Pandas & Data Analysis·Pandas from first Series to analysis project

GroupBy

Split data into groups, aggregate each group and combine the result.

Beginner 14 min read Lesson 6 of 300 Challenge included

What you will be able to do

Use groupby("column") to split.
Use agg with several functions.
Use named aggregation for clear output.
Reset the index when you need a flat table.

In plain English

groupby follows split-apply-combine. Choose the grouping column, choose an aggregation and optionally reset the index. Named aggregation makes the output readable.

groupby.pyPython

import pandas as pd

df = pd.DataFrame({
    'course': ['HTML', 'CSS', 'CSS', 'SQL'],
    'minutes': [12, 18, 22, 9],
})

summary = df.groupby('course').agg(
    lessons=('minutes', 'size'),
    total=('minutes', 'sum'),
    average=('minutes', 'mean'),
).reset_index().sort_values('total', ascending=False)

print(summary)

Your turn

Average minutes per course

Easy

Group by course and compute average minutes.

PYTHON · starter

df.groupby('course')

Check your understanding before moving on.

Knowledge checkQuestion 1

What are the three stages of a groupby operation?

Select an answer to continue

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Lesson 6 of 300 · 0% of course