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NumPy Essentials

You're reading from  NumPy Essentials

Product type Book
Published in Apr 2016
Publisher
ISBN-13 9781784393670
Pages 156 pages
Edition 1st Edition
Languages
Authors (3):
Leo (Liang-Huan) Chin Leo (Liang-Huan) Chin
Profile icon Leo (Liang-Huan) Chin
Tanmay Dutta Tanmay Dutta
Profile icon Tanmay Dutta
Shane Holloway Shane Holloway
Profile icon Shane Holloway
View More author details

Table of Contents (16) Chapters

NumPy Essentials
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. An Introduction to NumPy 2. The NumPy ndarray Object 3. Using NumPy Arrays 4. NumPy Core and Libs Submodules 5. Linear Algebra in NumPy 6. Fourier Analysis in NumPy 7. Building and Distributing NumPy Code 8. Speeding Up NumPy with Cython 9. Introduction to the NumPy C-API 10. Further Reading

Setting up Cython


Cython is a compiler that converts Python code with the type definition to C code, which still runs in the Python environment. The final output is native machine code, which runs much faster than the bytecode produced by Python. The magnitude of speed-up for Python code is more evident in code that heavily uses loops. In order to compile C code, the first prerequisite is to have a C/C++ compiler such as gcc (Linux) or mingw (Windows) installed on the computer.

The second step is to install Cython. Cython comes just like any other library with a Python module and you can install it using any of your preferred methods (pip, easy_install, and so on). Once these two steps are done, you can test your setup by just trying to call Cython from the shell. If you get an error message, then you have missed the second step and you need to reinstall Cython or download the TAR archive from the Cython official website (http://cython.org/#download), then run the following command from the...

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