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Python data science handbook : essential tools for working with data / Jake VanderPlas.

By: Publisher: Sebastopol, CA : O'Reilly Media, Inc., [2016]Copyright date: 2017Edition: First editionDescription: xvi, 529 pages : illustrations, maps ; 24 cmContent type:
  • text
  • still image
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781491912058
  • 1491912057
Subject(s): DDC classification:
  • 006.312 23
LOC classification:
  • QA76.73.P98 V365 2016
Contents:
IPython: beyond normal Python -- Introduction to NumPy -- Data manipulation with Pandas -- Visualization with Matplotlib -- Machine learning.
Summary: 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.-- Provided by Publisher.
Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
BOOK BOOK NCAR Library Foothills Lab QA76.73 .P98 .V365 2016 1 Checked out 07/01/2024 50583020010074
Total holds: 0

Includes index.

IPython: beyond normal Python -- Introduction to NumPy -- Data manipulation with Pandas -- Visualization with Matplotlib -- Machine learning.

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.-- Provided by Publisher.

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