Pandas Joins for Spreadsheet Users

Overview

Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.

Joining two or more datasets is necessary for almost any real-world analysis. You’ve done it before with spreadsheets using VLOOKUP and related functions. Can you build on this experience as you transition to the world of Python? Yes! In this course you will learn the ins and outs of bringing datasets together with pandas, Python’s gold standard for manipulating tabular data. You’ll apply pandas functions to combine data from the National Football League (NFL) framed in a familiar spreadsheet environment. Armed with these skills you will be able to harness the power of pandas and integrate larger, more complex datasets into any analysis.

Syllabus

Introduction to joining data

In this chapter, we'll build a foundation for using pandas to join data. You'll learn about the types of joins and how pandas can improve your effectiveness and productivity.

VLOOKUP-style joins

In this chapter, you'll learn how to use pandas for joining data in a way similar to using VLOOKUP formulas in a spreadsheet. You'll learn about three types of joins and then focus on the first type, one-to-one joins.

One-to-many joins

In this chapter, we'll focus on one-to-many relationships. You'll practice identifying the relationship of key columns and joining data frames by column. You'll also learn how to join two or more data frames based on their indices.

Advanced joins

In the final chapter, you'll learn advanced joining techniques to use when faced with challenging data. You'll be presented with a challenge of your own in the form of a case study that tests your skills.