Understanding Pandas GroupBy Month

Introduction to Pandas Grouby Feature

The groupby function in pandas is a powerful feature used for data aggregation. In this article, we will delve into the specifics of using groupby with the pd.Grouper object to perform monthly aggregations.

Problem Statement

Given a DataFrame with date columns and a desire to sum debits and credits by month, but encountering empty rows in between months due to missing data, how can we modify our approach to exclude these empty rows?

The Initial Approach

Creating the GroupBy Object

g = df.groupby(pd.Grouper(freq='M')).sum(['debit', 'credit'])

This initial approach creates a groupby object using the monthly frequency ('M') and then sums up the debits and credits for each month.

Examining the Results

Unexpected Empty Rows

However, upon executing this code, we observe that the resulting DataFrame includes not only the two months with data but also all the empty rows in between. This is because groupby treats each unique value as a separate group.

            debit      credit
    date                             
    2012-08-31  56513.000  395600.000
    2012-09-30          0           0
    2012-10-31          0           0
    2012-11-30          0           0
    ....
    2016-04-30          0           0
    2016-05-31  13883.000  166600.000

Solution: Truncating Dates to Months

One possible solution involves truncating the dates to months, effectively removing any empty rows between months.

Step 1: Defining a Function for Truncation

def trunc_to_month(x):
    y = x.split('-')
    return '-'.join(y[0], y[1], '1')

This function splits each date into its year, month, and day components, then rejoins them with a fixed day value ('1') to create a new string representing only the month.

Step 2: Applying Truncation to the DataFrame

df['date_month'] = df.date.apply(trunc_to_month)

By assigning this truncated date string back into df, we can then proceed with grouping by the month column instead of the original date column.

Modified GroupBy Approach

Executing the Modified Code

With our truncated date strings in place, we execute the same initial approach but now using the new date_month column:

g = df.groupby(pd.Grouper(freq='M')).sum(['debit', 'credit'])

This modified approach will yield a DataFrame containing only the debits and credits for each month, without any empty rows.

Code Example

Here is the complete code example demonstrating this process:

import pandas as pd

# Assuming df contains our date-based data
df = pd.DataFrame({
    'action': ['+', '-'],
    'shares': [13883.0, 166600.0],
    'debit': [13883.0, 10000.0],
    'credit': [0.0, 385600.0]
}, index=pd.date_range('2012-08-27', periods=7))

def trunc_to_month(x):
    y = x.split('-')
    return '-'.join(y[0], y[1], '1')

df['date_month'] = df.date.apply(trunc_to_month)
g = df.groupby(pd.Grouper(freq='M')).sum(['debit', 'credit'])
print(g)

This approach effectively filters out the empty rows and provides a cleaner summary of the monthly debits and credits.

Last modified on 2024-05-18