & (df['Settlement_Ageing'].between(15,45))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims with status pending and claim amount > 40000 and approved amount = 0 and created in last 30 days.",
+        "pandas_code": "last_30_days = pd.Timestamp.now() - pd.DateOffset(days=30);  df[(df['Status'] == 'Pending') & (df['Claim_Amount'] > 40000) & (df['Approved_Amount'] == 0) & (pd.to_datetime(df['Claim_Created_Date']) >= last_30_days)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider-wise claims for loss type 'Critical Illness' with claim amount > 90000 and paid amount between 60000-80000 and approval within 10 days.",
+        "pandas_code": "df['Approval_Days'] = (pd.to_datetime(df['Claim_Approved_Date']) - pd.to_datetime(df['Claim_Created_Date'])).dt.days;  df[(df['Loss_Type'] == 'Critical Illness') & (df['Claim_Amount'] > 90000) & (df['Paid_Amount'].between(60000,80000)) & (df['Approval_Days'] <= 10)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider and ICD-wise claims for female patients with nationality 'Sample' and product 'Narayana Group Health' and claim amount > 75000 and status approved.",
+        "pandas_code": " df[(df['Gender'] == 'Female') & (df['Nationality'] == 'Sample') & (df['Product'] == 'Narayana Group Health') & (df['Claim_Amount'] > 75000) & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List ICD-wise provider claims with policy 'POL12345' and loss type 'Accident' and claim amount between 60000-120000 and paid amount > 40000 and deduction amount < 8000.",
+        "pandas_code": " df[(df['Policy_Number'] == 'POL12345') & (df['Loss_Type'] == 'Accident') & (df['Claim_Amount'].between(60000,120000)) & (df['Paid_Amount'] > 40000) & (df['Deduction_Amount'] < 8000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise claims for male patients with scheme 'Arogya Sanjeevani' and claim amount > 50000 and approved amount > 40000 and paid amount between 35000-45000.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Scheme'] == 'Arogya Sanjeevani') & (df['Claim_Amount'] > 50000) & (df['Approved_Amount'] > 40000) & (df['Paid_Amount'].between(35000,45000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims with status rejected and claim amount > 70000 and approved amount > 0 and created in last quarter.",
+        "pandas_code": "last_quarter_start = pd.Timestamp.now() - pd.offsets.QuarterBegin(startingMonth=1);  df[(df['Status'] == 'Rejected') & (df['Claim_Amount'] > 70000) & (df['Approved_Amount'] > 0) & (pd.to_datetime(df['Claim_Created_Date']) >= last_quarter_start)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for product 'Narayana Group Health' with loss type 'Hospitalization' and claim amount > 80000 and approved amount between 60000-90000 and settlement ageing < 25 days.",
+        "pandas_code": " df[(df['Product'] == 'Narayana Group Health') & (df['Loss_Type'] == 'Hospitalization') & (df['Claim_Amount'] > 80000) & (df['Approved_Amount'].between(60000,90000)) & (df['Settlement_Ageing'] < 25)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider-wise claims for female patients with policy 'POL56789' and claim amount > 65000 and paid amount > 50000 and hospitalization duration 7-14 days.",
+        "pandas_code": "df['Hospitalization_Days'] = (pd.to_datetime(df['Date_of_Discharge']) - pd.to_datetime(df['Date_of_Admission'])).dt.days;  df[(df['Gender'] == 'Female') & (df['Policy_Number'] == 'POL56789') & (df['Claim_Amount'] > 65000) & (df['Paid_Amount'] > 50000) & (df['Hospitalization_Days'].between(7,14))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider and ICD-wise claims with nationality 'Sample' and claim amount between 50000-150000 and approved amount > 40000 and status paid and deduction amount between 5000-15000.",
+        "pandas_code": " df[(df['Nationality'] == 'Sample') & (df['Claim_Amount'].between(50000,150000)) & (df['Approved_Amount'] > 40000) & (df['Status'] == 'Paid') & (df['Deduction_Amount'].between(5000,15000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give ICD-wise provider claims for loss type 'Accident' with claim amount > 85000 and approved amount > 70000 and paid amount between 60000-75000 and approval within 7 days.",
+        "pandas_code": "df['Approval_Days'] = (pd.to_datetime(df['Claim_Approved_Date']) - pd.to_datetime(df['Claim_Created_Date'])).dt.days;  df[(df['Loss_Type'] == 'Accident') & (df['Claim_Amount'] > 85000) & (df['Approved_Amount'] > 70000) & (df['Paid_Amount'].between(60000,75000)) & (df['Approval_Days'] <= 7)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise claims for male patients with product 'Arogya Sanjeevani' and scheme 'Narayana Plus' and claim amount > 70000 and approved amount > 60000 and status approved.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Product'] == 'Arogya Sanjeevani') & (df['Scheme'] == 'Narayana Plus') & (df['Claim_Amount'] > 70000) & (df['Approved_Amount'] > 60000) & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims with policy 'POL12345' and loss type 'Critical Illness' and claim amount between 80000-200000 and paid amount > 65000 and settlement ageing between 20-40 days.",
+        "pandas_code": " df[(df['Policy_Number'] == 'POL12345') & (df['Loss_Type'] == 'Critical Illness') & (df['Claim_Amount'].between(80000,200000)) & (df['Paid_Amount'] > 65000) & (df['Settlement_Ageing'].between(20,40))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for female patients with nationality 'Sample' and claim amount > 90000 and approved amount between 70000-100000 and paid amount > 60000 and deduction amount < 10000.",
+        "pandas_code": " df[(df['Gender'] == 'Female') & (df['Nationality'] == 'Sample') & (df['Claim_Amount'] > 90000) & (df['Approved_Amount'].between(70000,100000)) & (df['Paid_Amount'] > 60000) & (df['Deduction_Amount'] < 10000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider-wise claims with status pending and claim amount between 40000-80000 and approved amount = 0 and created in last 45 days and loss type 'Hospitalization'.",
+        "pandas_code": "last_45_days = pd.Timestamp.now() - pd.DateOffset(days=45);  df[(df['Status'] == 'Pending') & (df['Claim_Amount'].between(40000,80000)) & (df['Approved_Amount'] == 0) & (pd.to_datetime(df['Claim_Created_Date']) >= last_45_days) & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider and ICD-wise claims for male patients with product 'Narayana Group Health' and policy 'POL56789' and claim amount > 75000 and approved amount > 65000 and paid amount between 55000-70000.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Product'] == 'Narayana Group Health') & (df['Policy_Number'] == 'POL56789') & (df['Claim_Amount'] > 75000) & (df['Approved_Amount'] > 65000) & (df['Paid_Amount'].between(55000,70000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List ICD-wise provider claims with loss type 'Accident' and claim amount between 70000-150000 and approved amount > 60000 and status approved and settlement ageing < 30 days and deduction amount between 8000-12000.",
+        "pandas_code": " df[(df['Loss_Type'] == 'Accident') & (df['Claim_Amount'].between(70000,150000)) & (df['Approved_Amount'] > 60000) & (df['Status'] == 'Approved') & (df['Settlement_Ageing'] < 30) & (df['Deduction_Amount'].between(8000,12000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise claims for female patients with scheme 'Arogya Sanjeevani' and claim amount > 80000 and approved amount between 65000-95000 and paid amount > 55000 and hospitalization days > 10.",
+        "pandas_code": "df['Hospitalization_Days'] = (pd.to_datetime(df['Date_of_Discharge']) - pd.to_datetime(df['Date_of_Admission'])).dt.days;  df[(df['Gender'] == 'Female') & (df['Scheme'] == 'Arogya Sanjeevani') & (df['Claim_Amount'] > 80000) & (df['Approved_Amount'].between(65000,95000)) & (df['Paid_Amount'] > 55000) & (df['Hospitalization_Days'] > 10)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider and ICD-wise claims with status rejected and claim amount > 95000 and approved amount > 0 and created in last 60 days and loss type 'Critical Illness'.",
+        "pandas_code": "last_60_days = pd.Timestamp.now() - pd.DateOffset(days=60);  df[(df['Status'] == 'Rejected') & (df['Claim_Amount'] > 95000) & (df['Approved_Amount'] > 0) & (pd.to_datetime(df['Claim_Created_Date']) >= last_60_days) & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for male patients with nationality 'Sample' and product 'Narayana Group Health' and policy 'POL12345' and claim amount between 85000-180000 and approved amount > 75000 and status paid.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Nationality'] == 'Sample') & (df['Product'] == 'Narayana Group Health') & (df['Policy_Number'] == 'POL12345') & (df['Claim_Amount'].between(85000,180000)) & (df['Approved_Amount'] > 75000) & (df['Status'] == 'Paid')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider-wise claims with loss type 'Hospitalization' and claim amount > 100000 and approved amount between 80000-120000 and paid amount > 70000 and settlement ageing between 15-35 days and deduction amount < 15000.",
+        "pandas_code": " df[(df['Loss_Type'] == 'Hospitalization') & (df['Claim_Amount'] > 100000) & (df['Approved_Amount'].between(80000,120000)) & (df['Paid_Amount'] > 70000) & (df['Settlement_Ageing'].between(15,35)) & (df['Deduction_Amount'] < 15000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider and ICD-wise claims for female patients with scheme 'Narayana Plus' and claim amount > 90000 and approved amount > 80000 and paid amount between 70000-85000 and approval within 5 days.",
+        "pandas_code": "df['Approval_Days'] = (pd.to_datetime(df['Claim_Approved_Date']) - pd.to_datetime(df['Claim_Created_Date'])).dt.days;  df[(df['Gender'] == 'Female') & (df['Scheme'] == 'Narayana Plus') & (df['Claim_Amount'] > 90000) & (df['Approved_Amount'] > 80000) & (df['Paid_Amount'].between(70000,85000)) & (df['Approval_Days'] <= 5)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give ICD-wise provider claims with policy 'POL56789' and loss type 'Accident' and claim amount between 95000-200000 and approved amount > 85000 and status approved and paid amount > 75000 and hospitalization days 8-20.",
+        "pandas_code": "df['Hospitalization_Days'] = (pd.to_datetime(df['Date_of_Discharge']) - pd.to_datetime(df['Date_of_Admission'])).dt.days;  df[(df['Policy_Number'] == 'POL56789') & (df['Loss_Type'] == 'Accident') & (df['Claim_Amount'].between(95000,200000)) & (df['Approved_Amount'] > 85000) & (df['Status'] == 'Approved') & (df['Paid_Amount'] > 75000) & (df['Hospitalization_Days'].between(8,20))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise claims for male patients with product 'Arogya Sanjeevani' and nationality 'Sample' and claim amount > 110000 and approved amount between 90000-130000 and paid amount > 80000 and settlement ageing < 20 days.",
+        "pandas_code": " df[(df['Gender'] == 'Male') & (df['Product'] == 'Arogya Sanjeevani') & (df['Nationality'] == 'Sample') & (df['Claim_Amount'] > 110000) & (df['Approved_Amount'].between(90000,130000)) & (df['Paid_Amount'] > 80000) & (df['Settlement_Ageing'] < 20)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider and ICD-wise claims with status pending and claim amount between 60000-100000 and approved amount = 0 and created in last 30 days and loss type 'Critical Illness' and patient gender female.",
+        "pandas_code": "last_30_days = pd.Timestamp.now() - pd.DateOffset(days=30);  df[(df['Status'] == 'Pending') & (df['Claim_Amount'].between(60000,100000)) & (df['Approved_Amount'] == 0) & (pd.to_datetime(df['Claim_Created_Date']) >= last_30_days) & (df['Loss_Type'] == 'Critical Illness') & (df['Gender'] == 'Female')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show ICD-wise provider claims for product 'Narayana Group Health' with policy 'POL12345' and claim amount > 120000 and approved amount > 100000 and paid amount between 90000-110000 and status paid and deduction amount between 10000-20000.",
+        "pandas_code": " df[(df['Product'] == 'Narayana Group Health') & (df['Policy_Number'] == 'POL12345') & (df['Claim_Amount'] > 120000) & (df['Approved_Amount'] > 100000) & (df['Paid_Amount'].between(90000,110000)) & (df['Status'] == 'Paid') & (df['Deduction_Amount'].between(10000,20000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Give provider-wise claims for female patients with scheme 'Arogya Sanjeevani' and loss type 'Hospitalization' and claim amount between 100000-250000 and approved amount > 95000 and paid amount > 85000 and approval within 3 days and hospitalization days > 15.",
+        "pandas_code": "df['Approval_Days'] = (pd.to_datetime(df['Claim_Approved_Date']) - pd.to_datetime(df['Claim_Created_Date'])).dt.days; df['Hospitalization_Days'] = (pd.to_datetime(df['Date_of_Discharge']) - pd.to_datetime(df['Date_of_Admission'])).dt.days;  df[(df['Gender'] == 'Female') & (df['Scheme'] == 'Arogya Sanjeevani') & (df['Loss_Type'] == 'Hospitalization') & (df['Claim_Amount'].between(100000,250000)) & (df['Approved_Amount'] > 95000) & (df['Paid_Amount'] > 85000) & (df['Approval_Days'] <= 3) & (df['Hospitalization_Days'] > 15)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show all claims grouped by provider and ICD.",
+        "pandas_code": "  df.groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List all approved claims.",
+        "pandas_code": "  df[df['Status'] == 'Approved']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with claim amount greater than 50000.",
+        "pandas_code": "  df[df['Claim_Amount'] > 50000]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List all claims for male patients.",
+        "pandas_code": "  df[df['Gender'] == 'Male']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status 'Pending'.",
+        "pandas_code": "  df[df['Status'] == 'Pending']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for product 'Arogya Sanjeevani'.",
+        "pandas_code": "  df[df['Product'] == 'Arogya Sanjeevani']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Accident'.",
+        "pandas_code": "  df[df['Loss_Type'] == 'Accident']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims where approved amount is zero.",
+        "pandas_code": "  df[df['Approved_Amount'] == 0]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with paid amount greater than 100000.",
+        "pandas_code": "  df[df['Paid_Amount'] > 100000]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for policy number 'POL12345'.",
+        "pandas_code": "  df[df['Policy_Number'] == 'POL12345']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with deduction amount less than 5000.",
+        "pandas_code": "  df[df['Deduction_Amount'] < 5000]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims where settlement ageing is more than 60 days.",
+        "pandas_code": "  df[df['Settlement_Ageing'] > 60]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients.",
+        "pandas_code": "  df[df['Gender'] == 'Female']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with nationality 'Sample'.",
+        "pandas_code": "  df[df['Nationality'] == 'Sample']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims where claim amount is between 20000 and 50000.",
+        "pandas_code": "  df[df['Claim_Amount'].between(20000,50000)]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List rejected claims.",
+        "pandas_code": "  df[df['Status'] == 'Rejected']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for scheme 'Narayana Plus'.",
+        "pandas_code": "  df[df['Scheme'] == 'Narayana Plus']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims where paid amount is zero.",
+        "pandas_code": "  df[df['Paid_Amount'] == 0]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with approved amount greater than 75000.",
+        "pandas_code": "  df[df['Approved_Amount'] > 75000]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for loss type 'Critical Illness'.",
+        "pandas_code": "  df[df['Loss_Type'] == 'Critical Illness']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise total claim amount.",
+        "pandas_code": "  df.groupby('Provider_Name')['Claim_Amount'].sum().reset_index(name='Total_Claim_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List ICD-wise claim counts.",
+        "pandas_code": "  df.groupby('ICD').size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show status-wise distribution of claims.",
+        "pandas_code": "  df.groupby('Status').size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List product-wise average claim amount.",
+        "pandas_code": "  df.groupby('Product')['Claim_Amount'].mean().reset_index(name='Avg_Claim_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show gender-wise claim counts.",
+        "pandas_code": "  df.groupby('Gender').size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List loss type-wise total approved amount.",
+        "pandas_code": "  df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index(name='Total_Approved_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show scheme-wise paid amounts.",
+        "pandas_code": "  df.groupby('Scheme')['Paid_Amount'].sum().reset_index(name='Total_Paid_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List nationality-wise claim counts.",
+        "pandas_code": "  df.groupby('Nationality').size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show policy-wise total deductions.",
+        "pandas_code": "  df.groupby('Policy_Number')['Deduction_Amount'].sum().reset_index(name='Total_Deductions')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List provider-wise average settlement ageing.",
+        "pandas_code": "  df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index(name='Avg_Settlement_Days')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show top 10 highest claim amounts.",
+        "pandas_code": "  df.nlargest(10, 'Claim_Amount')[['Claim_Number','Provider_Name','Claim_Amount']]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List bottom 10 lowest approved amounts.",
+        "pandas_code": "  df.nsmallest(10, 'Approved_Amount')[['Claim_Number','Provider_Name','Approved_Amount']]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims created in the last 30 days.",
+        "pandas_code": "last_30_days = pd.Timestamp.now() - pd.DateOffset(days=30);   df[pd.to_datetime(df['Claim_Created_Date']) >= last_30_days]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims approved in the last quarter.",
+        "pandas_code": "last_quarter_start = pd.Timestamp.now() - pd.offsets.QuarterBegin(startingMonth=1);   df[pd.to_datetime(df['Claim_Approved_Date']) >= last_quarter_start]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims paid in the current month.",
+        "pandas_code": "current_month_start = pd.Timestamp.now().replace(day=1);   df[pd.to_datetime(df['Claim_Paid_Date']) >= current_month_start]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims where claim amount is exactly 100000.",
+        "pandas_code": "  df[df['Claim_Amount'] == 100000]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with no deductions.",
+        "pandas_code": "  df[df['Deduction_Amount'] == 0]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims where approved amount equals claim amount.",
+        "pandas_code": "  df[df['Claim_Amount'] == df['Approved_Amount']]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims where paid amount equals approved amount.",
+        "pandas_code": "  df[df['Paid_Amount'] == df['Approved_Amount']]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with different claim and approved amounts.",
+        "pandas_code": "  df[df['Claim_Amount'] != df['Approved_Amount']]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims where patient country is different from nationality.",
+        "pandas_code": "  df[df['Country'] != df['Nationality']]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims created by specific user 'Admin'.",
+        "pandas_code": "  df[df['Created_User'] == 'Admin']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims approved by user 'Manager'.",
+        "pandas_code": "  df[df['Approved_User'] == 'Manager']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims paid by user 'Cashier'.",
+        "pandas_code": "  df[df['Paid_User'] == 'Cashier']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims rejected by user 'Supervisor'.",
+        "pandas_code": "  df[df['Rejected_User'] == 'Supervisor']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with specific ICD code 'I10'.",
+        "pandas_code": "  df[df['ICD'] == 'I10']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for specific disease 'Hypertension'.",
+        "pandas_code": "  df[df['Icd Disease'] == 'Hypertension']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims where relation is 'Self'.",
+        "pandas_code": "  df[df['Relation_Nm'] == 'Self']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims where relation is 'Spouse'.",
+        "pandas_code": "  df[df['Relation_Nm'] == 'Spouse']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims where relation is 'Child'.",
+        "pandas_code": "  df[df['Relation_Nm'] == 'Child']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with claim type 'Cashless'.",
+        "pandas_code": "  df[df['Claim_Type'] == 'Cashless']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with claim type 'Reimbursement'.",
