Polars Cookbook - Kakegawa, Yuki

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Résumé :
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 Description: Polars 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 for: This 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. 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...

Biographie:
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....

Sommaire:
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 Description: Polars 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 for: This 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. 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...