Python for Data Analysis (Rough Cuts)

Why Python for data analysis?

Python as glue

Part of Python's success as a scientific computing platform is the ease of integrating C, C++, and FORTRAN code. Most modern computing environments share a similar set of legacy FORTRAN and C libraries for doing linear algebra, optimization, integration, fast fourier transforms, and other such algorithms. The same story has held true for many companies and national labs that have used Python to glue together 30 years' worth of legacy software.

 In the last few years, the Cython project (http://cython.org) has become one of the preferred ways of both creating fast compiled extensions for Python also interfacing with C and C++ code.

Solving the two language problem

In many organizations, it is common to research, prototype, and test new ideas using a more domain-specific computing language like MATLAB or R then later port those ideas to be part of a larger production system written in, say, Java, C#, or C++. What people are increasingly finding is that Python is a suitable language not only for doing research and prototyping but also building the production systems too. I strongly believe that more and more companies will go down this path as there are often significant organizational benefits to having both scientists and technologists using the same set of programmatic tools.