+        "pandas_code": "  df[df['Claim_Type'] == 'Reimbursement']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status group 'Completed'.",
+        "pandas_code": "  df[df['Status_Group'] == 'Completed']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status group 'In Progress'.",
+        "pandas_code": "  df[df['Status_Group'] == 'In Progress']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims where final outcome date is in last week.",
+        "pandas_code": "last_week = pd.Timestamp.now() - pd.DateOffset(days=7);   df[pd.to_datetime(df['Final_Outcome_Date']) >= last_week]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with transaction date in current year.",
+        "pandas_code": "current_year_start = pd.Timestamp.now().replace(month=1, day=1);   df[pd.to_datetime(df['Transaction_Date']) >= current_year_start]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss date in last 6 months.",
+        "pandas_code": "six_months_ago = pd.Timestamp.now() - pd.DateOffset(months=6);   df[pd.to_datetime(df['Loss_Date']) >= six_months_ago]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims where date of admission was on weekends.",
+        "pandas_code": "df['Admission_Day'] = pd.to_datetime(df['Date_of_Admission']).dt.dayofweek;   df[df['Admission_Day'].isin([5,6])]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with same admission and discharge date.",
+        "pandas_code": "  df[pd.to_datetime(df['Date_of_Admission']) == pd.to_datetime(df['Date_of_Discharge'])]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with hospitalization duration more than 30 days.",
+        "pandas_code": "df['Hospitalization_Days'] = (pd.to_datetime(df['Date_of_Discharge']) - pd.to_datetime(df['Date_of_Admission'])).dt.days;   df[df['Hospitalization_Days'] > 30]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with provider code starting with 'HOS'.",
+        "pandas_code": "  df[df['Provider_Code'].str.startswith('HOS', na=False)]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with patient name containing 'Kumar'.",
+        "pandas_code": "  df[df['Patient_Name'].str.contains('Kumar', na=False)]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with specific status code 'APP'.",
+        "pandas_code": "  df[df['Status_Code'] == 'APP']",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List duplicate claim numbers.",
+        "pandas_code": "  df[df.duplicated('Claim_Number', keep=False)]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with missing approved date.",
+        "pandas_code": "  df[df['Claim_Approved_Date'].isna()]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with missing paid date.",
+        "pandas_code": "  df[df['Claim_Paid_Date'].isna()]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with missing rejection date.",
+        "pandas_code": "  df[df['Rejected_Date'].isna()]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with all dates present.",
+        "pandas_code": "  df[df['Claim_Created_Date'].notna() & df['Claim_Approved_Date'].notna() & df['Claim_Paid_Date'].notna()]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims where approved amount is more than 80% of claim amount.",
+        "pandas_code": "  df[(df['Approved_Amount'] / df['Claim_Amount']) > 0.8]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims where deduction is more than 30% of claim amount.",
+        "pandas_code": "  df[(df['Deduction_Amount'] / df['Claim_Amount']) > 0.3]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with zero settlement ageing.",
+        "pandas_code": "  df[df['Settlement_Ageing'] == 0]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims sorted by claim amount descending.",
+        "pandas_code": "  df.sort_values('Claim_Amount', ascending=False)",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims sorted by creation date ascending.",
+        "pandas_code": "  df.sort_values('Claim_Created_Date', ascending=True)",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List unique provider names.",
+        "pandas_code": "  df['Provider_Name'].unique().tolist()",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show distinct ICD codes.",
+        "pandas_code": "  df['ICD'].unique().tolist()",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List all possible loss types.",
+        "pandas_code": "  df['Loss_Type'].unique().tolist()",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show all available products.",
+        "pandas_code": "  df['Product'].unique().tolist()",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List all status values.",
+        "pandas_code": "  df['Status'].unique().tolist()",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show count of claims by provider and status.",
+        "pandas_code": "  df.groupby(['Provider_Name','Status']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List average claim amount by gender and loss type.",
+        "pandas_code": "  df.groupby(['Gender','Loss_Type'])['Claim_Amount'].mean().reset_index(name='Avg_Claim_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show total paid amount by product and scheme.",
+        "pandas_code": "  df.groupby(['Product','Scheme'])['Paid_Amount'].sum().reset_index(name='Total_Paid_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claim counts by nationality and gender.",
+        "pandas_code": "  df.groupby(['Nationality','Gender']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show maximum approved amount by provider.",
+        "pandas_code": "  df.groupby('Provider_Name')['Approved_Amount'].max().reset_index(name='Max_Approved_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List minimum deduction amount by loss type.",
+        "pandas_code": "  df.groupby('Loss_Type')['Deduction_Amount'].min().reset_index(name='Min_Deduction_Amount')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show average settlement ageing by status.",
+        "pandas_code": "  df.groupby('Status')['Settlement_Ageing'].mean().reset_index(name='Avg_Settlement_Days')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims count by month of creation.",
+        "pandas_code": "df['Creation_Month'] = pd.to_datetime(df['Claim_Created_Date']).dt.to_period('M');   df.groupby('Creation_Month').size().reset_index(name='Monthly_Claims')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims count by day of week.",
+        "pandas_code": "df['Creation_Day'] = pd.to_datetime(df['Claim_Created_Date']).dt.day_name();   df.groupby('Creation_Day').size().reset_index(name='Daily_Claims')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with exactly 10000 approved amount.",
+        "pandas_code": "  df[df['Approved_Amount'] == 10000]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with paid amount between 5000 and 15000.",
+        "pandas_code": "  df[df['Paid_Amount'].between(5000,15000)]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims where deduction amount is exactly 10% of claim amount.",
+        "pandas_code": "  df[abs((df['Deduction_Amount'] / df['Claim_Amount']) - 0.1) < 0.01]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with provider name containing 'Hospital'.",
+        "pandas_code": "  df[df['Provider_Name'].str.contains('Hospital', na=False)]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with patient name starting with 'A'.",
+        "pandas_code": "  df[df['Patient_Name'].str.startswith('A', na=False)]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with ICD disease containing 'Cancer'.",
+        "pandas_code": "  df[df['Icd Disease'].str.contains('Cancer', na=False)]",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List all claims without any filters.",
+        "pandas_code": "  df",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show basic statistics of numerical columns.",
+        "pandas_code": "  df[['Claim_Amount','Approved_Amount','Paid_Amount','Deduction_Amount','Settlement_Ageing']].describe()",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List data types of all columns.",
+        "pandas_code": "  pd.DataFrame(df.dtypes, columns=['Data_Type']).reset_index().rename(columns={'index': 'Column_Name'})",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show count of missing values in each column.",
+        "pandas_code": "  pd.DataFrame(df.isnull().sum(), columns=['Missing_Count']).reset_index().rename(columns={'index': 'Column_Name'})",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List unique values count for each categorical column.",
+        "pandas_code": "categorical_cols = df.select_dtypes(include=['object']).columns;   pd.DataFrame({col: df[col].nunique() for col in categorical_cols}, index=['Unique_Count']).T.reset_index().rename(columns={'index': 'Column_Name'})",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show provider-wise ICD-wise claims where claim amount > 50000 and status is approved.",
+        "pandas_code": "  df[(df['Claim_Amount'] > 50000) & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with approved amount > 40000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Approved_Amount'] > 40000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for product 'Arogya Sanjeevani' with status pending.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with claim amount between 30000-70000 and loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Claim_Amount'].between(30000,70000)) & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with paid amount > 25000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Paid_Amount'] > 25000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and status approved.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for loss type 'Accident' with approved amount > 35000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Approved_Amount'] > 35000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with nationality 'Sample' and claim amount > 45000.",
+        "pandas_code": "  df[(df['Nationality'] == 'Sample') & (df['Claim_Amount'] > 45000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for scheme 'Narayana Plus' with paid amount < 20000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Paid_Amount'] < 20000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status rejected and claim amount > 60000.",
+        "pandas_code": "  df[(df['Status'] == 'Rejected') & (df['Claim_Amount'] > 60000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for product 'Narayana Group Health' with deduction amount > 5000.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Deduction_Amount'] > 5000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with loss type 'Critical Illness'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).status().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with approved amount between 20000-50000 and status paid.",
+        "pandas_code": "  df[(df['Approved_Amount'].between(20000,50000)) & (df['Status'] == 'Paid')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with policy 'POL56789'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Policy_Number'] == 'POL56789')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with claim amount > 80000 and paid amount < 40000.",
+        "pandas_code": "  df[(df['Claim_Amount'] > 80000) & (df['Paid_Amount'] < 40000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for loss type 'Hospitalization' with settlement ageing > 30 days.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Settlement_Ageing'] > 30)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with nationality 'Sample' and approved amount > 25000.",
+        "pandas_code": "  df[(df['Nationality'] == 'Sample') & (df['Approved_Amount'] > 25000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for product 'Arogya Sanjeevani' with claim amount between 40000-90000.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Claim_Amount'].between(40000,90000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status pending and approved amount = 0.",
+        "pandas_code": "  df[(df['Status'] == 'Pending') & (df['Approved_Amount'] == 0)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with paid amount between 15000-35000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Paid_Amount'].between(15000,35000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Accident' and deduction amount < 3000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Deduction_Amount'] < 3000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and paid amount > 45000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Paid_Amount'] > 45000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with loss type 'Critical Illness'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and approved amount > 30000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Approved_Amount'] > 30000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status rejected and paid amount = 0.",
+        "pandas_code": "  df[(df['Status'] == 'Rejected') & (df['Paid_Amount'] == 0)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for product 'Narayana Group Health' with claim amount > 70000.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Claim_Amount'] > 70000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with nationality 'Sample' and status approved.",
+        "pandas_code": "  df[(df['Nationality'] == 'Sample') & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with policy 'POL56789'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Policy_Number'] == 'POL56789')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Hospitalization' and paid amount between 20000-60000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Paid_Amount'].between(20000,60000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with claim amount < 20000 and status pending.",
+        "pandas_code": "  df[(df['Claim_Amount'] < 20000) & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with approved amount between 30000-70000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Approved_Amount'].between(30000,70000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and loss type 'Accident'.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Loss_Type'] == 'Accident')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL12345' and deduction amount > 8000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Deduction_Amount'] > 8000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with scheme 'Narayana Plus'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Scheme'] == 'Narayana Plus')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status paid and settlement ageing < 15 days.",
+        "pandas_code": "  df[(df['Status'] == 'Paid') & (df['Settlement_Ageing'] < 15)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Critical Illness' and claim amount > 90000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Claim_Amount'] > 90000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with nationality 'Sample'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with approved amount > 50000 and paid amount < 30000.",
+        "pandas_code": "  df[(df['Approved_Amount'] > 50000) & (df['Paid_Amount'] < 30000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with product 'Narayana Group Health' and status rejected.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Status'] == 'Rejected')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with claim amount between 50000-100000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Claim_Amount'].between(50000,100000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Accident' and paid amount > 35000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Paid_Amount'] > 35000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and approved amount between 25000-75000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Approved_Amount'].between(25000,75000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with deduction amount < 2000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Deduction_Amount'] < 2000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Arogya Sanjeevani' and claim amount > 60000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Claim_Amount'] > 60000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status approved and settlement ageing between 10-30 days.",
+        "pandas_code": "  df[(df['Status'] == 'Approved') & (df['Settlement_Ageing'].between(10,30))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with nationality 'Sample' and paid amount > 40000.",
+        "pandas_code": "  df[(df['Nationality'] == 'Sample') & (df['Paid_Amount'] > 40000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and deduction amount between 3000-8000.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Deduction_Amount'].between(3000,8000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL12345' and loss type 'Critical Illness'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with scheme 'Narayana Plus'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Scheme'] == 'Narayana Plus')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status pending and claim amount between 25000-75000.",
+        "pandas_code": "  df[(df['Status'] == 'Pending') & (df['Claim_Amount'].between(25000,75000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and approved amount < 20000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Approved_Amount'] < 20000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with nationality 'Sample'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Narayana Group Health' and paid amount between 15000-45000.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Paid_Amount'].between(15000,45000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL56789' and status pending.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with claim amount > 80000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Claim_Amount'] > 80000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Hospitalization' and deduction amount > 7000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Deduction_Amount'] > 7000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Arogya Sanjeevani' and approved amount > 35000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Approved_Amount'] > 35000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status rejected and claim amount between 40000-90000.",
+        "pandas_code": "  df[(df['Status'] == 'Rejected') & (df['Claim_Amount'].between(40000,90000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with paid amount > 50000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Paid_Amount'] > 50000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with nationality 'Sample' and loss type 'Critical Illness'.",
+        "pandas_code": "  df[(df['Nationality'] == 'Sample') & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and settlement ageing > 25 days.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Settlement_Ageing'] > 25)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL12345' and paid amount between 30000-70000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Paid_Amount'].between(30000,70000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with loss type 'Accident'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Loss_Type'] == 'Accident')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with scheme 'Narayana Plus' and claim amount < 30000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Claim_Amount'] < 30000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status approved and deduction amount < 4000.",
+        "pandas_code": "  df[(df['Status'] == 'Approved') & (df['Deduction_Amount'] < 4000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with policy 'POL56789'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Policy_Number'] == 'POL56789')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Hospitalization' and approved amount > 60000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Approved_Amount'] > 60000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with nationality 'Sample' and status pending.",
+        "pandas_code": "  df[(df['Nationality'] == 'Sample') & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Narayana Group Health' and claim amount between 60000-120000.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Claim_Amount'].between(60000,120000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with approved amount > 45000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Approved_Amount'] > 45000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and loss type 'Accident'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Loss_Type'] == 'Accident')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with scheme 'Arogya Sanjeevani' and paid amount > 40000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Paid_Amount'] > 40000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status rejected and approved amount > 0.",
+        "pandas_code": "  df[(df['Status'] == 'Rejected') & (df['Approved_Amount'] > 0)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with deduction amount between 1000-5000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Deduction_Amount'].between(1000,5000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Critical Illness' and paid amount < 25000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Paid_Amount'] < 25000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with nationality 'Sample' and claim amount between 35000-85000.",
