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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 multiple files

When working on actual data projects, there are cases where data is split into multiple files in a directory. Dealing with each file one by one can be a pain and may distract you from working on other critical components of your project.

In this recipe, we’ll cover reading multiple files into a single DataFrame or into multiple DataFrames, as well as writing a DataFrame to multiple files.

How to do it...

Here are some ways to work with multiple files:

  1. Write a DataFrame to multiple CSV files:
    1. Create a DataFrame:
    data = {'Letter': ['A','B','C'], 'Value': [1,2,3]}
    df = pl.DataFrame(data)
    1. Split it into multiple DataFrames:
    dfs = df.group_by(['Letter'])
    print(dfs)

    The preceding code will return the following output:

    >> <polars.dataframe.group_by.GroupBy object at 0x154373390>
    1. Write them to CSV files:
    for name, df in dfs:
        df.write_csv(f'...
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