Polars Cookbook - Yuki Kakegawa

Polars Cookbook

Over 60 practical recipes to transform, manipulate, and analyze your data using Python Polars 1.x

(Autor)

Buch | Softcover
394 Seiten
2024
Packt Publishing Limited (Verlag)
978-1-80512-115-2 (ISBN)
47,35 inkl. MwSt
Leverage a lightning fast DataFrame library for efficient data wrangling in Python

Key Features

Unlock the power of Python Polars for faster and more efficient data analysis workflows
Master the fundamentals of Python Polars with step-by-step recipes
Discover data manipulation techniques to apply across multiple data problems
Purchase of the print or Kindle book includes a free PDF eBook

Book DescriptionPolars Cookbook is a complete guide that not only helps you get started with Python Polars but also gives you effective solutions to your day-to-day data problems. Dive into the world of Polars, a high-performance DataFrame library designed for efficient data processing and analysis. This cookbook takes a practical approach to unlocking the full potential of Polars through detailed, step-by-step recipes.
Starting with installation and basic operations, this book guides you through data manipulation, advanced querying, and performance optimization techniques. You’ll learn how to handle large datasets, perform complex transformations, and leverage Polars’ powerful features for data science tasks. As you progress, you’ll explore Polars’ integration with other tools and libraries, and discover how to deploy Polars in both onpremises and cloud environments. You’ll also explore use cases for data engineering, time series analysis, statistical analysis, and machine learning, providing essential strategies for securing and optimizing your Polars workflows.
By the end of this book, you’ll have acquired the knowledge and skills to build scalable, efficient, and reliable data processing solutions using Polars.What you will learn

Read from different data sources and write to various files and databases
Apply aggregations, window functions, and string manipulations
Perform common data tasks such as handling missing values and performing list and array operations
Discover how to reshape and tidy your data by pivoting, joining, and concatenating
Analyze your time series data in Python Polars
Create better workflows with testing and debugging

Who this book is forThis book is for data analysts, data scientists, and data engineers who want to learn how to use Polars in their workflows. Working knowledge of the Python programming language is required. Experience working with a DataFrame library such as pandas or PySpark will also be helpful.

Yuki Kakegawa is a data analytics professional with a background in computer science. Yuki has worked in the data space for the past several years, most of which has been spent in consulting, focusing on data engineering, analytics, and business intelligence. His clients are from various industries, such as healthcare, education, insurance, and private equity. He has worked with various companies, including Microsoft and Stanford Health Care, to name a couple. He also runs Orem Data, a data analytics consultancy that helps companies improve their existing data and analytics infrastructure. Aside from work, Yuki enjoys playing baseball and softball with his wife and friends.

Table of Contents

Getting Started with Python Polars
Reading and Writing Files
An Introduction to Data Analysis in Python Polars
Data Transformation Techniques
Handling Missing Data
Performing String Manipulations
Working with Nested Data Structures
Reshaping and Tidying data
Time Series Analysis
Interoperability with Other Python Libraries
Working with Common Cloud Data Sources
Testing and Debugging in Polars

Erscheinungsdatum
Vorwort Marco Gorelli
Verlagsort Birmingham
Sprache englisch
Maße 191 x 235 mm
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Informatik Software Entwicklung User Interfaces (HCI)
Mathematik / Informatik Informatik Theorie / Studium
ISBN-10 1-80512-115-4 / 1805121154
ISBN-13 978-1-80512-115-2 / 9781805121152
Zustand Neuware
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