+        "pandas_code": "  df[(df['Nationality'] == 'Sample') & (df['Claim_Amount'].between(35000,85000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and status paid.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Status'] == 'Paid')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with policy 'POL56789'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Policy_Number'] == 'POL56789')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status pending and paid amount = 0.",
+        "pandas_code": "  df[(df['Status'] == 'Pending') & (df['Paid_Amount'] == 0)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with approved amount between 40000-80000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Approved_Amount'].between(40000,80000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Accident' and status approved.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and claim amount > 90000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Claim_Amount'] > 90000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with scheme 'Arogya Sanjeevani'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Scheme'] == 'Arogya Sanjeevani')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Narayana Group Health' and approved amount > 50000.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Approved_Amount'] > 50000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status rejected and settlement ageing > 40 days.",
+        "pandas_code": "  df[(df['Status'] == 'Rejected') & (df['Settlement_Ageing'] > 40)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with loss type 'Critical Illness'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with nationality 'Sample' and paid amount between 20000-50000.",
+        "pandas_code": "  df[(df['Nationality'] == 'Sample') & (df['Paid_Amount'].between(20000,50000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and deduction amount > 6000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Deduction_Amount'] > 6000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with claim amount between 60000-110000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Claim_Amount'].between(60000,110000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Hospitalization' and status paid.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Status'] == 'Paid')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with scheme 'Narayana Plus' and approved amount between 35000-75000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Approved_Amount'].between(35000,75000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status approved and paid amount > 45000.",
+        "pandas_code": "  df[(df['Status'] == 'Approved') & (df['Paid_Amount'] > 45000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and loss type 'Critical Illness'.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL12345' and status pending.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with deduction amount > 3000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Deduction_Amount'] > 3000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Accident' and claim amount between 40000-90000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Claim_Amount'].between(40000,90000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Arogya Sanjeevani' and status rejected.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Status'] == 'Rejected')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status paid and approved amount > 55000.",
+        "pandas_code": "  df[(df['Status'] == 'Paid') & (df['Approved_Amount'] > 55000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Hospitalization' and deduction amount between 4000-9000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Deduction_Amount'].between(4000,9000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with nationality 'Sample' and scheme 'Narayana Plus'.",
+        "pandas_code": "  df[(df['Nationality'] == 'Sample') & (df['Scheme'] == 'Narayana Plus')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with product 'Narayana Group Health' and paid amount > 35000.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Paid_Amount'] > 35000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL12345' and approved amount between 30000-80000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Approved_Amount'].between(30000,80000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status pending and claim amount > 70000.",
+        "pandas_code": "  df[(df['Status'] == 'Pending') & (df['Claim_Amount'] > 70000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with scheme 'Arogya Sanjeevani'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Scheme'] == 'Arogya Sanjeevani')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Critical Illness' and status approved.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with nationality 'Sample' and product 'Narayana Group Health'.",
+        "pandas_code": "  df[(df['Nationality'] == 'Sample') & (df['Product'] == 'Narayana Group Health')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with paid amount between 25000-60000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Paid_Amount'].between(25000,60000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL56789' and loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and status paid.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Status'] == 'Paid')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with approved amount > 60000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Approved_Amount'] > 60000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and paid amount < 15000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Paid_Amount'] < 15000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with product 'Arogya Sanjeevani' and policy 'POL12345'.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Policy_Number'] == 'POL12345')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with nationality 'Sample'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status rejected and claim amount < 30000.",
+        "pandas_code": "  df[(df['Status'] == 'Rejected') & (df['Claim_Amount'] < 30000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Critical Illness' and deduction amount > 8000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Deduction_Amount'] > 8000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with paid amount > 55000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Paid_Amount'] > 55000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and scheme 'Arogya Sanjeevani'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Scheme'] == 'Arogya Sanjeevani')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status approved and deduction amount between 2000-6000.",
+        "pandas_code": "  df[(df['Status'] == 'Approved') & (df['Deduction_Amount'].between(2000,6000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with product 'Narayana Group Health' and nationality 'Sample'.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and claim amount between 40000-95000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Claim_Amount'].between(40000,95000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with policy 'POL12345'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Policy_Number'] == 'POL12345')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and approved amount between 25000-65000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Approved_Amount'].between(25000,65000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status pending and paid amount > 0.",
+        "pandas_code": "  df[(df['Status'] == 'Pending') & (df['Paid_Amount'] > 0)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with product 'Arogya Sanjeevani'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Product'] == 'Arogya Sanjeevani')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL56789' and approved amount > 40000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Approved_Amount'] > 40000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Critical Illness' and settlement ageing < 20 days.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Settlement_Ageing'] < 20)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with scheme 'Narayana Plus'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Scheme'] == 'Narayana Plus')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with nationality 'Sample' and status rejected.",
+        "pandas_code": "  df[(df['Nationality'] == 'Sample') & (df['Status'] == 'Rejected')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with product 'Narayana Group Health' and loss type 'Accident'.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Loss_Type'] == 'Accident')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with approved amount between 50000-90000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Approved_Amount'].between(50000,90000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL12345' and paid amount < 20000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Paid_Amount'] < 20000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Hospitalization' and claim amount > 95000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Claim_Amount'] > 95000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Arogya Sanjeevani' and deduction amount > 7000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Deduction_Amount'] > 7000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status paid and claim amount between 80000-150000.",
+        "pandas_code": "  df[(df['Status'] == 'Paid') & (df['Claim_Amount'].between(80000,150000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Critical Illness' and approved amount > 70000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Approved_Amount'] > 70000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and paid amount between 30000-70000.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Paid_Amount'].between(30000,70000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with loss type 'Accident'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Loss_Type'] == 'Accident')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and scheme 'Narayana Plus'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Scheme'] == 'Narayana Plus')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status approved and nationality 'Sample'.",
+        "pandas_code": "  df[(df['Status'] == 'Approved') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with deduction amount between 4000-8000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Deduction_Amount'].between(4000,8000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Hospitalization' and status pending.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Narayana Group Health' and policy 'POL56789'.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Policy_Number'] == 'POL56789')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with paid amount < 10000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Paid_Amount'] < 10000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Arogya Sanjeevani' and approved amount between 40000-85000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Approved_Amount'].between(40000,85000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status rejected and loss type 'Accident'.",
+        "pandas_code": "  df[(df['Status'] == 'Rejected') & (df['Loss_Type'] == 'Accident')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL12345' and nationality 'Sample'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Critical Illness' and paid amount > 60000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Paid_Amount'] > 60000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with product 'Arogya Sanjeevani'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Product'] == 'Arogya Sanjeevani')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status paid and deduction amount < 3000.",
+        "pandas_code": "  df[(df['Status'] == 'Paid') & (df['Deduction_Amount'] < 3000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and scheme 'Narayana Plus'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Scheme'] == 'Narayana Plus')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with product 'Narayana Group Health' and status approved.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with approved amount between 55000-95000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Approved_Amount'].between(55000,95000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Accident' and settlement ageing > 35 days.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Settlement_Ageing'] > 35)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with policy 'POL12345'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Policy_Number'] == 'POL12345')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with scheme 'Arogya Sanjeevani' and loss type 'Critical Illness'.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status pending and approved amount > 0.",
+        "pandas_code": "  df[(df['Status'] == 'Pending') & (df['Approved_Amount'] > 0)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with deduction amount > 4000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Deduction_Amount'] > 4000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and paid amount between 35000-75000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Paid_Amount'].between(35000,75000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Hospitalization' and nationality 'Sample'.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with product 'Narayana Group Health'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Product'] == 'Narayana Group Health')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with scheme 'Narayana Plus' and paid amount > 45000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Paid_Amount'] > 45000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status approved and claim amount < 40000.",
+        "pandas_code": "  df[(df['Status'] == 'Approved') & (df['Claim_Amount'] < 40000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with loss type 'Critical Illness'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and product 'Arogya Sanjeevani'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Product'] == 'Arogya Sanjeevani')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with approved amount > 65000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Approved_Amount'] > 65000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and deduction amount between 2000-5000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Deduction_Amount'].between(2000,5000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with scheme 'Arogya Sanjeevani' and policy 'POL56789'.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Policy_Number'] == 'POL56789')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with nationality 'Sample'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Narayana Group Health' and loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with scheme 'Narayana Plus'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Scheme'] == 'Narayana Plus')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and deduction amount < 2500.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Deduction_Amount'] < 2500)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Critical Illness' and claim amount between 70000-120000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Claim_Amount'].between(70000,120000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with paid amount between 20000-50000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Paid_Amount'].between(20000,50000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with scheme 'Arogya Sanjeevani' and nationality 'Sample'.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status paid and loss type 'Accident'.",
+        "pandas_code": "  df[(df['Status'] == 'Paid') & (df['Loss_Type'] == 'Accident')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and approved amount > 55000.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Approved_Amount'] > 55000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Hospitalization' and paid amount > 50000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Paid_Amount'] > 50000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL12345' and status approved.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with loss type 'Critical Illness'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with scheme 'Arogya Sanjeevani' and claim amount > 75000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Claim_Amount'] > 75000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status pending and deduction amount > 6000.",
+        "pandas_code": "  df[(df['Status'] == 'Pending') & (df['Deduction_Amount'] > 6000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with product 'Narayana Group Health'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Product'] == 'Narayana Group Health')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and nationality 'Sample'.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with approved amount between 60000-100000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Approved_Amount'].between(60000,100000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with scheme 'Narayana Plus' and status rejected.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Status'] == 'Rejected')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with paid amount > 60000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Paid_Amount'] > 60000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Critical Illness' and deduction amount < 4000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Deduction_Amount'] < 4000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and policy 'POL12345'.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Policy_Number'] == 'POL12345')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status approved and paid amount between 40000-80000.",
+        "pandas_code": "  df[(df['Status'] == 'Approved') & (df['Paid_Amount'].between(40000,80000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with paid amount > 55000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Paid_Amount'] > 55000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Hospitalization' and approved amount > 65000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Approved_Amount'] > 65000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and claim amount between 50000-110000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Claim_Amount'].between(50000,110000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status rejected and loss type 'Critical Illness'.",
+        "pandas_code": "  df[(df['Status'] == 'Rejected') & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with product 'Arogya Sanjeevani'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Product'] == 'Arogya Sanjeevani')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and approved amount between 45000-85000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Approved_Amount'].between(45000,85000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Accident' and paid amount > 40000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Paid_Amount'] > 40000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with scheme 'Arogya Sanjeevani'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Scheme'] == 'Arogya Sanjeevani')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with product 'Narayana Group Health' and status pending.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL12345' and paid amount between 25000-65000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Paid_Amount'].between(25000,65000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and deduction amount > 5000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Deduction_Amount'] > 5000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with approved amount > 70000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Approved_Amount'] > 70000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Critical Illness' and nationality 'Sample'.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status paid and approved amount between 60000-100000.",
+        "pandas_code": "  df[(df['Status'] == 'Paid') & (df['Approved_Amount'].between(60000,100000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with deduction amount between 3000-7000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Deduction_Amount'].between(3000,7000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL12345' and loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Arogya Sanjeevani' and status approved.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with approved amount > 75000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Approved_Amount'] > 75000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Critical Illness' and claim amount > 100000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Claim_Amount'] > 100000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and nationality 'Sample'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with scheme 'Narayana Plus' and paid amount > 50000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Paid_Amount'] > 50000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status pending and claim amount between 60000-120000.",
