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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

Loading and saving files

In this section, you will learn how to load/import your data and save it. There are many different ways of loading data, and the right way depends on your file type. You can load/import text files, SAS/Stata files, HDF5 files, and many others. HDF (Hierarchical Data Format) is one of the popular data formats which is used to store and organize large amounts of data and it is very useful while working with a multidimensional homogeneous arrays. For example, Pandas library has a very handy class named as HDFStore where you can easily work with HDF5 files. While working on data science projects, you will most likely see many of these types of files, but in this book, we will cover the most popular ones, such as NumPy binary files, text files (.txt), and comma-separated values (.csv) files.

If you have a large dataset in memory and on disk to manage, you can...

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