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Course

Joining Data with pandas

IntermediateSkill Level

4.8+

Updated 05/2025

Learn to combine data from multiple tables by joining data together using pandas.

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PythonData Manipulation4 hours15 videos51 Exercises4,050 XP182,691Statement of Accomplishment

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Course Description

Being able to combine and work with multiple datasets is an essential skill for any aspiring Data Scientist. pandas is a crucial cornerstone of the Python data science ecosystem, with Stack Overflow recording 5 million views for pandas questions. Learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using pandas. You'll work with datasets from the World Bank and the City Of Chicago. You will finish the course with a solid skillset for data-joining in pandas.

Prerequisites

Data Manipulation with pandas

1

Data Merging Basics

What column to merge on?

Your first inner join

Inner joins and number of rows returned

One-to-many relationships

One-to-many classification

One-to-many merge

Merging multiple DataFrames

Total riders in a month

Three table merge

One-to-many merge with multiple tables

2

Merging Tables With Different Join Types

Counting missing rows with left join

Enriching a dataset

How many rows with a left join?

Other joins

Right join to find unique movies

Popular genres with right join

Using outer join to select actors

Merging a table to itself

How does pandas handle self joins?

Merging on indexes

Index merge for movie ratings

Do sequels earn more?

3

Advanced Merging and Concatenating

Filtering joins

Steps of a semi join

Performing an anti join

Performing a semi join

Concatenate DataFrames together vertically

Concatenation basics

Concatenating with keys

Verifying integrity

Validating a merge

Concatenate and merge to find common songs

4

Merging Ordered and Time-Series Data

Using merge_ordered()

Correlation between GDP and S&P500

Phillips curve using merge_ordered()

merge_ordered() caution, multiple columns

Using merge_asof()

Using merge_asof() to study stocks

Using merge_asof() to create dataset

merge_asof() and merge_ordered() differences

Selecting data with .query()

Explore financials with .query()

Subsetting rows with .query()

Reshaping data with .melt()

Select the right .melt() arguments

Using .melt() to reshape government data

Using .melt() for stocks vs bond performance

Course wrap-up

Joining Data with pandas

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*4.8

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Nicolás

about 6 hours

Prateek

about 6 hours

Emily

about 9 hours

Ivette

about 9 hours

easy at first, very hard later

Gianluca

about 10 hours

ok

Hamdan

about 4 hours

Nicolás

Prateek

Emily

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