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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

What this book covers

Chapter 1, Working with Numpy Arrays, explains the basics of numerical computing with NumPy, which is a Python library for working with multi-dimensional arrays and matrices used by scientific computing applications.

Chapter 2, Linear Algebra with Numpy, covers the basics of linear algebra and provides practical NumPy examples.

Chapter 3, Exploratory Data Analysis of Boston Housing Data with NumPy Statistics, explains exploratory data analysis and provides examples using Boston Housing Dataset.

Chapter 4, Predicting Housing Prices Using Linear Regression, covers supervised learning and provides a practical example for predicting housing prices using linear regression.

Chapter 5, Clustering Clients of a Wholesale Distributor Using NumPy, explains unsupervised learning and provides a practical example of a clustering algorithm to model a wholesale distributor sales dataset, which contains information on annual spending in monetary units for diverse product categories.

Chapter 6, NumPy, SciPy, Pandas, and Scikit-Learn, shows the relationship between NumPy and other libraries and provides examples of how they are used together.

Chapter 7, Advanced Numpy, explains the advanced considerations of NumPy library usage.

Chapter 8, Overview of High-Performance Numerical Computing Libraries, introduces several low-level, high-performance numerical computing libraries and their relationship with NumPy.

Chapter 9, Performance Benchmarks, takes a deep dive into the performance of NumPy algorithms depending on the underlying high-performance numerical computing libraries.

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