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Joining Data with pandas

70 reviews

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

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4 Hours15 Videos51 Exercises105,046 Learners4050 XPData Analyst with Python TrackData Manipulation with Python TrackData Scientist with Python TrackData Scientist Professional with Python Track

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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.
  1. 1

    Data Merging Basics


    Learn how you can merge disparate data using inner joins. By combining information from multiple sources you’ll uncover compelling insights that may have previously been hidden. You’ll also learn how the relationship between those sources, such as one-to-one or one-to-many, can affect your result.

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    Inner join
    50 xp
    What column to merge on?
    50 xp
    Your first inner join
    100 xp
    Inner joins and number of rows returned
    100 xp
    One-to-many relationships
    50 xp
    One-to-many classification
    100 xp
    One-to-many merge
    100 xp
    Merging multiple DataFrames
    50 xp
    Total riders in a month
    100 xp
    Three table merge
    100 xp
    One-to-many merge with multiple tables
    100 xp
  2. 2

    Merging Tables With Different Join Types

    Take your knowledge of joins to the next level. In this chapter, you’ll work with TMDb movie data as you learn about left, right, and outer joins. You’ll also discover how to merge a table to itself and merge on a DataFrame index.

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  3. 3

    Advanced Merging and Concatenating

    In this chapter, you’ll leverage powerful filtering techniques, including semi-joins and anti-joins. You’ll also learn how to glue DataFrames by vertically combining and using the pandas.concat function to create new datasets. Finally, because data is rarely clean, you’ll also learn how to validate your newly combined data structures.

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In the following tracks

Data Analyst with PythonData Manipulation with PythonData Scientist with PythonData Scientist Professional with Python


Amy Peterson
Maggie Matsui
Aaren Stubberfield Headshot

Aaren Stubberfield

Manager, Supply Chain Analytics @ Ingredion Incorporated

Manager of Supply Chain Analytics, with over 7 years of experience analyzing data to find insight for business related questions. I am responsible Supply Chain related Analytics for the NA business for $5.8 billion ingredient solutions provider to the food, beverage, brewing and pharmaceutical sectors. I graduated from DePaul University with distinction and received a MS in Predictive Analytics. I am passionate about Data Science / Machine Learning and I continue to work on my craft by learning new concepts through online classes.
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  • Alaeddine B.
    5 days

    Very good detailed course

  • K K.
    8 days

    I have learnt many useful techniques during this course.

  • Louis O.
    10 days

    Great topic

  • Thomas S.
    10 days

    Clear. Informative. Excellent learning environment.

  • Rodrigo A.
    18 days

    It is a good course

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"Very good detailed course"

Alaeddine B.

"I have learnt many useful techniques during this course."

K K.

"Great topic"

Louis O.

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