Not OP.

But I'm guessing it's something like this:

import pandas as pd

def calculate_monthly_business_sum(df, date_column, value_column):

    """
    Calculate monthly sums anchored to the last business day of each month

    Parameters:
    df: DataFrame with dates and values
    date_column: name of date column
    value_column: name of value column to sum
    
    Returns:
    DataFrame with sums anchored to last business day
    """
    # Ensure date column is datetime
    df[date_column] = pd.to_datetime(df[date_column])
    
    # Group by end of business month and sum
    monthly_sum = df.groupby(pd.Grouper(
        key=date_column,
        freq='BME'  # Business Month End frequency
    ))[value_column].sum().reset_index()

    return monthly_sum

# Example usage:

df = pd.DataFrame({ 'date': ['2024-01-01', '2024-01-31', '2024-02-29'], 'amount': [100, 200, 300] })

result = calculate_monthly_business_sum(df, 'date', 'amount')

print(result)

Which you can run here => https://python-fiddle.com/examples/pandas?checkpoint=1732114...