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Python Data Analysis, Second Edition

You're reading from   Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
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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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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. The Pandas Primer 4. Statistics and Linear Algebra 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

Integrating Boost and Python

Boost is a C++ library that can interface with Python. Download it from http://www.boost.org/users/download/. The Boost version at the time of writing is 1.63.0. 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, you should 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

In our case, we set this variable as follows:

export DYLD_LIBRARY_PATH=/usr/local/Cellar/boost/1.63.0/lib

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

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