+        "pandas_code": "  df[(df['Status'] == 'Pending') & (df['Claim_Amount'].between(60000,120000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with policy 'POL12345'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Policy_Number'] == 'POL12345')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with product 'Arogya Sanjeevani' and paid amount between 40000-90000.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Paid_Amount'].between(40000,90000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Hospitalization' and status rejected.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Status'] == 'Rejected')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and deduction amount < 3500.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Deduction_Amount'] < 3500)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Narayana Group Health' and approved amount between 50000-95000.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Approved_Amount'].between(50000,95000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and loss type 'Accident'.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Loss_Type'] == 'Accident')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with status approved and paid amount > 55000.",
+        "pandas_code": "  df[(df['Status'] == 'Approved') & (df['Paid_Amount'] > 55000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with claim amount between 70000-130000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Claim_Amount'].between(70000,130000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL12345' and product 'Narayana Group Health'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Product'] == 'Narayana Group Health')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for female patients with paid amount > 60000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Paid_Amount'] > 60000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Hospitalization' and deduction amount between 5000-10000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Deduction_Amount'].between(5000,10000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Arogya Sanjeevani' and policy 'POL56789'.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Policy_Number'] == 'POL56789')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with approved amount > 80000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Approved_Amount'] > 80000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and status rejected.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Status'] == 'Rejected')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and paid amount between 30000-70000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Paid_Amount'].between(30000,70000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with scheme 'Narayana Plus' and nationality 'Sample'.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims for male patients with deduction amount > 5000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Deduction_Amount'] > 5000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with loss type 'Accident' and approved amount between 30000-75000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Approved_Amount'].between(30000,75000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims with policy 'POL56789' and status paid.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Status'] == 'Paid')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Arogya Sanjeevani' and loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with paid amount between 25000-60000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Paid_Amount'].between(25000,60000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and deduction amount < 2000.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Deduction_Amount'] < 2000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with policy 'POL12345'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Policy_Number'] == 'POL12345')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and claim amount > 85000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Claim_Amount'] > 85000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with approved amount between 70000-120000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Approved_Amount'].between(70000,120000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Narayana Group Health' and paid amount > 40000.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Paid_Amount'] > 40000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Arogya Sanjeevani' and status pending.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with deduction amount between 5000-9000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Deduction_Amount'].between(5000,9000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Critical Illness' and paid amount between 45000-85000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Paid_Amount'].between(45000,85000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and approved amount > 60000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Approved_Amount'] > 60000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Narayana Group Health' and loss type 'Accident'.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Loss_Type'] == 'Accident')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with paid amount > 65000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Paid_Amount'] > 65000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Arogya Sanjeevani' and claim amount between 60000-130000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Claim_Amount'].between(60000,130000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with status approved.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and deduction amount > 7000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Deduction_Amount'] > 7000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and policy 'POL56789'.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Policy_Number'] == 'POL56789')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with paid amount between 30000-70000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Paid_Amount'].between(30000,70000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Critical Illness' and scheme 'Narayana Plus'.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Scheme'] == 'Narayana Plus')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with approved amount > 80000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Approved_Amount'] > 80000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and nationality 'Sample'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and paid amount > 45000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Paid_Amount'] > 45000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and status rejected.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Status'] == 'Rejected')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and approved amount between 65000-110000.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Approved_Amount'].between(65000,110000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with paid amount between 35000-75000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Paid_Amount'].between(35000,75000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and policy 'POL12345'.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Policy_Number'] == 'POL12345')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with deduction amount < 1500.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Deduction_Amount'] < 1500)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Critical Illness' and approved amount > 75000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Approved_Amount'] > 75000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with product 'Narayana Group Health'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Product'] == 'Narayana Group Health')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and loss type 'Accident'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Loss_Type'] == 'Accident')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with status paid and loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Status'] == 'Paid') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with approved amount between 80000-130000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Approved_Amount'].between(80000,130000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and nationality 'Sample'.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Nationality'] == 'Sample')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Critical Illness' and paid amount > 70000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Paid_Amount'] > 70000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and product 'Narayana Group Health'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Product'] == 'Narayana Group Health')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with approved amount > 90000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Approved_Amount'] > 90000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and scheme 'Arogya Sanjeevani'.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Scheme'] == 'Arogya Sanjeevani')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with paid amount between 40000-80000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Paid_Amount'].between(40000,80000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and status pending.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and paid amount > 50000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Paid_Amount'] > 50000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with deduction amount > 6000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Deduction_Amount'] > 6000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and loss type 'Critical Illness'.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and approved amount > 65000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Approved_Amount'] > 65000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Narayana Group Health' and scheme 'Arogya Sanjeevani'.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Scheme'] == 'Arogya Sanjeevani')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with paid amount > 70000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Paid_Amount'] > 70000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Hospitalization' and status approved.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with approved amount > 85000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Approved_Amount'] > 85000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and paid amount between 45000-85000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Paid_Amount'].between(45000,85000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and paid amount between 35000-75000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Paid_Amount'].between(35000,75000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and status rejected.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Status'] == 'Rejected')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with approved amount between 75000-125000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Approved_Amount'].between(75000,125000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Narayana Group Health' and loss type 'Critical Illness'.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with paid amount > 75000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Paid_Amount'] > 75000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and policy 'POL56789'.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Policy_Number'] == 'POL56789')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and paid amount between 50000-100000.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Paid_Amount'].between(50000,100000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with approved amount > 90000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Approved_Amount'] > 90000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and loss type 'Critical Illness'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Loss_Type'] == 'Critical Illness')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and status paid.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Status'] == 'Paid')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and approved amount between 70000-120000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Approved_Amount'].between(70000,120000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with paid amount > 80000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Paid_Amount'] > 80000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Hospitalization' and scheme 'Narayana Plus'.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Scheme'] == 'Narayana Plus')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with deduction amount between 6000-11000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Deduction_Amount'].between(6000,11000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Arogya Sanjeevani' and paid amount > 60000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Paid_Amount'] > 60000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and approved amount > 70000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Approved_Amount'] > 70000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and paid amount between 40000-90000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Paid_Amount'].between(40000,90000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and approved amount > 80000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Approved_Amount'] > 80000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with paid amount > 85000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Paid_Amount'] > 85000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with approved amount > 95000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Approved_Amount'] > 95000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and product 'Narayana Group Health'.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Product'] == 'Narayana Group Health')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and loss type 'Hospitalization'.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Loss_Type'] == 'Hospitalization')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with paid amount between 45000-95000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Paid_Amount'].between(45000,95000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Arogya Sanjeevani' and status approved.",
+        "pandas_code": "  df[(df['Product'] == 'Arogya Sanjeevani') & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and paid amount > 70000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Paid_Amount'] > 70000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Hospitalization' and approved amount between 70000-120000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Approved_Amount'].between(70000,120000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and deduction amount > 8000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Deduction_Amount'] > 8000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and status pending.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Status'] == 'Pending')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with approved amount > 95000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Approved_Amount'] > 95000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with paid amount > 90000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Paid_Amount'] > 90000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Critical Illness' and scheme 'Arogya Sanjeevani'.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Critical Illness') & (df['Scheme'] == 'Arogya Sanjeevani')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Narayana Group Health' and approved amount between 80000-130000.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Approved_Amount'].between(80000,130000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Hospitalization' and paid amount > 80000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Paid_Amount'] > 80000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with deduction amount between 7000-12000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Deduction_Amount'].between(7000,12000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Arogya Sanjeevani' and approved amount > 90000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Approved_Amount'] > 90000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and paid amount between 50000-100000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Paid_Amount'].between(50000,100000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Narayana Group Health' and scheme 'Narayana Plus'.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Scheme'] == 'Narayana Plus')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with approved amount > 100000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Approved_Amount'] > 100000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and status approved.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Status'] == 'Approved')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with paid amount > 95000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Paid_Amount'] > 95000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and scheme 'Narayana Plus'.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Scheme'] == 'Narayana Plus')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with product 'Narayana Group Health' and paid amount between 60000-110000.",
+        "pandas_code": "  df[(df['Product'] == 'Narayana Group Health') & (df['Paid_Amount'].between(60000,110000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with loss type 'Accident'.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Loss_Type'] == 'Accident')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL56789' and deduction amount > 9000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL56789') & (df['Deduction_Amount'] > 9000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with approved amount > 100000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Approved_Amount'] > 100000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Hospitalization' and scheme 'Arogya Sanjeevani'.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Hospitalization') & (df['Scheme'] == 'Arogya Sanjeevani')].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with paid amount > 90000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Paid_Amount'] > 90000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Narayana Plus' and paid amount > 70000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Narayana Plus') & (df['Paid_Amount'] > 70000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with loss type 'Accident' and approved amount > 80000.",
+        "pandas_code": "  df[(df['Loss_Type'] == 'Accident') & (df['Approved_Amount'] > 80000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with policy 'POL12345' and paid amount between 60000-120000.",
+        "pandas_code": "  df[(df['Policy_Number'] == 'POL12345') & (df['Paid_Amount'].between(60000,120000))].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "List claims with scheme 'Arogya Sanjeevani' and approved amount > 95000.",
+        "pandas_code": "  df[(df['Scheme'] == 'Arogya Sanjeevani') & (df['Approved_Amount'] > 95000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for female patients with paid amount > 95000.",
+        "pandas_code": "  df[(df['Gender'] == 'Female') & (df['Paid_Amount'] > 95000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    {
+        "english": "Show claims for male patients with approved amount > 110000.",
+        "pandas_code": "  df[(df['Gender'] == 'Male') & (df['Approved_Amount'] > 110000)].groupby(['Provider_Name','ICD']).size().reset_index(name='Claim_Count')",
+        "intent": "comparison",
+        "operation": "other"
+    },
+    
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show average of Claim_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Country",
+    "pandas_code": "df.groupby('Country')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show maximum of Deduction_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Deduction_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show sum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show maximum of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].max().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Claim_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Claim_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Nationality",
+    "pandas_code": "df.groupby('Nationality')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Claim_Amount grouped by Product",
+    "pandas_code": "df.groupby('Product')['Claim_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show sum of Settlement_Ageing grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Settlement_Ageing'].sum().reset_index()"
+  },
+  {
+    "english": "show mean of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Provider_Name",
+    "pandas_code": "df.groupby('Provider_Name')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show minimum of Approved_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Approved_Amount'].min().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Product",
