Detailed explanation of how to use pandas to convert data rows and columns to rows

import pandas as pd  # Import pandas library
def pivot_excel_data(input_file, output_file):
    """
    Will Excel Convert data rows in the file into columns，and save as new Excel document
    
    Parameters:
        input_file (str): Input Excel document路径
        output_file (str): Output Excel document路径
        
    Returns:
        None
    """
    # Read Excel data    df = pd.read_excel(input_file, sheet_name='Sheet1')
    
    # Use the pivot_table() function to convert data rows into columns    df_pivot = df.pivot_table(index='Shop', columns='New Fee Type', values='Amount').reset_index()
    
    # Save the processed data to a new Excel file    df_pivot.to_excel(output_file, index=False)

# Call function for data processinginput_file = 'C:\\Users\\Administrator\\Desktop\\New Data_After Processing.xlsx'
output_file = 'converted_data.xlsx'
pivot_excel_data(input_file, output_file)

import pandas as pd  # Import pandas library
def melt_excel_data(input_file, output_file):
    """
    Will Excel Convert data columns in the file to rows，and save as new Excel document
    
    Parameters:
        input_file (str): Input Excel document路径
        output_file (str): Output Excel document路径
        
    Returns:
        None
    """
    # Read Excel data    df = pd.read_excel(input_file, sheet_name='Sheet1')
    
    # Use melt() function to convert data columns into rows    df_melted = (id_vars=['Shop'], var_name='Fee Type', value_name='Amount')
    
    # Save the processed data to a new Excel file    df_melted.to_excel(output_file, index=False)

# Call function for data processinginput_file = 'C:\\Users\\Administrator\\Desktop\\converted_data.xlsx'
output_file = 'converted_data2.xlsx'
melt_excel_data(input_file, output_file)