What is the use of groupby() in Pandas?

August 07, 2025

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The groupby() function in Pandas is used to group data based on one or more columns and then apply aggregation functions like sum(), mean(), count(), etc., to analyze and summarize data efficiently.

Syntax:

df.groupby('column_name')

You can also group by multiple columns:

df.groupby(['col1', 'col2'])

Example:

import pandas as pd

data = {

'Department': ['Sales', 'Sales', 'HR', 'HR', 'IT'],

'Employee': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],

'Salary': [50000, 60000, 45000, 47000, 70000]

}

df = pd.DataFrame(data)

grouped = df.groupby('Department')['Salary'].mean()

print(grouped)

Output:

Department

HR 46000.0

IT 70000.0

Sales 55000.0

Use Cases:

Calculate department-wise averages

Count values in each group

Apply custom aggregation with agg()

Benefits:

Helps analyze grouped data easily

Works well with large datasets

Enables pivot-like summarization

Summary:

groupby() in Pandas is essential for data analysis tasks that require grouping and summarizing data. It simplifies operations like totals, averages, and counts across categories.

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