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Ashley Wu
Generates insights into the data given
"""
import pandas as pd
import gradio as gr
import utils
def calculate_stats(df):
"""
Create a dataframe with each column's mean, median, max, min, quartiles for numeric columns and unique values, value counts, and mode for categorical columns as well as missing values
Args:
dataframe: The pandas DataFrame to analyze
Returns:
A DataFrame with column statistics
"""
df = utils.detect_categories(df)
df = utils.detect_dates(df)
if df is None or len(df) == 0:
return None, None
numeric_data = pd.DataFrame()
categorical_data = pd.DataFrame()
for column in df.columns:
dtype = df[column].dtype
if dtype.kind == 'i' or dtype.kind == 'f' or dtype.kind == 'u' or dtype.kind == 'c':
numeric_data[column] = df[column]
elif dtype.name == 'category':
categorical_data[column] = df[column]
elif dtype.kind == 'M' or dtype.kind == 'm':
continue
else:
gr.Warning(f"dType of {column} could not be recognised!", duration=5)
numeric_data = calcualte_numeric_data(numeric_data)
categorical_data = calculate_categorical_data(categorical_data)
return numeric_data, categorical_data
def calculate_categorical_data(df):
"""
Calculates data for categorical data exploration
Args:
df: dataframe
Returns
table: dataframe
"""
table = pd.DataFrame({
'Column': pd.Series(dtype='string'),
'Mode': pd.Series(dtype='string'),
'Missing Values': pd.Series(dtype='int16'),
'Value Counts': pd.Series(dtype='string'),
})
for column in df.columns:
mode = df[column].mode()[0]
count = str(df[column].value_counts().to_dict())
missing_values = df[column].isnull().sum()
table.loc[len(table)] = [column, mode, missing_values, count]
return table
def calcualte_numeric_data(df):
"""
Calcualtes statistics for numerical data
Args:
df: dataframe
Returns:
table: df with column statistics
"""
table = pd.DataFrame({
'Column': pd.Series(dtype='string'),
'Mean': pd.Series(dtype='float16'),
'Median': pd.Series(dtype='float16'),
'Max': pd.Series(dtype='float16'),
'Min': pd.Series(dtype='float16'),
'Bottom Quartile': pd.Series(dtype='float16'),
'Top Quartile': pd.Series(dtype='float16'),
'Missing Values': pd.Series(dtype='int16'),
})
for column in df.columns:
mean = df[column].mean()
median = df[column].median()
max_val = df[column].max()
min_val = df[column].min()
q25 = df[column].quantile(.25)
q75 = df[column].quantile(.75)
missing_values = df[column].isnull().sum()
table.loc[len(table)] = [column, mean, median, max_val, min_val, q25, q75, missing_values]
return table
def correlation_matrix(df):
"""
Create a correleation matrix for the given dataframe
Args:
df: dataframe
Returns:
correlation_matrix: numpy array
"""
for col in df.columns:
if df[col].dtype.name == 'object':
df = df.drop(col, axis=1)
return df.corr()
def max_min_df(df):
"""
Find the max and min of a data frame
Arg:
df: dataframe
Returns:
max_min_df: dataframe
"""
for col in df.columns:
if df[col].dtype.name == 'object':
df = df.drop(col, axis=1)
df_max = df.max()
df_min = df.min()
result = pd.DataFrame({
'Column': df_max.index,
'Max': df_max.values,
'Min': df_min.values
})
return result
def std(df):
"""
Finds the standard deviation of the dataframe
Args:
df: dataframe
Returns:
std: dataframe
"""
for col in df.columns:
if df[col].dtype.name == 'object':
df = df.drop(col, axis=1)
std_series = df.std()
result = std_series.to_frame(name='Standard Deviation').reset_index()
result.columns = ['Column', 'Standard Deviation']
return result
def var(df):
"""
Finds the variance of values in a dataframe
Args:
df: dataframe
Returns:
var: df
"""
for col in df.columns:
if df[col].dtype.name == 'object':
df = df.drop(col, axis=1)
var_series = df.var()
result = var_series.to_frame(name='Variance').reset_index()
result.columns = ['Column', 'Variance']
return result |