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Joining Data with pandas

4.3+
124 reviews
Intermediate

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

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4 Hours15 Videos51 Exercises
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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

    Free

    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

Collaborators

Collaborator's avatar
Amy Peterson
Collaborator's avatar
Maggie Matsui
Aaren Stubberfield HeadshotAaren Stubberfield

Senior Data Scientist @ Microsoft

I am a Senior Data Scientist with expertise in Machine Learning, AI, and data governance. Currently, I work for Microsoft's Digital Advertising, which has revenues of more than $10 billion in the fiscal year 2023. However, my experience is not limited to just the advertising industry. I have worked in the Supply Chain and Data Governance industries. With my vast experience, I have led numerous teams of data scientists and have been instrumental in the successful completion of many projects. My technical skills include the use of AI, like LLMs, Python, and other various tools necessary for the execution of data science projects. My passion lies in using data to gain insights and making data-driven decisions. I constantly strive to improve my skills and knowledge and am always open to learning new techniques and tools.
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Don’t just take our word for it

*4.3
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  • Anya A.
    6 months

    Excellent explanation

  • Sarinya N.
    8 months

    Engaging exercise, well explanation

  • Jaime P.
    8 months

    The courses are excellent since I can practice the code at the same time.

  • Edwin A.
    8 months

    This is a recommended course to learn about joining data with pandas in Python.

  • Suzy R.
    8 months

    The material was clearly presented with good examples. I felt able to tackle all of the exercises.

"Excellent explanation"

Anya A.

"Engaging exercise, well explanation"

Sarinya N.

"The courses are excellent since I can practice the code at the same time."

Jaime P.

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