GroupBy with custom functions in Pandas

Introduction

Grouping data helps us see patterns in smaller parts. Using custom functions lets us get exactly the summary we want.

You want to find the average and also the range (max - min) of sales per store.

You need to count how many unique customers bought each product.

You want to apply a special calculation, like weighted average, on groups of data.

You want to clean or transform data differently for each group before analysis.

Syntax

Pandas

df.groupby('column').agg(custom_function)

# or with multiple columns
# df.groupby('column').agg({'col1': custom_func1, 'col2': custom_func2})

custom_function is a function you define that takes a Series and returns a single value.

You can use built-in functions or write your own for special calculations.

Examples

This example finds the range of sales for each store.

Pandas

def range_func(x):
    return x.max() - x.min()

df.groupby('Store')['Sales'].agg(range_func)

Sum sales and find average quantity sold per product using a lambda function.

Pandas

df.groupby('Product').agg({'Sales': 'sum', 'Quantity': lambda x: x.mean()})

Count unique customers per category.

Pandas

def unique_count(x):
    return x.nunique()

df.groupby('Category')['CustomerID'].agg(unique_count)

Sample Program

This program groups data by store. It calculates the sales range and average quantity sold per store.

Pandas

import pandas as pd

data = {
    'Store': ['A', 'A', 'B', 'B', 'C', 'C'],
    'Sales': [100, 150, 200, 210, 300, 310],
    'Quantity': [1, 2, 2, 3, 3, 4]
}

df = pd.DataFrame(data)

# Custom function to calculate range
def sales_range(x):
    return x.max() - x.min()

# Group by Store and apply custom function on Sales
result = df.groupby('Store').agg({'Sales': sales_range, 'Quantity': 'mean'})

print(result)

Important Notes

Custom functions must return a single value for each group.

Use lambda for quick small functions.

GroupBy with custom functions is powerful for tailored summaries.

Summary

Group data to analyze smaller parts.

Use custom functions to get exactly the summary you want.

Combine built-in and custom functions for flexible analysis.