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Polars Cookbook

You're reading from   Polars Cookbook Over 60 practical recipes to transform, manipulate, and analyze your data using Python Polars 1.x

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781805121152
Length 394 pages
Edition 1st Edition
Languages
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Author (1):
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Yuki Kakegawa Yuki Kakegawa
Author Profile Icon Yuki Kakegawa
Yuki Kakegawa
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Toc

Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Getting Started with Python Polars FREE CHAPTER 2. Chapter 2: Reading and Writing Files 3. Chapter 3: An Introduction to Data Analysis in Python Polars 4. Chapter 4: Data Transformation Techniques 5. Chapter 5: Handling Missing Data 6. Chapter 6: Performing String Manipulations 7. Chapter 7: Working with Nested Data Structures 8. Chapter 8: Reshaping and Tidying Data 9. Chapter 9: Time Series Analysis 10. Chapter 10: Interoperability with Other Python Libraries 11. Chapter 11: Working with Common Cloud Data Sources 12. Chapter 12: Testing and Debugging in Polars 13. Index 14. Other Books You May Enjoy

Technical requirements

You can download the dataset and code from the GitHub repository:

It is assumed that you have installed the Polars library in your Python environment:

>>> pip install polars

And that you imported it in your code:

import polars as pl

We’ll be using the international student demographic dataset in this chapter. There are a few CSV files in the dataset we’ll be using, but academic.csv will be used throughout the chapter. Read it into a DataFrame with the following code:

df = pl.read_csv('../data/academic.csv')

Clean the data by renaming a column, casting data types, and keeping only the recent years of data:

from polars import selectors as cs
df = (
    df
    .select(
      ...
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