Python Data Science Handbook: Essential Tools for Working with Data (Jake VanderPlas)

 
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Python Data Science Handbook: Essential Tools for Working with Data (Jake VanderPlas)

Python's libraries for data storage, manipulation, and insight extraction make it a top tool for many academics. Although there are other resources for each component of this data science stack, only the Python Data Science Handbook includes IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other relevant tools.

This thorough desk reference will prove helpful for working scientists and data crunchers who are familiar with reading and writing Python code. It addresses issues like manipulating, transforming, and cleaning data, visualizing various types of data, and using data to create statistical or machine, learning models. Simply put, this is the essential reference for Python scientific computing.

  • IPython and Jupyter: provide computational environments for data scientists using Python
  • NumPy: includes the array 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
  • Use statistical inference to answer questions about real-world data

Ebook Details

About the Authors
The Python scientific stack has been used and developed for a very long period by Jake VanderPlas. He currently works at the University of Washington as an interdisciplinary research director, pursues his own astronomical research, and spends time counseling and consulting with regional scientists working in a variety of disciplines.
Published
Published Date / Year
1 edition (December 10, 2016); eBook (2018. Updated continuously)
License(s)
Text Content: CC-BY-NC-ND; Source Code: MIT License
Hardcover
548 pages
eBook Format
HTML and PDF
ISBN-10
1491912057
ISBN-13
978-1491912058

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