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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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Visualizing data using Plotly

Visualizing data is essential in data analysis workflows because it simplifies complex information and highlights patterns while also improving communication and aiding in quality assessment. Additionally, data visualizations enable the detection of trends, anomalies, and relationships in data, serving as a foundational tool for exploratory data analysis.

There are many libraries available in Python to let you create visualizations, including, but not limited to, Matplotlib, Seaborn, Plotly, and Altair. Note that not all the data visualization libraries have built-in compatibility with Polars DataFrames.

In this recipe, we’ll explore the data by visualizing data using the plotly library. It is already compatible with Polars DataFrames.

Getting ready

You need to install plotly for this recipe. Use the following command to install it with pip:

>>> pip install plotly

You’ll also need the nbformat library to render Plotly...

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Polars Cookbook
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