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Pandas Cookbook

You're reading from   Pandas Cookbook Practical recipes for scientific computing, time series, and exploratory data analysis using Python

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781836205876
Length 404 pages
Edition 3rd Edition
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Authors (2):
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William Ayd William Ayd
Author Profile Icon William Ayd
William Ayd
Matthew Harrison Matthew Harrison
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Matthew Harrison
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Table of Contents (13) Chapters Close

Preface 1. pandas Foundations FREE CHAPTER 2. Selection and Assignment 3. Data Types 4. The pandas I/O System 5. Algorithms and How to Apply Them 6. Visualization 7. Reshaping DataFrames 8. Group By 9. Temporal Data Types and Algorithms 10. General Usage and Performance Tips 11. The pandas Ecosystem 12. Index

Comparing the best hitter in baseball across years

In the Finding the baseball players best at… recipe back in Chapter 5, Algorithms and How to Apply Them, we worked with a dataset that had already aggregated the performance of players from the years 2020-2023. However, comparing players based on their performance across multiple years is rather difficult. Even on a year-to-year basis, statistics that appear elite one year can be considered just “very good” in other years. The reasons for the variation in statistics across years can be debated, but likely come down to some combination of strategy, equipment, weather, and just pure statistical chance.

For this recipe, we are going to work with a more granular dataset that goes down to the game level. From there, we are going to aggregate the data up to a yearly summary, and from there calculate a common baseball statistic known as the batting average.

For those unfamiliar, a batting average is calculated...

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