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