Book Image

Python Data Analysis - Second Edition

By : Ivan Idris
Book Image

Python Data Analysis - Second Edition

By: Ivan Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Key Concepts
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 bundle...