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Volker Bernhard Duetsch has completed

Merging DataFrames with pandas

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4 hours
4,650 XP
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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.
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  1. 1

    Preparing data


    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.

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    Reading multiple data files
    50 xp
    Reading DataFrames from multiple files
    100 xp
    Reading DataFrames from multiple files in a loop
    100 xp
    Combining DataFrames from multiple data files
    100 xp
    Reindexing DataFrames
    50 xp
    Sorting DataFrame with the Index & columns
    100 xp
    Reindexing DataFrame from a list
    100 xp
    Reindexing using another DataFrame Index
    100 xp
    Arithmetic with Series & DataFrames
    50 xp
    Adding unaligned DataFrames
    50 xp
    Broadcasting in arithmetic formulas
    100 xp
    Computing percentage growth of GDP
    100 xp
    Converting currency of stocks
    100 xp
  2. 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.

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  3. 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.

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Baby namesSummer Olympic medalsAutomobile fuel efficiencyExchange ratesGDPOil pricesPittsburgh weather dataSalesS&P 500 Index
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