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Mastering Numerical Computing with NumPy

You're reading from   Mastering Numerical Computing with NumPy Master scientific computing and perform complex operations with ease

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788993357
Length 248 pages
Edition 1st Edition
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Authors (3):
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Tiago Antao Tiago Antao
Author Profile Icon Tiago Antao
Tiago Antao
Mert Cuhadaroglu Mert Cuhadaroglu
Author Profile Icon Mert Cuhadaroglu
Mert Cuhadaroglu
Umit Mert Cakmak Umit Mert Cakmak
Author Profile Icon Umit Mert Cakmak
Umit Mert Cakmak
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Table of Contents (11) Chapters Close

Preface 1. Working with NumPy Arrays FREE CHAPTER 2. Linear Algebra with NumPy 3. Exploratory Data Analysis of Boston Housing Data with NumPy Statistics 4. Predicting Housing Prices Using Linear Regression 5. Clustering Clients of a Wholesale Distributor Using NumPy 6. NumPy, SciPy, Pandas, and Scikit-Learn 7. Advanced Numpy 8. Overview of High-Performance Numerical Computing Libraries 9. Performance Benchmarks 10. Other Books You May Enjoy

NumPy and SciPy

Until now, you have seen numerous examples of NumPy usage and only a few of SciPy. NumPy has array data type, which allows you to perform various array operations, such as sorting and reshaping.

NumPy has some numerical algorithms that can be used for tasks such as calculating norms, eigenvalues, and eigenvectors. However, if numerical algorithms are your focus, you should ideally use SciPy, as it includes a more comprehensive algorithm set, as well as the latest versions of the algorithms. SciPy has a lot of useful subpackages for certain kinds of analysis.

The following list will give you an overall idea of the subpackages:

  • Cluster: This subpackage includes clustering algorithms. It has two submodules, vq and hierarchy. The vq module provides functions for k-means clustering. The hierarchy module includes functions for hierarchical clustering.
  • Fftpack: This...
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