Grouping by multiple columns in Pandas

Introduction

Grouping by multiple columns helps you organize data into smaller parts based on several categories at once. This makes it easier to analyze patterns and summaries for each group.

You want to find the total sales for each product in each store.

You need to calculate the average score for students by both class and subject.

You want to count how many customers bought each product in different regions.

You want to see the maximum temperature recorded for each city by month.

Syntax

Pandas

df.groupby(['column1', 'column2', ...])['target_column'].aggregation_function()

You list the columns to group by inside a list.

After grouping, you choose which column to aggregate and what function to use, like sum(), mean(), or count().

Examples

Sum sales for each city and month combination.

Pandas

df.groupby(['City', 'Month'])['Sales'].sum()

Calculate average score for each class and subject.

Pandas

df.groupby(['Class', 'Subject'])['Score'].mean()

Count how many times each product was sold in each store.

Pandas

df.groupby(['Store', 'Product'])['Quantity'].count()

Sample Program

This code groups sales data by both store and product. Then it adds up the sales numbers for each group. The result shows total sales for each product in each store.

Pandas

import pandas as pd

data = {
    'Store': ['A', 'A', 'B', 'B', 'A', 'B'],
    'Product': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple', 'Banana'],
    'Sales': [10, 15, 5, 20, 7, 8]
}

df = pd.DataFrame(data)

# Group by Store and Product, then sum the Sales
result = df.groupby(['Store', 'Product'])['Sales'].sum()

print(result)

Important Notes

Grouping by multiple columns creates a multi-level index in the result.

You can reset the index with reset_index() if you want a flat table.

Aggregation functions can be sum(), mean(), count(), max(), min(), and more.

Summary

Grouping by multiple columns helps analyze data by several categories at once.

Use a list of column names inside groupby() to group by more than one column.

Apply aggregation functions to get summaries like sums or averages for each group.