Python data science handbook : essential tools for working with data
Author(s)
Bibliographic Information
Python data science handbook : essential tools for working with data
O'Reilly Media, 2016
- : pbk
Available at / 19 libraries
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Institute for Space–Earth Environmental Research, Nagoya University宇宙地球研1
: pbk007.64||V||||宇1電磁気141683799
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Note
Includes index
"2016-11-17: First Release"--T.p. verso
Copyright 2017
Description and Table of Contents
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
by "Nielsen BookData"