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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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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

Pickle

The pickle format is Python’s built-in serialization format. Pickle files typically end with a .pkl extension.

Unlike other formats encountered so far, the pickle format should not be used to transfer data across machines. The main use case is for saving pandas objects that themselves contain Python objects to your own machine, returning to them at a later point in time. If you are unsure if you should be using this format or not, I would advise trying the Apache Parquet format first, which covers a wider array of use cases.

Do not load pickle files from untrusted sources. I would generally only advise using pickle for your own analyses; do not share data or expect to receive data from others in the pickle format.

How to do it

To highlight that the pickle format should really only be used when your pandas objects contain Python objects, let’s imagine we decided to store our Beatles data as a pd.Series of namedtuple types. It is a fair question...

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