Update pages/2_Data Cleaning and Processing .py

pages/2_Data Cleaning and Processing .py

@@ -726,7 +726,7 @@ if df is not None:
         st.write(df['price'].describe())  # Use describe() for a summary of the 'price' column
         
         # Filter rows where price is less than 600 or greater than 40,000
-        df_filtered = df[(df['price'] < 500) | (df['price'] > 40000)]
+        df_filtered = df[(df['price'] < 400) | (df['price'] > 40000)]
         df.drop(df_filtered.index, inplace=True)  # Drop rows based on the filtered indices
         
         st.write("### Updated Dataset with Filtered Rows")
@@ -745,7 +745,7 @@ if df is not None:
     """
     This tool allows you to filter rows in a dataset based on the `price` column and drop the rows where the `price` is either less than 600 or greater than 40,000.  
     - **Input**: A dataset with a `price` column.
-    - **Process**: Removes rows where `price` is less than 500 or greater than 40,000.
+    - **Process**: Removes rows where `price` is less than 400 or greater than 40,000.
     - **Output**: The filtered dataset with rows dropped and the `price` column intact.
     
     #### Why Drop Rows Based on Price?