Titel: Practical Python Data Wrangling and Data Quality: Getting Started with Reading, Cleaning, and Analyzing Data (eng)
Verlag: O'Reilly Media
Erscheinungsjahr: 2022
Sprache: Englisch
ISBN-10: 1492091502
ISBN-13: 9781492091509
Einband: Softcover
Zustand: new

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The world around us is full of data that holds unique insights and valuable stories, and this book will help you uncover them. Whether you already work with data or want to learn more about its possibilities, the examples and techniques in this practical book will help you more easily clean, evaluate, and analyze data so that you can generate meaningful insights and compelling visualizations.

Complementing foundational concepts with expert advice, author Susan E. McGregor provides the resources you need to extract, evaluate, and analyze a wide variety of data sources and formats, along with the tools to communicate your findings effectively. This book delivers a methodical, jargon-free way for data practitioners at any level, from true novices to seasoned professionals, to harness the power of data.

Use Python 3.8+ to read, write, and transform data from a variety of sources
Understand and use programming basics in Python to wrangle data at scale
Organize, document, and structure your code using best practices
Collect data from structured data files, web pages, and APIs
Perform basic statistical analyses to make meaning from datasets
Visualize and present data in clear and compelling ways

�ber die Autorin bzw. den Autor

Susan McGregor is the Assistant Director of the Tow Center for Digital Journalism, and has been teaching journalists and other non-programmers to code for more than a decade. With a background in computer science, journalism and information visualization, McGregor loves solving problems that help people achieve greater agency. Following several years as the Senior Programmer of the Online News Graphics team at The Wall Street Journal, McGregor spent nearly a decade at Columbia University, where she taught classes on everything from introductory data journalism to advanced algorithmic investigation and analysis.

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