Python Data Science Handbook

By (author)Jake VanderPlas
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Who Is This Book For?

In my teaching both at the University of Washington and at various tech-focused conferences and meetups, one of the most common questions I have heard is this: “How should I learn Python?” The people asking are generally technically minded students, developers, or researchers, often with an already strong background in writing code and using computational and numerical tools. Most of these folks don’t want to learn Python per se, but want to learn the language with the aim of using it as a tool for data-intensive and computational science. While a large patchwork of videos, blog posts, and tutorials for this audience is available online, I’ve long been frustrated by the lack of a single good answer to this question; that is what inspired this book.


Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all–IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you’ll learn how:

  • IPython and Jupyter provide computational environments for scientists using Python
  • NumPy includes the ndarray for efficient storage and manipulation of dense data arrays
  • Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data
  • Matplotlib includes capabilities for a flexible range of data visualizations
  • Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms






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