Python Data Science Handbook: Essential Tools for Working with Data

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ISBN-13: 9781491912058
ISBN-10: 1491912057
Edition: 1
Author: Jake VanderPlas
Publication date: 2016
Publisher: O'Reilly Media
Format: Paperback 548 pages
Category: Computers
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Book details

ISBN-13: 9781491912058
ISBN-10: 1491912057
Edition: 1
Author: Jake VanderPlas
Publication date: 2016
Publisher: O'Reilly Media
Format: Paperback 548 pages
Category: Computers

Summary

Acknowledged authors Jake VanderPlas wrote Python Data Science Handbook: Essential Tools for Working with Data comprising 548 pages back in 2016. Textbook and eTextbook are published under ISBN 1491912057 and 9781491912058. Since then Python Data Science Handbook: Essential Tools for Working with Data textbook received total rating of 4 stars and was available to sell back to BooksRun online for the top buyback price of $ 24.80 or rent at the marketplace.

Description

For many researchers, Python is a first-class tool mainly 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 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 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 to use:

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