FireDucks: Pandas but Faster

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

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...

df.resample("BME").sum()

Done. One line of code and it is quite obvious what it is doing - with perhaps the small exception of BME, but if you want max readability you could do:

df.resample(pd.offsets.BusinessMonthEnd()).sum()

This is why people use pandas.

> df.resample("BME").sum()

Assuming `df` is a dataframe (ie table) indexed by a timestamp index, which is usual for timeseries analysis.

"BME" stands for BusinessMonthEnd, which you can type out if you want the code to be easier to read by someone not familiar with pandas.

It so easy for my analyst team because of daily uses but my developers probavly will never thought/know BME and decided to implement the code again.