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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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Using SQL for data transformations

SQL is an essential tool in data analysis and transformations. Many data pipelines you see at workplaces are written in SQL and most data professionals are accustomed to using SQL for analytics work.

The good news is that you can use SQL in Python Polars as well. This opens the door for those who might not be as familiar with a DataFrame library. In this recipe, we’ll cover how to configure Polars to use SQL and how you can implement simple SQL queries such as aggregations.

Getting ready

We’ll use the Contoso dataset for this recipe as well. Run the following code to read the dataset:

df = pl.read_csv('../data/contoso_sales.csv', try_parse_dates=True)

How to do it…

Here’s how to use SQL in Polars:

  1. Define the SQL context and register your DataFrame:
    ctx = pl.SQLContext(eager=True)
    ctx.register('df', df)
  2. Create a simple query and execute it:
    ctx.execute(
        ...
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Polars Cookbook
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