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Pandas 1.x Cookbook

Pandas 1.x Cookbook

By : Matthew Harrison, Theodore Petrou
4.5 (28)
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Pandas 1.x Cookbook

Pandas 1.x Cookbook

4.5 (28)
By: Matthew Harrison, Theodore Petrou

Overview of this book

The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.
Table of Contents (17 chapters)
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15
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16
Index

Transforming through a weight loss bet

One method to increase motivation to lose weight is to make a bet with someone else. The scenario in this recipe will track weight loss from two individuals throughout a four-month period and determine a winner.

In this recipe, we use simulated data from two individuals to track the percentage of weight loss over four months. At the end of each month, a winner will be declared based on the individual who lost the highest percentage of body weight for that month. To track weight loss, we group our data by month and person, and then call the .transform method to find the percentage weight loss change for each week against the start of the month.

We will use the .transform method in this recipe. This method returns a new object that preserves the index of the original DataFrame but allows you to do calculations on groups of the data.

How to do it…

  1. Read in the raw weight_loss dataset, and examine the first...

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