+    "pandas_code": "df.groupby('Product')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show count of Settlement_Ageing grouped by Status",
+    "pandas_code": "df.groupby('Status')['Settlement_Ageing'].count().reset_index()"
+  },
+  {
+    "english": "show sum of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].sum().reset_index()"
+  },
+  {
+    "english": "show average of Settlement_Ageing grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Settlement_Ageing'].mean().reset_index()"
+  },
+  {
+    "english": "show maximum of Claim_Amount grouped by Relation_Nm",
+    "pandas_code": "df.groupby('Relation_Nm')['Claim_Amount'].max().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Loss_Type",
+    "pandas_code": "df.groupby('Loss_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Claim_Amount grouped by Gender",
+    "pandas_code": "df.groupby('Gender')['Claim_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show mean of Deduction_Amount grouped by Claim_Type",
+    "pandas_code": "df.groupby('Claim_Type')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Approved_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Approved_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show average of Deduction_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Deduction_Amount'].mean().reset_index()"
+  },
+  {
+    "english": "show count of Approved_Amount grouped by Status",
+    "pandas_code": "df.groupby('Status')['Approved_Amount'].count().reset_index()"
+  },
+  {
+    "english": "show minimum of Deduction_Amount grouped by Country",
+    "pandas_code": "df.groupby('Country')['Deduction_Amount'].min().reset_index()"
+  },
+  
+  {
+    "english": "Find the claim type with maximum rejections",
+    "code": "df[df['Status']=='Rejected']['Claim_Type'].value_counts().idxmax()"
+  },
+  {
+    "english": "Which claim entry number has the highest pending duration",
+    "code": "df.loc[df['outstanding_age'].idxmax(), 'Claim_Number']"
+  },
+  {
+    "english": "Get average unsettled ageinging for disease Complicated cataract",
+    "code": "df[df['ICD_Disease'] == 'Complicated cataract']['outstanding_age'].mean()"
+  },
+  {
+    "english": "Whichpolicynumberhasthehighestaverageoutstandingaging",
+    "code": "df[df['outstanding_age'].notna()].groupby('Policy_Number')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Total rejected claims grouped by ICD code",
+    "code": "df[df['Status']=='Rejected'].groupby('ICD_Code')['Claim_Number'].count()"
+  },
+  {
+    "english": "Group claims by gender and calculate average claim value",
+    "code": "df.groupby('Gender')['Claim_Amount'].mean()"
+  },
+  {
+    "english": "top 10 service provider by claim amount",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "which gender has the lowest average days pending",
+    "code": "df[df['outstanding_age'].notna()].groupby('Gender')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Show top 10 providers by disease coverage.",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Give 10 providers with highest number of ICD codes.",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Find which provider has the lowest outstanding financial exposure",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['Claim_Amount'].sum().idxmin()"
+  },
+  {
+    "english": "Total paid claims count grouped by provider",
+    "code": "df[df['Status']=='Paid'].groupby('Provider_Name')['Claim_Number'].count()"
+  },
+  {
+    "english": "Find outstanding ageing for a policy number BGDC221000109-00-000",
+    "code": "df.loc[df['Policy_Number'] == 'BGDC221000109-00-000', 'outstanding_age']"
+  },
+  {
+    "english": "Which provider has the highest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "List cases settled in 15 days or less.",
+    "code": "df[df['settlement_ageing'] <= 15]"
+  },
+  {
+    "english": "Whichhospitalhastheleastoutstandingclaimrecords",
+    "code": "df[df['outstanding_age'].notna()]['Provider_Name'].value_counts().idxmin()"
+  },
+  {
+    "english": "Show all ageing details (outstanding and settlement) for a claim number  CBGDC22021912-00",
+    "code": "df.loc[df['Claim_Number'] == 'CBGDC22021912-00 ', ['outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Group approved cases by month",
+    "code": "df[df['Status']=='Approved'].assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "Find total emergency claim records with outstanding aging",
+    "code": "df[(df['Claim_Type']=='Emergency') & (df['outstanding_age'].notna())].shape[0]"
+  },
+  {
+    "english": "Get top 5 clients with highest average days pending",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].mean().nlargest(5)"
+  },
+  {
+    "english": "Average outstanding aging grouped by ICD disease",
+    "code": "df.groupby('ICD_Disease')['outstanding_age'].mean()"
+  },
+  {
+    "english": "Find lowest settlement aging among clients???",
+    "code": "df.groupby('Patient_Name')['settlement_age'].mean().idxmin()"
+  },
+  {
+    "english": "Top 10 clients with highest total claim amount",
+    "code": "df.groupby('Patient_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Top 10 providers with maximum ICD codes?",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Show grouped summary by ICD disease with count and sum of claim amount",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Disease')['Claim_Amount'].agg(['count','sum'])"
+  },
+  {
+    "english": "Group outstanding claims by claim type and calculate highest average outstanding aging",
+    "code": "df[df['outstanding_age'].notna()].groupby('Claim_Type')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Get most frequent panel by claim entry volume",
+    "code": "df['Provider_Name'].value_counts().idxmax()"
+  },
+  {
+    "english": "Find gender distribution for rejected claim entrys???",
+    "code": "df[df['Status']=='Rejected']['Gender'].value_counts()"
+  },
+  {
+    "english": "Show top 10 rejected claim records by amount",
+    "code": "df[df['Status']=='Rejected'].nlargest(10, 'Claim_Amount')"
+  },
+  {
+    "english": "Show top five panels based on claim amount.",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(5)"
+  },
+  {
+    "english": "find closure timeing for claim record number CBGDC22017155-00",
+    "code": "df.loc[df['Claim_Number'] == 'CBGDC22017155-00', 'settlement_age']"
+  },
+  {
+    "english": "Get average outstanding ageing for disease Complicated cataract",
+    "code": "df[df['ICD_Disease'] == 'Complicated cataract']['outstanding_age'].mean()"
+  },
+  {
+    "english": "Which policy number has the lowest average outstanding aging",
+    "code": "df[df['outstanding_age'].notna()].groupby('Policy_Number')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Which client/patient has the most outstanding claim records",
+    "code": "df[df['outstanding_age'].notna()]['Patient_Name'].value_counts().idxmax()"
+  },
+  {
+    "english": "Who are top 12 providers by icd disease?",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].count().nlargest(12)"
+  },
+  {
+    "english": "Find ICD with highest pending claim count",
+    "code": "df[df['Status']=='Pending'].groupby('ICD_Code')['Claim_Number'].count().idxmax()"
+  },
+  {
+    "english": "Claims with days to settleing up to 20 days.",
+    "code": "df[df['settlement_ageing'] <= 20]"
+  },
+  {
+    "english": "Which ICD code has the lowest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Code')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Group claim entrys by provider and aggregate metrics",
+    "code": "df.groupby('Provider_Name').agg({'Claim_Amount':'sum','Approved_Amount':'sum','outstanding_age':'mean'})"
+  },
+  {
+    "english": "Group approved claim entrys by month",
+    "code": "df[df['Status']=='Approved'].assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "Which ICD code has the lowest average pending duration",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Code')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Find highest approved claim entry amount record",
+    "code": "df.loc[df['Approved_Amount'].idxmax()]"
+  },
+  {
+    "english": "Find gender distribution for rejected claims",
+    "code": "df[df['Status']=='Rejected']['Gender'].value_counts()"
+  },
+  {
+    "english": "Top 7 providers by total claims?",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(7)"
+  },
+  {
+    "english": "Group outstanding claims by provider and calculate average unsettled ageing???",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].mean()"
+  },
+  {
+    "english": "Give claim records that got settled within 7 days.",
+    "code": "df[df['settlement_ageing'] <= 7]"
+  },
+  {
+    "english": "How many claim entrys got settled fast (30 days)?",
+    "code": "df[df['settlement_ageing'] <= 30].shape[0]"
+  },
+  {
+    "english": "Group outstanding claim records by service provider and calculate average open days",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].mean()"
+  },
+  {
+    "english": "Find outstanding ageing for a given ICD code G07",
+    "code": "df.loc[df['ICD_Code'] == 'G07', ['Claim_Number', 'outstanding_age']]"
+  },
+  {
+    "english": "What is the total claim amount for each loss type?",
+    "code": "df.groupby('Loss_Type')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Which providers have the highest number of diseases (top 10)?",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Find highest settlement aging among clients",
+    "code": "df.groupby('Patient_Name')['settlement_age'].mean().idxmax()"
+  },
+  {
+    "english": "Find the case type with maximum rejections",
+    "code": "df[df['Status']=='Rejected']['Claim_Type'].value_counts().idxmax()"
+  },
+  {
+    "english": "Show top 10 providers by disease coverage.???",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Count claim entrys where settlement age is greater than 90 days",
+    "code": "df[df['settlement_age'] > 90].shape[0]"
+  },
+  {
+    "english": "Return top ten providers sorted by claim entrys.",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  }
+,
+
+  {
+    "english": "Show all ageing details for policy number BGDP211000196-01-000",
+    "code": "df.loc[df['Policy_Number'] == 'BGDP211000196-01-000', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Compare total outstanding vs settled claim record amounts",
+    "code": "df[df['outstanding_age'].notna()]['Claim_Amount'].sum(), 'settled': df[df['settlement_age'].notna()]['Claim_Amount'].sum()}"
+  },
+  {
+    "english": "Top 5 clients with highest claim record count",
+    "code": "df['Patient_Name'].value_counts().nlargest(5)"
+  },
+  {
+    "english": "Whichclienthasthelowestapprovedamount",
+    "code": "df.groupby('Patient_Name')['Approved_Amount'].sum().idxmin()"
+  },
+  {
+    "english": "Find lowest settlement aging among clients",
+    "code": "df.groupby('Patient_Name')['settlement_age'].mean().idxmin()"
+  },
+  {
+    "english": "Top 3 policies with highest total approved amount",
+    "code": "df.groupby('Policy_Number')['Approved_Amount'].sum().nlargest(3)"
+  },
+  {
+    "english": "Average outstanding aging grouped by ICD disease",
+    "code": "df.groupby('ICD_Disease')['outstanding_age'].mean()"
+  },
+  {
+    "english": "Group clm entrys by policy and get outstanding totals",
+    "code": "df[df['outstanding_age'].notna()].groupby('Policy_Number')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Find provider with fastest settlement time",
+    "code": "df.groupby('Provider_Name')['settlement_age'].mean().idxmin()"
+  },
+  {
+    "english": "Show settlement summary for claims within 30 days ",
+    "code": "df[df['settlement_ageing'] <= 30].describe()"
+  },
+  {
+    "english": "Find provider with fastest settlement time",
+    "code": "df.groupby('Provider_Name')['settlement_age'].mean().idxmin()"
+  },
+  {
+    "english": "Find outstanding ageing for a claim entry number CBGDC22013782-00",
+    "code": "df.loc[df['Claim_Number'] == 'CBGDC22013782-00', 'outstanding_age']"
+  },
+  {
+    "english": "Find highest settlement aging among clients",
+    "code": "df.groupby('Patient_Name')['settlement_age'].mean().idxmax()"
+  },
+  {
+    "english": "Top 10 panels with maximum ICD codes ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Shortest aging open claim record record",
+    "code": "df.loc[df['outstanding_age'].idxmin()]"
+  },
+  {
+    "english": "Which gender has the highest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('Gender')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "what is settlement ageing for a policy number BGDC221000033-00-000",
+    "code": "df.loc[df['Policy_Number'] == 'BGDC221000033-00-000', 'settlement_age']"
+  },
+  {
+    "english": "List top 10 providers by unique ICD diseases ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Total claim count grouped by month of creation",
+    "code": "df.assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "Get average unsettled ageinging for disease Complicated cataract",
+    "code": "df[df['ICD_Disease'] == 'Complicated cataract']['outstanding_age'].mean()"
+  },
+  {
+    "english": "Which client has the highest approved amount",
+    "code": "df.groupby('Patient_Name')['Approved_Amount'].sum().idxmax()"
+  },
+  {
+    "english": "Get highest average claim entry amount grouped by gender",
+    "code": "df.groupby('Gender')['Claim_Amount'].mean().idxmax()"
+  },
+  {
+    "english": "Which providers have the highest number of diseases (top 10) ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Give all ageing data based on gender Female",
+    "code": "df.loc[df['Gender'] == 'F', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Give 10 providers with highest number of ICD codes ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Find outstanding ageing for a policy number BGDC221000109-00-000",
+    "code": "df.loc[df['Policy_Number'] == 'BGDC221000109-00-000', 'outstanding_age']"
+  },
+  {
+    "english": "Which ICD disease category has the most outstanding claim entry count   ",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Disease'].value_counts().idxmax()"
+  },
+  {
+    "english": "Show all claims that were settled within 10 days ",
+    "code": "df[df['settlement_ageing'] <= 10]"
+  },
+  {
+    "english": "Find average open daysing for a provider TAN TOCK SENG HOSPITAL (OUTPATIENT) ",
+    "code": "df[df['Provider_Name'] == 'TAN TOCK SENG HOSPITAL (OUTPATIENT)']['outstanding_age'].mean()"
+  },
+  {
+    "english": "which service providers have highest case amounts (top 10) ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Get outstanding ageing for a claim entry type cashless",
+    "code": "df.loc[df['Claim_Type'] == 'cashless', ['Claim_Number', 'outstanding_age']]"
+  },
+  {
+    "english": "Find the claim record type with maximum rejections   ",
+    "code": "df[df['Status']=='Rejected']['Claim_Type'].value_counts().idxmax()"
+  },
+  {
+    "english": "Get highest outstanding claim record grouped by ICD",
+    "code": "df.groupby('ICD_Code')['outstanding_age'].max()"
+  },
+  {
+    "english": "Longest open case record (oldest outstanding)",
+    "code": "df.loc[df['outstanding_age'].idxmax()]"
+  },
+  {
+    "english": "top 5 icd codes with highest financial loss",
+    "code": "df.groupby('ICD_Code')['Claim_Amount'].sum().nlargest(5)"
+  },
+  {
+    "english": "Groupoutstandingclaimentrysbyclaimentrytypeandcalculatehighestaverageoutstandingaging",
+    "code": "df[df['outstanding_age'].notna()].groupby('Claim_Type')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Which providers have highest claim amounts (top 10) ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Which gender has the least number of outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Gender'].value_counts().idxmin()"
+  },
+  {
+    "english": "Show top ten providers according to ICD coverage ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Find which service provider has the lowest outstanding financial exposure",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['Claim_Amount'].sum().idxmin()"
+  },
+  {
+    "english": "List claim records settled in 15 days or less ",
+    "code": "df[df['settlement_ageing'] <= 15]"
+  },
+  {
+    "english": "Whichgenderhasthelowestaveragependingduration",
+    "code": "df[df['outstanding_age'].notna()].groupby('Gender')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Search all ageing data based on gender Male",
+    "code": "df.loc[df['Gender'] == 'M', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "List top 10 providers by unique ICD diseases ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Which ICD code has the highest average days pending",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Code')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Total claim record count grouped by month of creation",
+    "code": "df.assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "Top 10 clinics according to claim amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Compare total outstanding vs settled claim amounts",
+    "code": "{'outstanding': df[df['outstanding_age'].notna()]['Claim_Amount'].sum(), 'settled': df[df['settlement_age'].notna()]['Claim_Amount'].sum()}"
+  },
+  {
+    "english": "top 10 paid claims by amount",
+    "code": "df[df['Status']=='Paid'].nlargest(10, 'Claim_Amount')"
+  },
+  {
+    "english": "What is the most common loss type ",
+    "code": "df['Loss_Type'].value_counts().idxmax()"
+  },
+  {
+    "english": "FindaverageopendaysingforapanelTANTOCKSENGHOSPITAL(OUTPATIENT)",
+    "code": "df[df['Provider_Name'] == 'TAN TOCK SENG HOSPITAL (OUTPATIENT)']['outstanding_age'].mean()"
+  },
+  {
+    "english": "Give 10 panels with highest number of ICD codes ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Which provider has the highest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Count how many claim entrys were settled in 5 days ",
+    "code": "df[df['settlement_ageing'] <= 5].shape[0]"
+  },
+  {
+    "english": "Group outstanding claim entrys by month and count number of cases",
+    "code": "df[df['outstanding_age'].notna()].assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "Find clients with more than 3 outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Patient_Name'].value_counts()[lambda x: x>3]"
+  },
+  {
+    "english": "Which policy number has the least outstanding claim records   ",
+    "code": "df[df['outstanding_age'].notna()]['Policy_Number'].value_counts().idxmin()"
+  },
+  {
+    "english": "Which ICD code has the most outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Code'].value_counts().idxmax()"
+  },
+  {
+    "english": "Show ageing details filtered by claim status Paid",
+    "code": "df.loc[df['Status'] == 'Paid', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Show top ten panels according to ICD coverage ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Which gender has the least number of outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Gender'].value_counts().idxmin()"
+  },
+  {
+    "english": "Which clinic has the highest average pending duration",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Total number of claims settled within 18 days ",
+    "code": "df[df['settlement_ageing'] <= 18].shape[0]"
+  },
+  {
+    "english": "Compareclaimamountvsapprovedamountgroupedbyserviceprovider",
+    "code": "df.groupby('Provider_Name')[['Claim_Amount','Approved_Amount']].sum()"
+  },
+  {
+    "english": "Get highest outstanding claim grouped by ICD",
+    "code": "df.groupby('ICD_Code')['outstanding_age'].max()"
+  },
+  {
+    "english": "Show ageing for claim records created on a 03/01/2023",
+    "code": "df.loc[df['Claim_Created_Date'] == '03/01/2023', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Which provider has the least outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Provider_Name'].value_counts().idxmin()"
+  },
+  {
+    "english": "Findlowestsettlementagingamongclients",
+    "code": "df.groupby('Patient_Name')['settlement_age'].mean().idxmin()"
+  },
+  {
+    "english": "Which ICD code has the highest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Code')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Show grouped summary of outstanding aging by panel with count, sum and average",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].agg(['count','sum','mean'])"
+  },
+  {
+    "english": "group cases by gender and calculate average case value",
+    "code": "df.groupby('Gender')['Claim_Amount'].mean()"
+  },
+  {
+    "english": "Who are top 3 clinics by case amount    ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(3)"
+  },
+  {
+    "english": "Give the 10 highest case hospitals ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Show all ageing details (outstanding and settlement) for a claim entry number  CBGDC22021912-00",
+    "code": "df.loc[df['Claim_Number'] == 'CBGDC22021912-00 ', ['outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Who are top 3 service providers by icd disease ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(3)"
+  },
+  {
+    "english": "How many claims are settled within 10 days ",
+    "code": "df[df['settlement_ageing'] <= 10]"
+  },
+  {
+    "english": "top 5 clients with highest claim count",
+    "code": "df['Patient_Name'].value_counts().nlargest(5)"
+  },
+  {
+    "english": "FindunsettledageingingforagivenICDcodeG07",
+    "code": "df.loc[df['ICD_Code'] == 'G07', ['Claim_Number', 'outstanding_age']]"
