groupby() basics in Pandas

Introduction

We use groupby() to split data into groups based on some criteria. This helps us analyze each group separately.

You want to find the average sales per store from a sales list.

You need to count how many students are in each class from a school dataset.

You want to sum expenses by category from your budget data.

You want to see the maximum temperature recorded each day from weather data.

Syntax

Pandas

df.groupby('column_name')

This creates groups based on unique values in the specified column.

You usually follow it with an aggregation like .sum(), .mean(), or .count().

Examples

Groups data by 'Category' and sums numeric columns in each group.

Pandas

df.groupby('Category').sum()

Groups data by 'Class' and calculates the average of numeric columns for each class.

Pandas

df.groupby('Class').mean()

Groups data by 'Store' and counts the number of rows in each group.

Pandas

df.groupby('Store').count()

Sample Program

This code creates a small table of sales by store. Then it groups the data by 'Store' and sums the sales for each store. Finally, it prints the total sales per store.

Pandas

import pandas as pd

data = {'Store': ['A', 'B', 'A', 'B', 'C'],
        'Sales': [100, 200, 150, 300, 250]}
df = pd.DataFrame(data)

grouped = df.groupby('Store').sum()
print(grouped)

Important Notes

Grouping does not change the original data unless you assign or print the result.

You can group by multiple columns by passing a list, like df.groupby(['Col1', 'Col2']).

Summary

groupby() splits data into groups based on column values.

Use aggregation functions like sum(), mean(), or count() after grouping.

This helps analyze parts of data separately and find patterns.