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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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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

Reading and writing Excel files

We all know that Excel is one of the most popular data analysis tools out there. It still is the one that most of us are familiar with. Being able to work with Excel in Polars is essential for data analysts. In this recipe, we’ll go through reading and writing Excel files, as well as utilizing some of their useful parameters.

Getting ready

This recipe requires a few Python libraries on top of Polars. You can install it with the following command:

>>> pip install xlsx2csv xlsxwriter

How to do it...

We’ll cover how to read and write Excel files using the following steps:

  1. Let’s first read a CSV file into a DataFrame and write it to an Excel file:
    output_file_path = '../data/output/financial_sample_output.xlsx'
    df = pl.read_csv('../data/customer_shopping_data.csv')
    df.write_excel(
        output_file_path,
        worksheet='Output Sheet1',
    &...
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