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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Integrating SWIG and NumPy

C is a widespread programming language developed around 1970. Various C dialects exist and C has influenced other programming languages. C is not object-oriented. This led to the creation of C++, which is an object-oriented language with C features, since C is a subset of C++. C and C++ are compiled languages. We need to compile source code to create so-called object files. After that, we must link the object files to create dynamically shared libraries.

The good thing about integrating C and Python is that a lot of options are available to us. The first option is Simplified Wrapper and Interface Generator (SWIG). SWIG adds an additional step in the development process, which is the generation of glue code between Python and C (or C++). Download SWIG from http://www.swig.org/download.html. At the time of writing, the most current SWIG version was 3.0.2. A prerequisite to installing SWIG is to install Perl Compatible Regular Expressions (PCRE). PCRE is a C regular...

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