What you'll learn
In this project you’ll follow along with our instructions to build data visualizations that present data on Major League Baseball All-star games.
Table of contents
Setup
15m
- Set up your local environment for projects. We'll walk you through everything you need to know, including how to install and configure your environment to be able to complete all of the tasks.
Game Files - Clean and Import Data
20m
- The data we will work with in this project is stored in several CSV files. In this module we will clean and import the data using the Pandas library.
Attendance - Select and Plot Data
10m
- In this module, we will answer the question: "How has All-star game attendance changed over time?"
Pitching - Group Data
10m
- In this module, we will answer the question: "How have the number of strike outs changed over time?"
Offense - Reshape with Pivot
15m
- In this module, we will use the `pivot()` function to show the distribution of hit types across innings.
Defensive Efficiency Ratio - Merge Data
15m
- Defensive Efficiency Ratio is used as a metric to gauge team defense. In this module we will calculate this for each league over time.
About the author
Tom is a staff author at Pluralsight helping to develop Hands-On content. Tom's background in software development, UI/UX, and instructional design was developed over the years while working as a faculty member at the School of Computing at Weber State University in Utah, and continues to grow as he develops Projects and Labs for Pluralsight. When he's not creating content to allow learners to gain real-life experience, he enjoys spending time with his family.
