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Updated for Python 3.10 and pandas 1 .4, the third edition of this handson guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, and Jupyterin the process.  \nWritten by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.  \n• Use the Jupyter notebook and the IPython shell for exploratory computing  \n• Learn basic and advanced features in NumPy  \n• Get started with data analysis tools in the pandas library  \n• Use flexible tools to load, clean, transform, merge, andreshape data  \n• Create informative visualizations with matplotlib  \n• Apply the pandas groupBy facility to slice, dice, and summarize datasets  \n• Analyze and manipulate regular and irregular time series data  \n• Learn how to solve real-world data analysis problems with thorough, detailed examples  \n“With this new edition, Wes has updated his book to ensure it remains the go-to resource for all things related to data analysis with Python and pandas. I cannot recommend this book highly enough.”  \n—Paul Barry  \nLecturer and author of O’Reilly’s HeadFirst Python  \nWes McKinney, cofounder and chief technology officer of Voltron Data, isan active member of the Python data community and an advocate for Python use in data analysis, finance, and statistical computing applications. A graduate of MIT, he’s also a member of the project management committees for the Apache Software Foundation’s Apache Arrow and Apache Parquet projects.  \nDATA  \nUS $69.99 CAN $87.99  \nISBN: 978-1-098-10403-0  \n9  \n781098 104030  \n5 6 9 9 9  \nTwitter: @oreillymedia [linkedin.com/company/oreilly-media](linkedin.com/company/oreilly-media)[ ](linkedin.com/company/oreilly-media)[youtube.com/oreillymedia](youtube.com/oreillymedia)  \nTHIRD EDITION  \nPython for Data Analysis Data Wrangling with pandas, NumPy, and Jupyter  \nWes McKinney  \nBeijing  Boston  Farnham  Sebastopol  Tokyo   \nPython for Data Analysis  \nby Wes McKinney  \nCopyright © 2022 Wesley McKinney. All rights reserved.  \nPrinted in the United States of America.  \nPublished by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.  \nO’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles ([http://oreilly.com](http://oreilly.com). For more information, contact our corporate/institutional)[). For more information, contact our corporate/institutional](http://oreilly.com). For more information, contact our corporate/institutional)  \nsales department: 800-998-9938 or [corporate@oreilly.com](corporate@oreilly.com).  \nAcquisitions Editor: Jessica Haberman  \nDevelopment Editor: Angela Rufino  \nProduction Editor: Christopher Faucher  \nCopyeditor: Sonia Saruba  \nProofreader: Piper Editorial Consulting, LLC  \nIndexer: Sue Klefstad  \nInterior Designer: David Futato  \nCover Designer: Karen Montgomery  \nIllustrator: Kate Dullea  \nOctober 2012: First Edition  \nOctober 2017: Second Edition  \nAugust 2022: Third Edition  \nRevision History for the Third Edition  \n2022-08-12: First Release  \nSee [https://www.oreilly.com/catalog/errata.csp?isbn=0636920519829](https://www.oreilly.com/catalog/errata.csp?isbn=0636920519829 for)[ for](https://www.oreilly.com/catalog/errata.csp?isbn=0636920519829 for) release details.  \nThe O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Python for Data Analysis, the cover image, and related trade dress are trade","cbCaibxhQOQJuqnQ","https://ap.wps.com/l/cbCaibxhQOQJuqnQ","pdf",9384476,582,"English","# Preface\n# 1. Preliminaries\n## 1.1 What Is This Book About?\n## 1.2 Why Python for Data Analysis?\n## 1.3 Essential Python Libraries\n## 1.4 Installation and Setup\n## 1.5 Community and Conferences\n## 1.6 Navigating This Book\n# 2. Python Language Basics, IPython, and Jupyter Notebooks\n## 2.1 The Python Interpreter\n## 2.2 IPython Basics\n## 2.3 Python Language Basics\n## 2.4 Conclusion\n# 3. Built-In Data Structures, Functions, and Files","[{\"question\":\"What skills will readers gain from this third edition?\",\"answer\":\"Readers learn how to manipulate, process, clean, and transform datasets in Python, using practical workflows across pandas, NumPy, and Jupyter. The edition focuses on solving real-world data analysis problems with detailed examples.\"},{\"question\":\"Which tools and environments does the book teach for exploratory computing?\",\"answer\":\"The book uses Jupyter notebooks and the IPython shell for exploratory computing. It also covers running Jupyter notebooks and working with tab completion and introspection.\"},{\"question\":\"How does the book approach data wrangling in pandas?\",\"answer\":\"It teaches flexible pandas tools to load, clean, transform, merge, and reshape data. Readers also learn visualization using matplotlib and dataset slicing, dicing, and summarizing with pandas groupBy.\"}]","Python for Data Analysis - Third Edition - A Definitive Handbook for Data Wrangling with pandas, NumPy & Jupyter | PDF",1467]