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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

Product type Paperback

Published in Oct 2024

Publisher Packt

ISBN-13 9781836205876

Length 404 pages

Edition 3rd Edition

Languages

Python

Tools

Pandas

Concepts

Data Analysis

Authors (2):

William Ayd

Matthew Harrison

View More author details

Table of Contents (14) Chapters

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. Other Books You May Enjoy

13. Index

Reshaping with pd.wide_to_long

So far, we have encountered two very viable ways of converting data from wide to long format, whether it be through the use of the pd.DataFrame.stack method, introduced in our Reshaping with pd.DataFrame.stack and pd.DataFrame.unstack recipe, or through the use of the pd.DataFrame.melt, as we saw in the Reshaping with pd.DataFrame.melt recipe.

If those aren’t enough, pandas offers the pd.wide_to_long function, which can help with that conversion given that your columns follow a particular naming pattern, as we will see in this recipe.

How to do it

Let’s assume we have the following pd.DataFrame, where we have one id variable of widget and four columns representing sales from a business quarter. Each column of sales begins with "quarter_":

df = pd.DataFrame([
    ["Widget 1", 1, 2, 4, 8],
    ["Widget 2", 16, 32, 64, 128],
], columns=["widget", "quarter_1", "quarter_2...

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Authors (2)

William Ayd

William Ayd

Will Ayd is a core maintainer of the pandas project, serving in that role since 2018. For over a decade working as a consultant, Will has helped countless clients get the most value from their data using pandas and the open-source ecosystem surrounding it

See other products by William Ayd

Matthew Harrison

Matthew Harrison

Matt Harrison has been using Python since 2000. He runs MetaSnake, which provides corporate training for Python and Data Science. He is the author of Machine Learning Pocket Reference, the bestselling Illustrated Guide to Python 3, and Learning the Pandas Library, among other books

See other products by Matthew Harrison

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