+  },
+  {
+    "english": "Top 10 clients with highest total claim record amount",
+    "code": "df.groupby('Patient_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Grouppendingclaimentrysbyclinic",
+    "code": "df[df['Status']=='Pending'].groupby('Provider_Name')['Claim_Number'].count()"
+  },
+  {
+    "english": "Show leading 10 providers by claim sum ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Which gender has the highest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('Gender')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Which client has the highest approved amount",
+    "code": "df.groupby('Patient_Name')['Approved_Amount'].sum().idxmax()"
+  },
+  {
+    "english": "Which client has the lowest approved amount",
+    "code": "df.groupby('Patient_Name')['Approved_Amount'].sum().idxmin()"
+  },
+  {
+    "english": "Give claims that got settled within 7 days ",
+    "code": "df[df['settlement_ageing'] <= 7]"
+  },
+  {
+    "english": "Find which gender has highest settlement delay   ",
+    "code": "df.groupby('Gender')['settlement_age'].mean().idxmax()"
+  },
+  {
+    "english": "Who are top 7 hospitals by claim amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(7)"
+  },
+  {
+    "english": "Show ageing details for  patient name Adamn46",
+    "code": "df.loc[df['Patient_Name'] == 'Adam46', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Get top 5 clients with highest average open days   ",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].mean().nlargest(5)"
+  },
+  {
+    "english": "Get bottom 5 clients with lowest average days pending",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].mean().nsmallest(5)"
+  },
+  {
+    "english": "Find ICD with highest pending claim count",
+    "code": "df[df['Status']=='Pending'].groupby('ICD_Code')['Claim_Number'].count().idxmax()"
+  },
+  {
+    "english": "Count how many claims were settled in 5 days ",
+    "code": "df[df['settlement_ageing'] <= 5].shape[0]"
+  },
+  {
+    "english": "Who are top 3 service providers by claim entry amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(3)"
+  },
+  {
+    "english": "Search all ageing data based on gender Male",
+    "code": "df.loc[df['Gender'] == 'M', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Show settlement summary for claims within 30 days ",
+    "code": "df[df['settlement_ageing'] <= 30].describe()"
+  },
+  {
+    "english": "Return top ten hospitals with most disease types ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Compare claim amount vs approved amount grouped by provider",
+    "code": "df.groupby('Provider_Name')[['Claim_Amount','Approved_Amount']].sum()"
+  },
+  {
+    "english": "Group cases by policy and tell me outstanding totals",
+    "code": "df[df['outstanding_age'].notna()].groupby('Policy_Number')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Count claims where days to settle is greater than 90 days",
+    "code": "df[df['settlement_age'] > 90].shape[0]"
+  },
+  {
+    "english": "Which ICD code has the least outstanding claim records",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Code'].value_counts().idxmin()"
+  },
+  {
+    "english": "Which gender has the highest average outstanding age   ",
+    "code": "df[df['outstanding_age'].notna()].groupby('Gender')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "what is processed durationing for a policy number BGDC221000033-00-000",
+    "code": "df.loc[df['Policy_Number'] == 'BGDC221000033-00-000', 'settlement_age']"
+  },
+  {
+    "english": "Find how many claims have outstanding age more than 120 days",
+    "code": "df[df['outstanding_age'] > 120].shape[0]"
+  },
+  {
+    "english": "who are top 12 service providers by icd disease ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].count().nlargest(12)"
+  },
+  {
+    "english": "Which client/patient has the least outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Patient_Name'].value_counts().idxmin()"
+  },
+  {
+    "english": "Find total emergency claim entrys with outstanding aging",
+    "code": "df[(df['Claim_Type']=='Emergency') & (df['outstanding_age'].notna())].shape[0]"
+  },
+  {
+    "english": "Show top ten providers according to ICD coverage ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Which client/patient has the most outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Patient_Name'].value_counts().idxmax()"
+  },
+  {
+    "english": "Show top ten providers according to ICD coverage ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Return top ten providers with most disease types ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Who are top 5 providers by claim amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(5)"
+  },
+  {
+    "english": "Getopendaysingforaclaimrecordtypecashless",
+    "code": "df.loc[df['Claim_Type'] == 'cashless', ['Claim_Number', 'outstanding_age']]"
+  },
+  {
+    "english": "Show grouped summary of outstanding aging by provider with count, sum and average",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].agg(['count','sum','mean'])"
+  },
+  {
+    "english": "Top 5 ICD diseases by paid clm record count",
+    "code": "df[df['Status']=='Paid'].groupby('ICD_Disease')['Claim_Number'].count().nlargest(5)"
+  },
+  {
+    "english": "Find settlement ageing for case number CBGDC22017155-00",
+    "code": "df.loc[df['Claim_Number'] == 'CBGDC22017155-00', 'settlement_age']"
+  },
+  {
+    "english": "How many cases got settled fast (\u226430 days) ",
+    "code": "df[df['settlement_ageing'] <= 30].shape[0]"
+  },
+  {
+    "english": "Find total days pendinging for a hospital  HEALTHCARE FAMILY CLINIC & SURGERY",
+    "code": "df[df['Provider_Name'] == 'HEALTHCARE FAMILY CLINIC & SURGERY']['outstanding_age'].sum()"
+  },
+  {
+    "english": "top 10 hospital by claim amount",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Which ICD disease category has the most outstanding claim count",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Disease'].value_counts().idxmax()"
+  },
+  {
+    "english": "What is the total approved amount for each loss type ",
+    "code": "df.groupby('Loss_Type')['Approved_Amount'].sum()"
+  },
+  {
+    "english": "Find open daysing for a case number CBGDC22013782-00",
+    "code": "df.loc[df['Claim_Number'] == 'CBGDC22013782-00', 'outstanding_age']"
+  },
+  {
+    "english": "Group outstanding cases by year of case created date and show average outstanding days",
+    "code": "df[df['outstanding_age'].notna()].assign(year=df['Claim_Created_Date'].dt.year).groupby('year')['outstanding_age'].mean()"
+  },
+  {
+    "english": "what is days to settleing for a given icd code j26",
+    "code": "df.loc[df['ICD_Code'] == 'J26', ['Claim_Number', 'settlement_age']]"
+  },
+  {
+    "english": "Which claim number has the lowest outstanding age",
+    "code": "df.loc[df['outstanding_age'].idxmin(), 'Claim_Number']"
+  },
+  {
+    "english": "Find ICD category with fastest settlement duration",
+    "code": "df.groupby('ICD_Disease')['settlement_age'].mean().idxmin()"
+  },
+  {
+    "english": "Find which panel has the lowest outstanding financial exposure",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['Claim_Amount'].sum().idxmin()"
+  },
+  {
+    "english": "Return top ten providers sorted by cases ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Which ICD disease category has the least outstanding claim count",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Disease'].value_counts().idxmin()"
+  },
+  {
+    "english": "Which ICD disease category has the most outstanding case count",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Disease'].value_counts().idxmax()"
+  },
+  {
+    "english": "Comparetotaloutstandingvssettledcaseamounts",
+    "code": "{'outstanding': df[df['outstanding_age'].notna()]['Claim_Amount'].sum(), 'settled': df[df['settlement_age'].notna()]['Claim_Amount'].sum()}"
+  },
+  {
+    "english": "what is closure timeing for a policy number BGDC221000033-00-000",
+    "code": "df.loc[df['Policy_Number'] == 'BGDC221000033-00-000', 'settlement_age']"
+  },
+  {
+    "english": "Get average outstanding ageing for disease Complicated cataract",
+    "code": "df[df['ICD_Disease'] == 'Complicated cataract']['outstanding_age'].mean()"
+  },
+  {
+    "english": "who are top 5 providers by claim entry amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(5)"
+  },
+  {
+    "english": "Who are the leading 10 providers by claim total ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Who are top 7 panels by claim entry amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(7)"
+  },
+  {
+    "english": "Compare claim amount vs approved amount grouped by panel",
+    "code": "df.groupby('Provider_Name')[['Claim_Amount','Approved_Amount']].sum()"
+  },
+  {
+    "english": "Show top five providers based on claim record amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(5)"
+  },
+  {
+    "english": "Total rejected claims grouped by ICD code",
+    "code": "df[df['Status']=='Rejected'].groupby('ICD_Code')['Claim_Number'].count()"
+  },
+  {
+    "english": "Group claims by provider and aggregate metrics",
+    "code": "df.groupby('Provider_Name').agg({'Claim_Amount':'sum','Approved_Amount':'sum','outstanding_age':'mean'})"
+  },
+  {
+    "english": "Top 10 paid claims by amount",
+    "code": "df[df['Status']=='Paid'].nlargest(10, 'Claim_Amount')"
+  },
+  {
+    "english": "Top 10 providers with most ICD diseases ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Show all ageing details (outstanding and settlement) for a claim number  CBGDC22021912-00",
+    "code": "df.loc[df['Claim_Number'] == 'CBGDC22021912-00 ', ['outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Show open daysing for all claim entrys for a given provider CASA DENTAL ADMIRALTY    ",
+    "code": "df.loc[df['Provider_Name'] == 'CASA DENTAL ADMIRALTY', ['Claim_Number', 'outstanding_age']]"
+  },
+  {
+    "english": "Show top five providers based on claim amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(5)"
+  },
+  {
+    "english": "Show ageing details for  patient name Adamn46",
+    "code": "df.loc[df['Patient_Name'] == 'Adam46', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Top 10 providers according to claim amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "get top 5 patient names with highest rejection history",
+    "code": "df[df['Status']=='Rejected']['Patient_Name'].value_counts().nlargest(5)"
+  },
+  {
+    "english": "Which clinics have the highest number of diseases (top 10) ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Group claims by gender and calculate average claim value",
+    "code": "df.groupby('Gender')['Claim_Amount'].mean()"
+  },
+  {
+    "english": "Which client/patient has the lowest total outstanding age combined",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].sum().idxmin()"
+  },
+  {
+    "english": "Get bottom 5 clients with lowest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].mean().nsmallest(5)"
+  },
+  {
+    "english": "count cases where closure time is greater than 90 days",
+    "code": "df[df['settlement_age'] > 90].shape[0]"
+  },
+  {
+    "english": "Find pending durationing for a policy number BGDC221000109-00-000",
+    "code": "df.loc[df['Policy_Number'] == 'BGDC221000109-00-000', 'outstanding_age']"
+  },
+  {
+    "english": "top 5 policies with highest settlement delay",
+    "code": "df.groupby('Policy_Number')['settlement_age'].mean().nlargest(5)"
+  },
+  {
+    "english": "Give claims that got settled within 7 days ",
+    "code": "df[df['settlement_ageing'] <= 7]"
+  },
+  {
+    "english": "Find highest approved claim record amount record",
+    "code": "df.loc[df['Approved_Amount'].idxmax()]"
+  },
+  {
+    "english": "Get pending durationing for a claim record type cashless",
+    "code": "df.loc[df['Claim_Type'] == 'cashless', ['Claim_Number', 'outstanding_age']]"
+  },
+  {
+    "english": "Get most frequent provider by case volume",
+    "code": "df['Provider_Name'].value_counts().idxmax()"
+  },
+  {
+    "english": "Who are top 3 providers by claim amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(3)"
+  },
+  {
+    "english": "Group outstanding claims by month and count number of cases",
+    "code": "df[df['outstanding_age'].notna()].assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "Which ICD disease category has the least outstanding claim record count",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Disease'].value_counts().idxmin()"
+  },
+  {
+    "english": "Top 10 providers with most ICD diseases ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Show settlement summary for claim entrys within 30 days ",
+    "code": "df[df['settlement_ageing'] <= 30].describe()"
+  },
+  {
+    "english": "Group approved claims by month",
+    "code": "df[df['Status']=='Approved'].assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "Who are top 7 providers by claim amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(7)"
+  },
+  {
+    "english": "Which policy number has the lowest average outstanding aging",
+    "code": "df[df['outstanding_age'].notna()].groupby('Policy_Number')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Group claim entrys by gender and calculate average claim entry value",
+    "code": "df.groupby('Gender')['Claim_Amount'].mean()"
+  },
+  {
+    "english": "Give total claim amount for claims settled within 30 days ",
+    "code": "df[df['settlement_ageing'] <= 30]['claim_amount'].sum()"
+  },
+  {
+    "english": "Show top 10 providers by disease coverage ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Show all claims that were settled within 10 days ",
+    "code": "df[df['settlement_ageing'] <= 10]"
+  },
+  {
+    "english": "Find average aging for each claim type",
+    "code": "df.groupby('Claim_Type')['outstanding_age'].mean()"
+  },
+  {
+    "english": "Show all ageing details for policy number BGDP211000196-01-000",
+    "code": "df.loc[df['Policy_Number'] == 'BGDP211000196-01-000', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Return top ten providers sorted by claims ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Which gender has the highest number of outstanding claim entrys   ",
+    "code": "df[df['outstanding_age'].notna()]['Gender'].value_counts().idxmax()"
+  },
+  {
+    "english": "Show ageing for claims created on a 03/01/2023",
+    "code": "df.loc[df['Claim_Created_Date'] == '03/01/2023', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Which policy number has the highest average outstanding aging",
+    "code": "df[df['outstanding_age'].notna()].groupby('Policy_Number')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Top 5 ICD codes with highest financial loss",
+    "code": "df.groupby('ICD_Code')['Claim_Amount'].sum().nlargest(5)"
+  },
+  {
+    "english": "Count how many claims were settled in 5 days ",
+    "code": "df[df['settlement_ageing'] <= 5].shape[0]"
+  },
+  {
+    "english": "who leads the service providers in ICD disease count (top 10) ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "find clients with more than 3 outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Patient_Name'].value_counts()[lambda x: x>3]"
+  },
+  {
+    "english": "Group outstanding claim entrys by claim entry type and sum claim entry amount",
+    "code": "df[df['outstanding_age'].notna()].groupby('Claim_Type')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Which policy number has the highest average outstanding aging",
+    "code": "df[df['outstanding_age'].notna()].groupby('Policy_Number')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Top 7 service providers by total claim entrys    ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(7)"
+  },
+  {
+    "english": "Which gender has the highest number of outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Gender'].value_counts().idxmax()"
+  },
+  {
+    "english": "Find ICD with highest pending claim entry count",
+    "code": "df[df['Status']=='Pending'].groupby('ICD_Code')['Claim_Number'].count().idxmax()"
+  },
+  {
+    "english": "Get total outstanding age for a disease Common Cold / flu",
+    "code": "df[df['ICD_Disease'] == 'Common Cold / flu']['outstanding_age'].sum()"
+  },
+  {
+    "english": "Top 10 providers with most ICD diseases    ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Group pending cases by hospital",
+    "code": "df[df['Status']=='Pending'].groupby('Provider_Name')['Claim_Number'].count()"
+  },
+  {
+    "english": "Which clm number has the lowest pending duration",
+    "code": "df.loc[df['outstanding_age'].idxmin(), 'Claim_Number']"
+  },
+  {
+    "english": "Find the claim type with maximum rejections",
+    "code": "df[df['Status']=='Rejected']['Claim_Type'].value_counts().idxmax()"
+  },
+  {
+    "english": "Which service provider has the highest total outstanding claim records",
+    "code": "df[df['outstanding_age'].notna()]['Provider_Name'].value_counts().idxmax()"
+  },
+  {
+    "english": "Show resolved ageing for all claims for a given provider Kang An TCM Pte Ltd",
+    "code": "df.loc[df['Provider_Name'] == 'Kang An TCM Pte Ltd', ['Claim_Number', 'settlement_age']]"
+  },
+  {
+    "english": "Which gender has the highest average days pending",
+    "code": "df[df['outstanding_age'].notna()].groupby('Gender')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Who are top 3 service providers by case amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(3)"
+  },
+  {
+    "english": "Which ICD code has the least outstanding claim entrys",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Code'].value_counts().idxmin()"
+  },
+  {
+    "english": "Total paid claims count grouped by provider",
+    "code": "df[df['Status']=='Paid'].groupby('Provider_Name')['Claim_Number'].count()"
+  },
+  {
+    "english": "Show top 10 rejected claims by amount",
+    "code": "df[df['Status']=='Rejected'].nlargest(10, 'Claim_Amount')"
+  },
+  {
+    "english": "Which client/patient has the most outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Patient_Name'].value_counts().idxmax()"
+  },
+  {
+    "english": "Give total claim amount for claims settled within 30 days ",
+    "code": "df[df['settlement_ageing'] <= 30]['claim_amount'].sum()"
+  },
+  {
+    "english": "Show ageing details filtered by case status Paid",
+    "code": "df.loc[df['Status'] == 'Paid', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Get bottom 5 clients with lowest average unsettled ageing   ",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].mean().nsmallest(5)"
+  },
+  {
+    "english": "What is the total approved amount for each loss type ",
+    "code": "df.groupby('Loss_Type')['Approved_Amount'].sum()"
+  },
+  {
+    "english": "Show grouped summary by ICD disease with count and sum of claim entry amount",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Disease')['Claim_Amount'].agg(['count','sum'])"
+  },
+  {
+    "english": "Get top 5 patient names with highest rejection history",
+    "code": "df[df['Status']=='Rejected']['Patient_Name'].value_counts().nlargest(5)"
+  },
+  {
+    "english": "Which client/patient has the lowest total open days combined",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].sum().idxmin()"
+  },
+  {
+    "english": "Top 5 ICD diseases by paid claim count",
+    "code": "df[df['Status']=='Paid'].groupby('ICD_Disease')['Claim_Number'].count().nlargest(5)"
+  },
+  {
+    "english": "Group by ICD and find average approved amount",
+    "code": "df.groupby('ICD_Code')['Approved_Amount'].mean()"
+  },
+  {
+    "english": "Search all ageing data based on gender Male",
+    "code": "df.loc[df['Gender'] == 'M', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Find total unsettled ageinging for a service provider  HEALTHCARE FAMILY CLINIC & SURGERY",
+    "code": "df[df['Provider_Name'] == 'HEALTHCARE FAMILY CLINIC & SURGERY']['outstanding_age'].sum()"
+  },
+  {
+    "english": "Find total emergency claims with outstanding aging",
+    "code": "df[(df['Claim_Type']=='Emergency') & (df['outstanding_age'].notna())].shape[0]"
+  },
+  {
+    "english": "Group outstanding claim records by gender and calculate total claim record amount",
+    "code": "df[df['outstanding_age'].notna()].groupby('Gender')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Top 10 paid cases by amount",
+    "code": "df[df['Status']=='Paid'].nlargest(10, 'Claim_Amount')"
+  },
+  {
+    "english": "Top 10 providers with maximum ICD codes ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Group outstanding claim records by gender and calculate total claim record amount",
+    "code": "df[df['outstanding_age'].notna()].groupby('Gender')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Find average aging for each case type",
+    "code": "df.groupby('Claim_Type')['outstanding_age'].mean()"
+  },
+  {
+    "english": "Which policy number has the most outstanding claim entrys",
+    "code": "df[df['outstanding_age'].notna()]['Policy_Number'].value_counts().idxmax()"
+  },
+  {
+    "english": "Group outstanding claims by year of claim created date and fetch average outstanding days",
+    "code": "df[df['outstanding_age'].notna()].assign(year=df['Claim_Created_Date'].dt.year).groupby('year')['outstanding_age'].mean()"
+  },
+  {
