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Python Data Science Handbook Essential Tools For Working With Data 2nd Edition 2nd Edition Jake Vanderplas

  • SKU: BELL-47710390
Python Data Science Handbook Essential Tools For Working With Data 2nd Edition 2nd Edition Jake Vanderplas
$ 31.00 $ 45.00 (-31%)

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Python Data Science Handbook Essential Tools For Working With Data 2nd Edition 2nd Edition Jake Vanderplas instant download after payment.

Publisher: O'Reilly Media
File Extension: PDF
File size: 19.7 MB
Pages: 591
Author: Jake VanderPlas
ISBN: 9781098121228, 1098121228
Language: English
Year: 2022
Edition: 2

Product desciption

Python Data Science Handbook Essential Tools For Working With Data 2nd Edition 2nd Edition Jake Vanderplas by Jake Vanderplas 9781098121228, 1098121228 instant download after payment.

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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