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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
Languages
Tools
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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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Toc

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

Other DataFrame libraries

Soon after pandas was developed, it became the de facto DataFrame library in the Python space. Since then, many new DataFrame libraries have been developed in the space, which all aim to address some of the shortcomings of pandas while introducing their own novel design decisions.

Ibis

Ibis is yet another amazing analytics tool created by Wes McKinney, the creator of pandas. At a high level, Ibis is a DataFrame “frontend” that gives you one generic API through which you can query multiple “backends.”

To help understand what that means, it is worth contrasting that with the design approach of pandas. In pandas, the API or “frontend” for a group by and a sum looks like this:

df.groupby("column").agg(result="sum")

From this code snippet, the frontend of pandas defines how the query looks (i.e., for a group-by the operation you must call pd.DataFrame.groupby). Behind the scenes...

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