+    "english": "Show grouped summary of outstanding aging by panel with count, sum and average",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].agg(['count','sum','mean'])"
+  },
+  {
+    "english": "Find which client has highest outstanding financial impact",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['Claim_Amount'].sum().idxmax()"
+  },
+  {
+    "english": "get highest outstanding claim grouped by ICD",
+    "code": "df.groupby('ICD_Code')['outstanding_age'].max()"
+  },
+  {
+    "english": "Shortest aging open claim record",
+    "code": "df.loc[df['outstanding_age'].idxmin()]"
+  },
+  {
+    "english": "Findclinicwithfastestsettlementtime",
+    "code": "df.groupby('Provider_Name')['settlement_age'].mean().idxmin()"
+  },
+  {
+    "english": "Which ICD disease category has the most outstanding claim entry count",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Disease'].value_counts().idxmax()"
+  },
+  {
+    "english": "Find gender distribution for rejected claims",
+    "code": "df[df['Status']=='Rejected']['Gender'].value_counts()"
+  },
+  {
+    "english": "Longest open claim record (oldest outstanding)",
+    "code": "df.loc[df['outstanding_age'].idxmax()]"
+  },
+  {
+    "english": "Find clients with more than 3 outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Patient_Name'].value_counts()[lambda x: x>3]"
+  },
+  {
+    "english": "Claims with resolved ageing up to 20 days ",
+    "code": "df[df['settlement_ageing'] <= 20]"
+  },
+  {
+    "english": "Get bottom 5 clients with lowest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].mean().nsmallest(5)"
+  },
+  {
+    "english": "Which client has the lowest approved amount",
+    "code": "df.groupby('Patient_Name')['Approved_Amount'].sum().idxmin()"
+  },
+  {
+    "english": "Which service provider has the least outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Provider_Name'].value_counts().idxmin()"
+  },
+  {
+    "english": "Group outstanding claim records by claim record type and sum claim record amount",
+    "code": "df[df['outstanding_age'].notna()].groupby('Claim_Type')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Which client/patient has the highest total outstanding age combined",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].sum().idxmax()"
+  },
+  {
+    "english": "top 10 provider by claim amount",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Group outstanding claims by ICD code and count number of claims",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Code')['Claim_Number'].count()"
+  },
+  {
+    "english": "Find average aging for each claim entry type",
+    "code": "df.groupby('Claim_Type')['outstanding_age'].mean()"
+  },
+  {
+    "english": "How many claims got settled fast (30 days) ",
+    "code": "df[df['settlement_ageing'] <= 30].shape[0]"
+  },
+  {
+    "english": "Find average outstanding ageing for a provider TAN TOCK SENG HOSPITAL (OUTPATIENT) ",
+    "code": "df[df['Provider_Name'] == 'TAN TOCK SENG HOSPITAL (OUTPATIENT)']['outstanding_age'].mean()"
+  },
+  {
+    "english": "Find which client has highest outstanding financial impact",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['Claim_Amount'].sum().idxmax()"
+  },
+  {
+    "english": "Show open daysing for all claim records for a given provider CASA DENTAL ADMIRALTY ",
+    "code": "df.loc[df['Provider_Name'] == 'CASA DENTAL ADMIRALTY', ['Claim_Number', 'outstanding_age']]"
+  },
+  {
+    "english": "Group outstanding claims by month and count number of cases",
+    "code": "df[df['outstanding_age'].notna()].assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "Which ICD disease category has the least outstanding claim count",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Disease'].value_counts().idxmin()"
+  },
+  {
+    "english": "Get month-wise count of rejected claims",
+    "code": "df[df['Status']=='Rejected'].assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "List top 10 claim amount providers ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Find total emergency claims with outstanding aging",
+    "code": "df[(df['Claim_Type']=='Emergency') & (df['outstanding_age'].notna())].shape[0]"
+  },
+  {
+    "english": "Find which gender has highest settlement delay",
+    "code": "df.groupby('Gender')['settlement_age'].mean().idxmax()"
+  },
+  {
+    "english": "Which ICD code has the highest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Code')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Total number of claim records settled within 18 days ",
+    "code": "df[df['settlement_ageing'] <= 18].shape[0]"
+  },
+  {
+    "english": "Get most frequent provider by claim volume",
+    "code": "df['Provider_Name'].value_counts().idxmax()"
+  },
+  {
+    "english": "Who are top 7 providers by claim amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(7)"
+  },
+  {
+    "english": "Top 7 panels by total claim entrys ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(7)"
+  },
+  {
+    "english": "Show settlement summary for claims within 30 days ",
+    "code": "df[df['settlement_ageing'] <= 30].describe()"
+  },
+  {
+    "english": "Which provider has the lowest average outstanding age   ",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Group outstanding claims by gender and calculate total claim amount",
+    "code": "df[df['outstanding_age'].notna()].groupby('Gender')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Find which client has highest outstanding financial impact",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['Claim_Amount'].sum().idxmax()"
+  },
+  {
+    "english": "Find highest approved claim amount record",
+    "code": "df.loc[df['Approved_Amount'].idxmax()]"
+  },
+  {
+    "english": "Group pending claims by provider",
+    "code": "df[df['Status']=='Pending'].groupby('Provider_Name')['Claim_Number'].count()"
+  },
+  {
+    "english": "Show resolved ageing for all claim records for a given panel Kang An TCM Pte Ltd",
+    "code": "df.loc[df['Provider_Name'] == 'Kang An TCM Pte Ltd', ['Claim_Number', 'settlement_age']]"
+  },
+  {
+    "english": "which service provider has the least outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Provider_Name'].value_counts().idxmin()"
+  },
+  {
+    "english": "What is the most common loss type ",
+    "code": "df['Loss_Type'].value_counts().idxmax()"
+  },
+  {
+    "english": "Total number of cases settled within 18 days ",
+    "code": "df[df['settlement_ageing'] <= 18].shape[0]"
+  },
+  {
+    "english": "Show ageing for claims created on a 03/01/2023",
+    "code": "df.loc[df['Claim_Created_Date'] == '03/01/2023', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Top 10 providers according to claim record amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Get top 5 patient names with highest rejection history",
+    "code": "df[df['Status']=='Rejected']['Patient_Name'].value_counts().nlargest(5)"
+  },
+  {
+    "english": "Give total claim entry amount for claim entrys settled within 30 days ",
+    "code": "df[df['settlement_ageing'] <= 30]['claim_amount'].sum()"
+  },
+  {
+    "english": "Get settlement ageing for a clm entry type Payment Approval",
+    "code": "df.loc[df['Claim_Type'] == 'Payment Approval', ['Claim_Number', 'settlement_age']]"
+  },
+  {
+    "english": "Top 7 providers by total cases ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(7)"
+  },
+  {
+    "english": "find how many cases have outstanding age more than 120 days",
+    "code": "df[df['outstanding_age'] > 120].shape[0]"
+  },
+  {
+    "english": "Compareclaimrecordamountvsapprovedamountgroupedbyserviceprovider",
+    "code": "df.groupby('Provider_Name')[['Claim_Amount','Approved_Amount']].sum()"
+  },
+  {
+    "english": "Show ageing details filtered by claim status Paid",
+    "code": "df.loc[df['Status'] == 'Paid', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "How many cases are settled within 10 days ",
+    "code": "df[df['settlement_ageing'] <= 10]"
+  },
+  {
+    "english": "Which hospital has the highest total outstanding claim records",
+    "code": "df[df['outstanding_age'].notna()]['Provider_Name'].value_counts().idxmax()"
+  },
+  {
+    "english": "Find total open daysing for a provider  HEALTHCARE FAMILY CLINIC & SURGERY",
+    "code": "df[df['Provider_Name'] == 'HEALTHCARE FAMILY CLINIC & SURGERY']['outstanding_age'].sum()"
+  },
+  {
+    "english": "Find clients with more than 3 outstanding cases",
+    "code": "df[df['outstanding_age'].notna()]['Patient_Name'].value_counts()[lambda x: x>3]"
+  },
+  {
+    "english": "Who are the leading 10 panels by claim record total ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "list top 10 claim amount panels ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Which claim number has the highest outstanding age",
+    "code": "df.loc[df['outstanding_age'].idxmax(), 'Claim_Number']"
+  },
+  {
+    "english": "Top 10 clients with highest total claim amount",
+    "code": "df.groupby('Patient_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Show ageing details for  patient name Adamn46",
+    "code": "df.loc[df['Patient_Name'] == 'Adam46', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Find lowest settlement aging among clients",
+    "code": "df.groupby('Patient_Name')['settlement_age'].mean().idxmin()"
+  },
+  {
+    "english": "Get outstanding ageing for a claim type cashless",
+    "code": "df.loc[df['Claim_Type'] == 'cashless', ['Claim_Number', 'outstanding_age']]"
+  },
+  {
+    "english": "What is the total claim amount for each loss type ",
+    "code": "df.groupby('Loss_Type')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Which client/patient has the least outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Patient_Name'].value_counts().idxmin()"
+  },
+  {
+    "english": "Compare total outstanding vs settled claim amounts",
+    "code": "{'outstanding': df[df['outstanding_age'].notna()]['Claim_Amount'].sum(), 'settled': df[df['settlement_age'].notna()]['Claim_Amount'].sum()}"
+  },
+  {
+    "english": "Show top 10 rejected cases by amount",
+    "code": "df[df['Status']=='Rejected'].nlargest(10, 'Claim_Amount')"
+  },
+  {
+    "english": "Find average outstanding ageing for a provider TAN TOCK SENG HOSPITAL (OUTPATIENT) ",
+    "code": "df[df['Provider_Name'] == 'TAN TOCK SENG HOSPITAL (OUTPATIENT)']['outstanding_age'].mean()"
+  },
+  {
+    "english": "Top 3 policies with highest total approved amount",
+    "code": "df.groupby('Policy_Number')['Approved_Amount'].sum().nlargest(3)"
+  },
+  {
+    "english": "Give the 10 highest claim providers ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Show ageing details filtered by claim status Paid",
+    "code": "df.loc[df['Status'] == 'Paid', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Who leads the providers in ICD disease count (top 10) ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Find average aging for each claim type",
+    "code": "df.groupby('Claim_Type')['outstanding_age'].mean()"
+  },
+  {
+    "english": "Getmonth-wisecountofrejectedcases",
+    "code": "df[df['Status']=='Rejected'].assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "Return top ten providers sorted by claims ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "top 10 provider by claim amount",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Which ICD code has the least outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Code'].value_counts().idxmin()"
+  },
+  {
+    "english": "Count how many claims were settled in 5 days ",
+    "code": "df[df['settlement_ageing'] <= 5].shape[0]"
+  },
+  {
+    "english": "Which gender has the lowest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('Gender')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Which policy number has the lowest average outstanding aging",
+    "code": "df[df['outstanding_age'].notna()].groupby('Policy_Number')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Which client has the lowest approved amount",
+    "code": "df.groupby('Patient_Name')['Approved_Amount'].sum().idxmin()"
+  },
+  {
+    "english": "Find ICD with highest pending claim entry count",
+    "code": "df[df['Status']=='Pending'].groupby('ICD_Code')['Claim_Number'].count().idxmax()"
+  },
+  {
+    "english": "Total paid claim records count grouped by provider",
+    "code": "df[df['Status']=='Paid'].groupby('Provider_Name')['Claim_Number'].count()"
+  },
+  {
+    "english": "Top 5 ICD diseases by paid case count",
+    "code": "df[df['Status']=='Paid'].groupby('ICD_Disease')['Claim_Number'].count().nlargest(5)"
+  },
+  {
+    "english": "Top 3 policies with highest total approved amount",
+    "code": "df.groupby('Policy_Number')['Approved_Amount'].sum().nlargest(3)"
+  },
+  {
+    "english": "Which policy number has the most outstanding cases",
+    "code": "df[df['outstanding_age'].notna()]['Policy_Number'].value_counts().idxmax()"
+  },
+  {
+    "english": "Give total claim amount for claims settled within 30 days ",
+    "code": "df[df['settlement_ageing'] <= 30]['claim_amount'].sum()"
+  },
+  {
+    "english": "Which providers have highest claim amounts (top 10) ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Group by ICD and find average approved amount",
+    "code": "df.groupby('ICD_Code')['Approved_Amount'].mean()"
+  },
+  {
+    "english": "Find highest approved claim amount record",
+    "code": "df.loc[df['Approved_Amount'].idxmax()]"
+  },
+  {
+    "english": "Give all ageing data based on gender Female",
+    "code": "df.loc[df['Gender'] == 'F', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Group outstanding claim records by claim record type and sum claim record amount",
+    "code": "df[df['outstanding_age'].notna()].groupby('Claim_Type')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Show top five hospitals based on case amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(5)"
+  },
+  {
+    "english": "Which gender has the lowest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('Gender')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Show top 10 clinics by disease coverage ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "which client/patient has the highest total unsettled ageing combined",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].sum().idxmax()"
+  },
+  {
+    "english": "Who are top 5 providers by claim amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(5)"
+  },
+  {
+    "english": "Find outstanding ageing for a given ICD code G07",
+    "code": "df.loc[df['ICD_Code'] == 'G07', ['Claim_Number', 'outstanding_age']]"
+  },
+  {
+    "english": "Get month-wise count of rejected claims",
+    "code": "df[df['Status']=='Rejected'].assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "Group outstanding claims by provider and calculate average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].mean()"
+  },
+  {
+    "english": "Which case number has the highest outstanding age",
+    "code": "df.loc[df['outstanding_age'].idxmax(), 'Claim_Number']"
+  },
+  {
+    "english": "Find which client has highest outstanding financial impact",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['Claim_Amount'].sum().idxmax()"
+  },
+  {
+    "english": "Get highest average claim amount grouped by gender",
+    "code": "df.groupby('Gender')['Claim_Amount'].mean().idxmax()"
+  },
+  {
+    "english": "Find gender distribution for rejected claim entrys",
+    "code": "df[df['Status']=='Rejected']['Gender'].value_counts()"
+  },
+  {
+    "english": "Givethe10highestclaimrecordhospitals ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Group outstanding claims by month and count number of cases",
+    "code": "df[df['outstanding_age'].notna()].assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "What is the total claim amount for each loss type ",
+    "code": "df.groupby('Loss_Type')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Which policy number has the most outstanding cases",
+    "code": "df[df['outstanding_age'].notna()]['Policy_Number'].value_counts().idxmax()"
+  },
+  {
+    "english": "Who leads the providers in ICD disease count (top 10) ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Show grouped summary by ICD disease with count and sum of case amount",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Disease')['Claim_Amount'].agg(['count','sum'])"
+  },
+  {
+    "english": "Which policy number has the most outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Policy_Number'].value_counts().idxmax()"
+  },
+  {
+    "english": "Total paid claims count grouped by provider",
+    "code": "df[df['Status']=='Paid'].groupby('Provider_Name')['Claim_Number'].count()"
+  },
+  {
+    "english": "Who are top 3 providers by icd disease ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(3)"
+  },
+  {
+    "english": "what is settlement ageing for a given icd code j26",
+    "code": "df.loc[df['ICD_Code'] == 'J26', ['Claim_Number', 'settlement_age']]"
+  },
+  {
+    "english": "List claims settled in 15 days or less ",
+    "code": "df[df['settlement_ageing'] <= 15]"
+  },
+  {
+    "english": "Which gender has the highest number of outstanding claim entrys",
+    "code": "df[df['outstanding_age'].notna()]['Gender'].value_counts().idxmax()"
+  },
+  {
+    "english": "Which client/patient has the lowest total outstanding age combined",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].sum().idxmin()"
+  },
+  {
+    "english": "Which clm record number has the highest outstanding age",
+    "code": "df.loc[df['outstanding_age'].idxmax(), 'Claim_Number']"
+  },
+  {
+    "english": "Find open daysing for a claim number CBGDC22013782-00",
+    "code": "df.loc[df['Claim_Number'] == 'CBGDC22013782-00', 'outstanding_age']"
+  },
+  {
+    "english": "Show all claim records that were settled within 10 days ",
+    "code": "df[df['settlement_ageing'] <= 10]"
+  },
+  {
+    "english": "Show outstanding ageing for all claims for a given provider CASA DENTAL ADMIRALTY ",
+    "code": "df.loc[df['Provider_Name'] == 'CASA DENTAL ADMIRALTY', ['Claim_Number', 'outstanding_age']]"
+  },
+  {
+    "english": "Total rejected claims grouped by ICD code",
+    "code": "df[df['Status']=='Rejected'].groupby('ICD_Code')['Claim_Number'].count()"
+  },
+  {
+    "english": "Show ageing details for  patient name Adamn46",
+    "code": "df.loc[df['Patient_Name'] == 'Adam46', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Who are top 12 service providers by icd disease ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].count().nlargest(12)"
+  },
+  {
+    "english": "Top10providerswithmaximumICDcodes ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Show leading 10 clinics by claim entry sum ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Get settlement ageing for a claim type Payment Approval",
+    "code": "df.loc[df['Claim_Type'] == 'Payment Approval', ['Claim_Number', 'settlement_age']]"
+  },
+  {
+    "english": "Group claims by provider and aggregate metrics",
+    "code": "df.groupby('Provider_Name').agg({'Claim_Amount':'sum','Approved_Amount':'sum','outstanding_age':'mean'})"
+  },
+  {
+    "english": "Totalnumberofcasessettledwithin18days ",
+    "code": "df[df['settlement_ageing'] <= 18].shape[0]"
+  },
+  {
+    "english": "Show settlement ageing for all claims for a given provider Kang An TCM Pte Ltd",
+    "code": "df.loc[df['Provider_Name'] == 'Kang An TCM Pte Ltd', ['Claim_Number', 'settlement_age']]"
+  },
+  {
+    "english": "Which client/patient has the most outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Patient_Name'].value_counts().idxmax()"
+  },
+  {
+    "english": "Group outstanding cases by ICD code and count number of cases",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Code')['Claim_Number'].count()"
+  },
+  {
+    "english": "How many claims are settled within 10 days ",
+    "code": "df[df['settlement_ageing'] <= 10]"
+  },
+  {
+    "english": "FindopendaysingforapolicynumberBGDC221000109-00-000",
+    "code": "df.loc[df['Policy_Number'] == 'BGDC221000109-00-000', 'outstanding_age']"
+  },
+  {
+    "english": "Which provider has the highest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Group claims by service provider and calculate rejection ratio",
+    "code": "df.groupby('Provider_Name').apply(lambda x: (x['Status']=='Rejected').mean())"
+  },
+  {
+    "english": "Top 10 providers with most ICD diseases ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "How many claims got settled fast (\u226430 days) ",
+    "code": "df[df['settlement_ageing'] <= 30].shape[0]"
+  },
+  {
+    "english": "Show all ageing details for policy number BGDP211000196-01-000",
+    "code": "df.loc[df['Policy_Number'] == 'BGDP211000196-01-000', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Top 5 ICD codes with highest financial loss",
+    "code": "df.groupby('ICD_Code')['Claim_Amount'].sum().nlargest(5)"
+  },
+  {
+    "english": "Longest open claim record record (oldest outstanding)",
+    "code": "df.loc[df['outstanding_age'].idxmax()]"
+  },
+  {
+    "english": "Longest open claim entry record (oldest outstanding)",
+    "code": "df.loc[df['outstanding_age'].idxmax()]"
+  },
+  {
+    "english": "List top 10 providers by unique ICD diseases ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Get resolved ageing for a claim type Payment Approval",
+    "code": "df.loc[df['Claim_Type'] == 'Payment Approval', ['Claim_Number', 'settlement_age']]"
