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  • Book Overview & Buying Polars Cookbook
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Polars Cookbook

Polars Cookbook

By : Yuki Kakegawa
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Polars Cookbook

Polars Cookbook

5 (5)
By: Yuki Kakegawa

Overview of this book

The Polars Cookbook is a comprehensive, hands-on guide to Python Polars, one of the first resources dedicated to this powerful data processing library. Written by Yuki Kakegawa, a seasoned data analytics consultant who has worked with industry leaders like Microsoft and Stanford Health Care, this book offers targeted, real-world solutions to data processing, manipulation, and analysis challenges. The book also includes a foreword by Marco Gorelli, a core contributor to Polars, ensuring expert insights into Polars' applications. From installation to advanced data operations, you’ll be guided through data manipulation, advanced querying, and performance optimization techniques. You’ll learn to work with large datasets, conduct sophisticated transformations, leverage powerful features like chaining, and understand its caveats. This book also shows you how to integrate Polars with other Python libraries such as pandas, numpy, and PyArrow, and explore deployment strategies for both on-premises and cloud environments like AWS, BigQuery, GCS, Snowflake, and S3. With use cases spanning data engineering, time series analysis, statistical analysis, and machine learning, Polars Cookbook provides essential techniques for optimizing and securing your workflows. By the end of this book, you'll possess the skills to design scalable, efficient, and reliable data processing solutions with Polars.
Table of Contents (15 chapters)
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Filtering strings

One of the most common tasks when working with strings is to filter strings. By capturing patterns found in strings, you can filter records in your DataFrame. This allows you to apply business logic on strings to keep only what you need or discard what you don’t need for your analysis.

In this recipe, we’ll cover how to filter strings using string methods such as .str.starts_with(), .str.ends_with(), and .str.contains().

How to do it...

Here are five ways in which you can filter strings:

  • Filter a string based on the characters that it starts with. This is an example of a case-sensitive substring match:
    (
        df
        .filter(pl.col('content').str.starts_with('Very'))
        .select('content')
        .head()
    )

    The preceding code will return the following output:

Figure 6.1 – The first five rows where the content starts with Very

Figure 6.1 – The first five rows where...

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Polars Cookbook
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