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

Merging tables

Join DataFrames with merge, choose the correct how and validate cardinality.

Beginner 14 min read Lesson 7 of 300 Challenge included

What you will be able to do

Use on= for matching columns.
Use how to control which rows survive.
Use validate="one_to_many" to catch bad data.
Check row counts before and after.

In plain English

merge is a SQL join for DataFrames. Choose how="inner", "left", "right" or "outer" based on which rows must survive. Validate the relationship with validate= so a surprise many-to-many join fails loudly.

merge.pyPython

import pandas as pd

courses = pd.DataFrame({'slug': ['html', 'css'], 'title': ['HTML', 'CSS']})
lessons = pd.DataFrame({'course_slug': ['html', 'css', 'css'], 'minutes': [12, 18, 22]})

merged = lessons.merge(courses, left_on='course_slug', right_on='slug', how='left', validate='many_to_one')
print(merged.groupby('title')['minutes'].sum())

Your turn

Left merge

Medium

Merge lessons with courses on course_slug/slug keeping every lesson.

PYTHON · starter

lessons.merge(courses, left_on='course_slug', right_on='slug')

Check your understanding before moving on.

Knowledge checkQuestion 1

Which how value keeps every row from the left DataFrame?

Select an answer to continue

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