+  },
+  {
+    "english": "which gender has the highest number of outstanding claim records",
+    "code": "df[df['outstanding_age'].notna()]['Gender'].value_counts().idxmax()"
+  },
+  {
+    "english": "Show all ageing details (outstanding and settlement) for a claim number  CBGDC22021912-00",
+    "code": "df.loc[df['Claim_Number'] == 'CBGDC22021912-00 ', ['outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Which service providers have highest claim amounts (top 10) ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Which client has the highest approved amount",
+    "code": "df.groupby('Patient_Name')['Approved_Amount'].sum().idxmax()"
+  },
+  {
+    "english": "Which provider has the highest total outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Provider_Name'].value_counts().idxmax()"
+  },
+  {
+    "english": "Which ICD code has the most outstanding claim entrys",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Code'].value_counts().idxmax()"
+  },
+  {
+    "english": "Show grouped summary of outstanding aging by service provider with count, sum and average",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].agg(['count','sum','mean'])"
+  },
+  {
+    "english": "Which ICD code has the lowest average unsettled ageing",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Code')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Show all ageing details for policy number BGDP211000196-01-000",
+    "code": "df.loc[df['Policy_Number'] == 'BGDP211000196-01-000', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Which ICD disease category has the least outstanding claim entry count",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Disease'].value_counts().idxmin()"
+  },
+  {
+    "english": "what is settlement ageing for a given ICD code J26",
+    "code": "df.loc[df['ICD_Code'] == 'J26', ['Claim_Number', 'settlement_age']]"
+  },
+  {
+    "english": "Which clinic has the lowest average days pending",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Find how many claim records have days pending more than 120 days",
+    "code": "df[df['outstanding_age'] > 120].shape[0]"
+  },
+  {
+    "english": "Which provider has the lowest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Group outstanding claims by gender and calculate total claim amount",
+    "code": "df[df['outstanding_age'].notna()].groupby('Gender')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Which policy number has the least outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Policy_Number'].value_counts().idxmin()"
+  },
+  {
+    "english": "Which client has the highest approved amount",
+    "code": "df.groupby('Patient_Name')['Approved_Amount'].sum().idxmax()"
+  },
+  {
+    "english": "Top 5 clients with highest claim count",
+    "code": "df['Patient_Name'].value_counts().nlargest(5)"
+  },
+  {
+    "english": "Claims with resolved ageing up to 20 days ",
+    "code": "df[df['settlement_ageing'] <= 20]"
+  },
+  {
+    "english": "Group claims by policy and please show outstanding totals",
+    "code": "df[df['outstanding_age'].notna()].groupby('Policy_Number')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "List top 10 claim amount providers ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "find outstanding ageing for a given ICD code G07",
+    "code": "df.loc[df['ICD_Code'] == 'G07', ['Claim_Number', 'outstanding_age']]"
+  },
+  {
+    "english": "Claims with settlement ageing up to 20 days ",
+    "code": "df[df['settlement_ageing'] <= 20]"
+  },
+  {
+    "english": "Top 10 paid claim records by amount",
+    "code": "df[df['Status']=='Paid'].nlargest(10, 'Claim_Amount')"
+  },
+  {
+    "english": "Who are the leading 10 hospitals by case total ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Top 5 ICD codes with highest financial loss",
+    "code": "df.groupby('ICD_Code')['Claim_Amount'].sum().nlargest(5)"
+  },
+  {
+    "english": "Return top ten clinics with most disease types ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Which client/patient has the highest total pending duration combined",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].sum().idxmax()"
+  },
+  {
+    "english": "Who are top 3 providers by icd disease ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(3)"
+  },
+  {
+    "english": "Top 10 clients with highest total claim amount",
+    "code": "df.groupby('Patient_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "what is days to settleing for a given ICD code J26",
+    "code": "df.loc[df['ICD_Code'] == 'J26', ['Claim_Number', 'settlement_age']]"
+  },
+  {
+    "english": "Which client/patient has the least outstanding cases",
+    "code": "df[df['outstanding_age'].notna()]['Patient_Name'].value_counts().idxmin()"
+  },
+  {
+    "english": "What is the total clm amount for each loss type ",
+    "code": "df.groupby('Loss_Type')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Give the 10 highest case service providers ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Which gender has the least number of outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Gender'].value_counts().idxmin()"
+  },
+  {
+    "english": "List top 10 claim record amount providers ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Get top 5 clients with highest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].mean().nlargest(5)"
+  },
+  {
+    "english": "Which ICD code has the lowest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Code')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Group outstanding claim records by panel and calculate average pending duration",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].mean()"
+  },
+  {
+    "english": "What is the most common loss type ",
+    "code": "df['Loss_Type'].value_counts().idxmax()"
+  },
+  {
+    "english": "find settlement ageing for claim number CBGDC22017155-00",
+    "code": "df.loc[df['Claim_Number'] == 'CBGDC22017155-00', 'settlement_age']"
+  },
+  {
+    "english": "Get month-wise count of rejected claims",
+    "code": "df[df['Status']=='Rejected'].assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "Find which gender has highest settlement delay",
+    "code": "df.groupby('Gender')['settlement_age'].mean().idxmax()"
+  },
+  {
+    "english": "Top 5 policies with highest settlement delay",
+    "code": "df.groupby('Policy_Number')['settlement_age'].mean().nlargest(5)"
+  },
+  {
+    "english": "Get top 5 patient names with highest rejection history",
+    "code": "df[df['Status']=='Rejected']['Patient_Name'].value_counts().nlargest(5)"
+  },
+  {
+    "english": "Show leading 10 providers by claim entry sum ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Group outstanding claims by claim type and calculate highest average outstanding aging",
+    "code": "df[df['outstanding_age'].notna()].groupby('Claim_Type')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Top 10 clinics according to claim record amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Group outstanding claims by claim type and sum claim amount",
+    "code": "df[df['outstanding_age'].notna()].groupby('Claim_Type')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Which policy number has the least outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Policy_Number'].value_counts().idxmin()"
+  },
+  {
+    "english": "Get total outstanding age for a disease Common Cold / flu",
+    "code": "df[df['ICD_Disease'] == 'Common Cold / flu']['outstanding_age'].sum()"
+  },
+  {
+    "english": "Find total outstanding ageing for a provider  HEALTHCARE FAMILY CLINIC & SURGERY",
+    "code": "df[df['Provider_Name'] == 'HEALTHCARE FAMILY CLINIC & SURGERY']['outstanding_age'].sum()"
+  },
+  {
+    "english": "Total claim entry count grouped by month of creation   ",
+    "code": "df.assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "show ageing for cases created on a 03/01/2023",
+    "code": "df.loc[df['Claim_Created_Date'] == '03/01/2023', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Return top ten providers with most disease types ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Group outstanding claims by year of claim created date and find average outstanding days",
+    "code": "df[df['outstanding_age'].notna()].assign(year=df['Claim_Created_Date'].dt.year).groupby('year')['outstanding_age'].mean()"
+  },
+  {
+    "english": "Shortest aging open claim record",
+    "code": "df.loc[df['outstanding_age'].idxmin()]"
+  },
+  {
+    "english": "Group outstanding cases by ICD code and count number of cases",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Code')['Claim_Number'].count()"
+  },
+  {
+    "english": "Top 5 policies with highest settlement delay",
+    "code": "df.groupby('Policy_Number')['settlement_age'].mean().nlargest(5)"
+  },
+  {
+    "english": "show leading 10 panels by case sum ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Get total days pending for a disease Common Cold / flu",
+    "code": "df[df['ICD_Disease'] == 'Common Cold / flu']['outstanding_age'].sum()"
+  },
+  {
+    "english": "Which service providers have the highest number of diseases (top 10) ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Give claims that got settled within 7 days ",
+    "code": "df[df['settlement_ageing'] <= 7]"
+  },
+  {
+    "english": "How many cases are settled within 10 days ",
+    "code": "df[df['settlement_ageing'] <= 10]"
+  },
+  {
+    "english": "Who are the leading 10 providers by claim total ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(10)"
+  },
+  {
+    "english": "Group by ICD and find average approved amount",
+    "code": "df.groupby('ICD_Code')['Approved_Amount'].mean()"
+  },
+  {
+    "english": "Which panel has the lowest average days pending",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Which ICD code has the most outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Code'].value_counts().idxmax()"
+  },
+  {
+    "english": "Get top 5 clients with highest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].mean().nlargest(5)"
+  },
+  {
+    "english": "Show unsettled ageinging for all cases for a given hospital CASA DENTAL ADMIRALTY ",
+    "code": "df.loc[df['Provider_Name'] == 'CASA DENTAL ADMIRALTY', ['Claim_Number', 'outstanding_age']]"
+  },
+  {
+    "english": "Which gender has the least number of outstanding claim records",
+    "code": "df[df['outstanding_age'].notna()]['Gender'].value_counts().idxmin()"
+  },
+  {
+    "english": "get highest average claim amount grouped by gender",
+    "code": "df.groupby('Gender')['Claim_Amount'].mean().idxmax()"
+  },
+  {
+    "english": "Find how many claim entrys have outstanding age more than 120 days",
+    "code": "df[df['outstanding_age'] > 120].shape[0]"
+  },
+  {
+    "english": "Which policy number has the lowest average outstanding aging",
+    "code": "df[df['outstanding_age'].notna()].groupby('Policy_Number')['outstanding_age'].mean().idxmin()"
+  },
+  {
+    "english": "Average outstanding aging grouped by ICD disease",
+    "code": "df.groupby('ICD_Disease')['outstanding_age'].mean()"
+  },
+  {
+    "english": "Who are top 12 providers by icd disease ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].count().nlargest(12)"
+  },
+  {
+    "english": "Who are top 5 clinics by case amount ",
+    "code": "df.groupby('Provider_Name')['Claim_Amount'].sum().nlargest(5)"
+  },
+  {
+    "english": "Find outstanding ageing for a claim number CBGDC22013782-00",
+    "code": "df.loc[df['Claim_Number'] == 'CBGDC22013782-00', 'outstanding_age']"
+  },
+  {
+    "english": "TotalrejectedclaimentrysgroupedbyICDcode",
+    "code": "df[df['Status']=='Rejected'].groupby('ICD_Code')['Claim_Number'].count()"
+  },
+  {
+    "english": "Give 10 service providers with highest number of ICD codes ",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Show all clms that were settled within 10 days ",
+    "code": "df[df['settlement_ageing'] <= 10]"
+  },
+  {
+    "english": "Top 3 policies with highest total approved amount",
+    "code": "df.groupby('Policy_Number')['Approved_Amount'].sum().nlargest(3)"
+  },
+  {
+    "english": "Group claim entrys by panel and calculate rejection ratio",
+    "code": "df.groupby('Provider_Name').apply(lambda x: (x['Status']=='Rejected').mean())"
+  }
+,
+
+  {
+    "english": "Group claims by policy and find outstanding totals",
+    "code": "df[df['outstanding_age'].notna()].groupby('Policy_Number')['Claim_Amount'].sum()"
+  },
+  {
+    "english": "Find which gender has highest settlement delay",
+    "code": "df.groupby('Gender')['settlement_age'].mean().idxmax()"
+  },
+  {
+    "english": "Get highest average case amount grouped by gender",
+    "code": "df.groupby('Gender')['Claim_Amount'].mean().idxmax()"
+  },
+  {
+    "english": "Which claim entry number has the lowest pending duration",
+    "code": "df.loc[df['outstanding_age'].idxmin(), 'Claim_Number']"
+  },
+  {
+    "english": "group approved claims by month",
+    "code": "df[df['Status']=='Approved'].assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "Which client/patient has the highest total outstanding age combined",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].sum().idxmax()"
+  },
+  {
+    "english": "Find highest settlement aging among clients",
+    "code": "df.groupby('Patient_Name')['settlement_age'].mean().idxmax()"
+  },
+  {
+    "english": "Give all ageing data based on gender Female",
+    "code": "df.loc[df['Gender'] == 'F', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Count claims where settlement age is greater than 90 days",
+    "code": "df[df['settlement_age'] > 90].shape[0]"
+  },
+  {
+    "english": "What is the total approved amount for each loss type?",
+    "code": "df.groupby('Loss_Type')['Approved_Amount'].sum()"
+  },
+  {
+    "english": "Group outstanding claim entrys by claim entry type and calculate highest average outstanding aging",
+    "code": "df[df['outstanding_age'].notna()].groupby('Claim_Type')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Find ICD category with fastest settlement duration???",
+    "code": "df.groupby('ICD_Disease')['settlement_age'].mean().idxmin()"
+  },
+  {
+    "english": "Group outstanding claims by year of claim created date and find average outstanding days",
+    "code": "df[df['outstanding_age'].notna()].assign(year=df['Claim_Created_Date'].dt.year).groupby('year')['outstanding_age'].mean()"
+  },
+  {
+    "english": "Show top 10 rejected claims by amount",
+    "code": "df[df['Status']=='Rejected'].nlargest(10, 'Claim_Amount')"
+  },
+  {
+    "english": "show closure timeing for all claims for a given hospital Kang An TCM Pte Ltd",
+    "code": "df.loc[df['Provider_Name'] == 'Kang An TCM Pte Ltd', ['Claim_Number', 'settlement_age']]"
+  },
+  {
+    "english": "Which claim entry number has the lowest outstanding age",
+    "code": "df.loc[df['outstanding_age'].idxmin(), 'Claim_Number']"
+  },
+  {
+    "english": "What is the total approved amount for each loss type?",
+    "code": "df.groupby('Loss_Type')['Approved_Amount'].sum()"
+  },
+  {
+    "english": "Get highest outstanding claim grouped by ICD",
+    "code": "df.groupby('ICD_Code')['outstanding_age'].max()"
+  },
+  {
+    "english": "Average outstanding aging grouped by ICD disease",
+    "code": "df.groupby('ICD_Disease')['outstanding_age'].mean()"
+  },
+  {
+    "english": "Get most frequent hospital by claim volume",
+    "code": "df['Provider_Name'].value_counts().idxmax()"
+  },
+  {
+    "english": "Top 5 clients with highest claim count",
+    "code": "df['Patient_Name'].value_counts().nlargest(5)"
+  },
+  {
+    "english": "Which client/patient has the lowest total pending duration combined",
+    "code": "df[df['outstanding_age'].notna()].groupby('Patient_Name')['outstanding_age'].sum().idxmin()"
+  },
+  {
+    "english": "Shortest aging open claim record",
+    "code": "df.loc[df['outstanding_age'].idxmin()]"
+  },
+  {
+    "english": "Which ICD code has the least outstanding claim records",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Code'].value_counts().idxmin()"
+  },
+  {
+    "english": "Find ICD category with fastest settlement duration",
+    "code": "df.groupby('ICD_Disease')['settlement_age'].mean().idxmin()"
+  },
+  {
+    "english": "Group claims by provider and calculate rejection ratio",
+    "code": "df.groupby('Provider_Name').apply(lambda x: (x['Status']=='Rejected').mean())"
+  },
+  {
+    "english": "which policy number has the least outstanding claims",
+    "code": "df[df['outstanding_age'].notna()]['Policy_Number'].value_counts().idxmin()"
+  },
+  {
+    "english": "What is the most common loss type?",
+    "code": "df['Loss_Type'].value_counts().idxmax()"
+  },
+  {
+    "english": "Find provider with fastest settlement time",
+    "code": "df.groupby('Provider_Name')['settlement_age'].mean().idxmin()"
+  },
+  {
+    "english": "Get days to settleing for a case type Payment Approval",
+    "code": "df.loc[df['Claim_Type'] == 'Payment Approval', ['Claim_Number', 'settlement_age']]"
+  },
+  {
+    "english": "Group pending claims by provider",
+    "code": "df[df['Status']=='Pending'].groupby('Provider_Name')['Claim_Number'].count()"
+  },
+  {
+    "english": "Find highest settlement aging among clients",
+    "code": "df.groupby('Patient_Name')['settlement_age'].mean().idxmax()"
+  },
+  {
+    "english": "Group outstanding claim records by ICD code and count number of claim records",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Code')['Claim_Number'].count()"
+  },
+  {
+    "english": "Which service provider has the highest total outstanding cases",
+    "code": "df[df['outstanding_age'].notna()]['Provider_Name'].value_counts().idxmax()"
+  },
+  {
+    "english": "Group claims by provider and aggregate metrics",
+    "code": "df.groupby('Provider_Name').agg({'Claim_Amount':'sum','Approved_Amount':'sum','outstanding_age':'mean'})"
+  },
+  {
+    "english": "Whichclient/patienthastheleastoutstandingclaimrecords",
+    "code": "df[df['outstanding_age'].notna()]['Patient_Name'].value_counts().idxmin()"
+  },
+  {
+    "english": "Show grouped summary by ICD disease with count and sum of case amount",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Disease')['Claim_Amount'].agg(['count','sum'])"
+  },
+  {
+    "english": "Which policy number has the highest average outstanding aging",
+    "code": "df[df['outstanding_age'].notna()].groupby('Policy_Number')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Which ICD code has the most outstanding cases",
+    "code": "df[df['outstanding_age'].notna()]['ICD_Code'].value_counts().idxmax()"
+  },
+  {
+    "english": "Who are top 3 hospitals by icd disease?",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(3)"
+  },
+  {
+    "english": "List claim entrys settled in 15 days or less.",
+    "code": "df[df['settlement_ageing'] <= 15]"
+  },
+  {
+    "english": "what is settlement ageing for a policy number BGDC221000033-00-000",
+    "code": "df.loc[df['Policy_Number'] == 'BGDC221000033-00-000', 'settlement_age']"
+  },
+  {
+    "english": "Find settlement ageing for claim number CBGDC22017155-00",
+    "code": "df.loc[df['Claim_Number'] == 'CBGDC22017155-00', 'settlement_age']"
+  },
+  {
+    "english": "Find which provider has the lowest outstanding financial exposure",
+    "code": "df[df['outstanding_age'].notna()].groupby('Provider_Name')['Claim_Amount'].sum().idxmin()"
+  },
+  {
+    "english": "Who leads the providers in ICD disease count (top 10)?",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Get total outstanding age for a disease Common Cold / flu",
+    "code": "df[df['ICD_Disease'] == 'Common Cold / flu']['outstanding_age'].sum()"
+  },
+  {
+    "english": "Which ICD code has the highest average outstanding age",
+    "code": "df[df['outstanding_age'].notna()].groupby('ICD_Code')['outstanding_age'].mean().idxmax()"
+  },
+  {
+    "english": "Group claims by panel and calculate rejection ratio",
+    "code": "df.groupby('Provider_Name').apply(lambda x: (x['Status']=='Rejected').mean())"
+  },
+  {
+    "english": "Total claim count grouped by month of creation",
+    "code": "df.assign(month=df['Claim_Created_Date'].dt.month).groupby('month')['Claim_Number'].count()"
+  },
+  {
+    "english": "Give all ageing data based on gender Female",
+    "code": "df.loc[df['Gender'] == 'F', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Find ICD category with fastest settlement duration",
+    "code": "df.groupby('ICD_Disease')['settlement_age'].mean().idxmin()"
+  },
+  {
+    "english": "Search all ageing data based on gender Male",
+    "code": "df.loc[df['Gender'] == 'M', ['Claim_Number', 'outstanding_age', 'settlement_age']]"
+  },
+  {
+    "english": "Top 5 ICD diseases by paid claim count",
+    "code": "df[df['Status']=='Paid'].groupby('ICD_Disease')['Claim_Number'].count().nlargest(5)"
+  },
+  {
+    "english": "List top 10 clinics by unique ICD diseases.",
+    "code": "df.groupby('Provider_Name')['Icd Disease'].nunique().nlargest(10)"
+  },
+  {
+    "english": "Group by ICD and find average approved amount",
+    "code": "df.groupby('ICD_Code')['Approved_Amount'].mean()"
+  },
+  {
+    "english": "Top 5 policies with highest settlement delay",
+    "code": "df.groupby('Policy_Number')['settlement_age'].mean().nlargest(5)"
+  }
+]
\ No newline at end of file