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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 Boost and Python

Boost is a C++ library that can interface with Python. Download it from http://www.boost.org/users/download/. The latest Boost version at the time of writing was 1.56.0. The easiest but also slowest installation method involves the following commands:

$ ./bootstrap.sh --prefix=/path/to/boost
$ ./b2 install

The prefix argument specifies the installation directory. In this example, we will assume that Boost was installed under the user's home directory in a directory called Boost (such as ~/Boost). In this directory, a lib and include directory will be created. For Unix and Linux, it is useful to run the following command:

export LD_LIBRARY_PATH=$HOME/Boost/lib:${LD_LIBRARY_PATH}

On Mac OS X, set the following environment variable:

export DYLD_LIBRARY_PATH=$HOME/Boost/lib

Redefine a rain summation function as given in the boost_rain.cpp file in this book's code bundle:

#include <boost/python.hpp>

double sum_rain(boost::python::list rain, int len...
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