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  • Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

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Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

by Wes McKinney (Author) Format: Paperback
4.6 out of 5 stars (542)

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Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on 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 Jupyter in the process.

Written 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.

  • Use the Jupyter notebook and IPython shell for exploratory computing
  • Learn basic and advanced features in NumPy
  • Get started with data analysis tools in the pandas library
  • Use flexible tools to load, clean, transform, merge, and reshape data
  • Create informative visualizations with matplotlib
  • Apply the pandas groupby facility to slice, dice, and summarize datasets
  • Analyze and manipulate regular and irregular time series data
  • Learn how to solve real-world data analysis problems with thorough, detailed examples

Read more
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  1. ISBN-10
    109810403X
  2. ISBN-13
    978-1098104030
  3. Edition
    3rd
  4. Publisher
    O'Reilly Media
  5. Publication date
    September 20, 2022
  6. Language
    English
  7. Dimensions
    7 x 1.5 x 9 inches
  8. Print length
    579 pages
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From the Publisher

Python for Data Analysis

About this Book

What Is This Book About?

This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. My goal is to offer a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools that will equip you to become an effective data analyst. While “data analysis” is in the title of the book, the focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you need for data analysis.

Sometime after I originally published this book in 2012, people started using the term data science as an umbrella description for everything from simple descriptive statistics to more advanced statistical analysis and machine learning. The Python open source ecosystem for doing data analysis (or data science) has also expanded significantly since then. There are now many other books which focus specifically on these more advanced methodologies. My hope is that this book serves as adequate preparation to enable you to move on to a more domain-specific resource.

The first edition of this book was published in 2012, during a time when open source data analysis libraries for Python, especially pandas, were very new and developing rapidly. When the time came to write the second edition in 2016 and 2017, I needed to update the book not only for Python 3.6 (the first edition used Python 2.7) but also for the many changes in pandas that had occurred over the previous five years.

Now in 2022, there are fewer Python language changes (we are now at Python 3.10, with 3.11 coming out at the end of 2022), but pandas has continued to evolve. In this third edition, my goal is to bring the content up to date with current versions of Python, NumPy, pandas, and other projects, while also remaining relatively conservative about discussing newer Python projects that have appeared in the last few years. Since this book has become an important resource for many university courses and working professionals, I will try to avoid topics that are at risk of falling out of date within a year or two. That way paper copies won’t be too difficult to follow in 2023 or 2024 or beyond.

Editorial Reviews

About the Author

Wes McKinney is a Nashville-based software developer and entrepreneur. After finishing his undergraduate degree in mathematics at MIT in 2007, he went on to do quantitative finance work at AQR Capital Management in Greenwich, CT. Frustrated by cumbersome data analysis tools, he learned Python and started building what would later become the pandas project. He's now an active member of the Python data community and is an advocate for the use of Python in data analysis, finance, and statistical computing applications.

Wes was later the cofounder and CEO of DataPad, whose technology assets and team were acquired by Cloudera in 2014. He has since become involved in big data technology, joining the Project Management Committees for the Apache Arrow and Apache Parquet projects in the Apache Software Foundation. In 2018, he founded Ursa Labs, a not-for-profit organization focused Apache Arrow development, in partnership with RStudio and Two Sigma Investments. In 2021, he cofounded technology startup Voltron Data, where he currently works as the Chief Technology Officer.

Product details

  • Publisher ‏ : ‎ O'Reilly Media
  • Publication date ‏ : ‎ September 20, 2022
  • Edition ‏ : ‎ 3rd
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 579 pages
  • ISBN-10 ‏ : ‎ 109810403X
  • ISBN-13 ‏ : ‎ 978-1098104030
  • Item Weight ‏ : ‎ 1.95 pounds
  • Dimensions ‏ : ‎ 7 x 1.5 x 9 inches
  • Best Sellers Rank: #44,653 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.6 out of 5 stars (542)

About the author

Follow authors to get new release updates, plus improved recommendations.
Wes McKinney

Wes McKinney

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Since 2007, I have been creating fast, easy-to-use data wrangling and statistical computing tools, mostly in the Python programming language. I am best known for creating the pandas project and writing the book Python for Data Analysis. I am also a contributor to the Apache Arrow, Kudu, and Parquet projects within the Apache Software Foundation. I am currently the CTO and Co-founder of Voltron Data, which builds accelerated computing technologies powered by Apache Arrow. I previously worked for Ursa Labs (within RStudio / Posit), Two Sigma, Cloudera, DataPad, and AQR Capital Management.

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