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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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Time Series Analysis

Time series analysis is one of the most common and impactful things you can do with your time series data. So far in this book, we’ve covered many techniques in Python Polars in regards to transforming, manipulating, and analyzing data. In this chapter, you will continue to learn about what Python Polars is capable of. This chapter teaches you how to work with date and time columns. You will also learn to identify trends and seasonality in your data using various methods. You will build calculations on time series data, including a time series forecasting model to predict future values. Time series analysis provides you and your organization with insights and meaningful statistics from data. This chapter delves into how we can work with time series data and conduct analysis on it, leveraging the capability of Python Polars.

In this chapter, we’re going to cover the following main topics:

  • Working with date and time
  • Applying rolling windows...
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Polars Cookbook
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