Interactive Course

Merging DataFrames with pandas

This course is all about the act of combining, or merging, DataFrames, an essential part your Data Scientist's toolbox.

  • 4 hours
  • 14 Videos
  • 56 Exercises
  • 39,594 Participants
  • 4,650 XP

Loved by learners at thousands of top companies:

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Course Description

As a data scientist, you'll often find that the data you need is not in a single file. It may be spread across a number of text files, spreadsheets, or databases. You’ll want to be able to import the data you’re interested in as a collection of DataFrames and combine them to answer your central questions. This course is all about the act of combining—or merging—DataFrames, an essential part of any data scientist's toolbox. You'll hone your pandas skills by learning how to organize, reshape, and aggregate multiple datasets to answer your specific questions.

  1. 1

    Preparing data

    Free

    In this chapter, you'll learn about different techniques you can use to import multiple files into DataFrames. Having imported your data into individual DataFrames, you'll then learn how to share information between DataFrames using their indexes. Understanding how indexes work is essential to merging DataFrames, which you’ll learn later in the course.

  2. Merging data

    You'll learn all about merging pandas DataFrames. You'll explore different techniques for merging, and learn about left joins, right joins, inner joins, and outer joins, as well as when to use which. You'll also learn about ordered merging, which is useful when you want to merge DataFrames with columns that have natural orderings, like date-time columns.

  3. Concatenating data

    You'll learn how to perform database-style operations to combine DataFrames. In particular, you'll learn about appending and concatenating DataFrames while working with a variety of real-world datasets.

  4. Case Study - Summer Olympics

    To reinforce your new skills, you'll apply them to an in-depth case study using Olympic medal data. The analysis involves integrating your multi-DataFrame skills from this course and skills you've gained in previous pandas courses. This is a rich dataset that will allow you to fully leverage your pandas data manipulation skills.

  1. 1

    Preparing data

    Free

    In this chapter, you'll learn about different techniques you can use to import multiple files into DataFrames. Having imported your data into individual DataFrames, you'll then learn how to share information between DataFrames using their indexes. Understanding how indexes work is essential to merging DataFrames, which you’ll learn later in the course.

  2. Concatenating data

    You'll learn how to perform database-style operations to combine DataFrames. In particular, you'll learn about appending and concatenating DataFrames while working with a variety of real-world datasets.

  3. Merging data

    You'll learn all about merging pandas DataFrames. You'll explore different techniques for merging, and learn about left joins, right joins, inner joins, and outer joins, as well as when to use which. You'll also learn about ordered merging, which is useful when you want to merge DataFrames with columns that have natural orderings, like date-time columns.

  4. Case Study - Summer Olympics

    To reinforce your new skills, you'll apply them to an in-depth case study using Olympic medal data. The analysis involves integrating your multi-DataFrame skills from this course and skills you've gained in previous pandas courses. This is a rich dataset that will allow you to fully leverage your pandas data manipulation skills.

What do other learners have to say?

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Lloyd's Banking Group

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Harvard Business School

Ronbowers

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Ronald Bowers

Decision Science Analytics @ USAA

Team Anaconda
Team Anaconda

Data Science Training

This course was created in collaboration with Anaconda. With over 6 million users, the open source Anaconda Distribution is the fastest and easiest way to do Python data science and machine learning. It's the industry standard for developing, testing, and training on a single machine.

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