Single and multiple column grouping in Data Analysis Python

Introduction

Grouping helps us organize data by categories to see patterns or summaries easily.

You want to find the total sales per product category.

You need to count how many students are in each class and gender.

You want to calculate the average temperature for each city and month.

You want to summarize expenses by department and month.

Syntax

Data Analysis Python

df.groupby('column_name').agg({'column_to_aggregate': 'aggregation_function'})
df.groupby(['col1', 'col2']).agg({'column_to_aggregate': 'aggregation_function'})

Use a single column name as a string for grouping by one column.

Use a list of column names to group by multiple columns.

Examples

Groups data by 'Category' and sums all numeric columns.

Data Analysis Python

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

Groups data by 'Category' and 'Region', then calculates the average of numeric columns.

Data Analysis Python

df.groupby(['Category', 'Region']).mean()

Groups by 'Category' and applies different aggregation functions to columns.

Data Analysis Python

df.groupby('Category').agg({'Sales': 'sum', 'Quantity': 'mean'})

Sample Program

This program creates a small sales dataset. It first groups data by 'Category' and sums numeric columns. Then it groups by both 'Category' and 'Region' and calculates the average quantity.

Data Analysis Python

import pandas as pd

# Create sample data
sales_data = pd.DataFrame({
    'Category': ['Fruit', 'Fruit', 'Vegetable', 'Vegetable', 'Fruit'],
    'Region': ['North', 'South', 'North', 'South', 'North'],
    'Sales': [100, 150, 200, 130, 120],
    'Quantity': [10, 15, 20, 13, 12]
})

# Group by single column and sum sales
single_group = sales_data.groupby('Category').sum()

# Group by multiple columns and calculate mean quantity
multi_group = sales_data.groupby(['Category', 'Region']).agg({'Quantity': 'mean'})

print('Single column grouping (sum):\n', single_group)
print('\nMultiple column grouping (mean):\n', multi_group)

Important Notes

Grouping returns a new table with the grouped keys as index.

You can use many aggregation functions like sum, mean, count, min, max.

After grouping, you can reset the index if you want normal columns using reset_index().

Summary

Grouping helps summarize data by one or more columns.

Use groupby() with a column name or list of columns.

Apply aggregation functions like sum or mean to get useful summaries.