The second tool that we'll discuss in this chapter is called pandas (http://pandas.pydata.org/). It is an open source library that provides high-performance, easy-to-use data structures, and data-analysis tools for Python.
This tool was invented back in 2008 by developer Wes McKinney while needing a performant solution to perform quantitative analysis on financial data. The library has become one of the most popular and active projects in the Python community.
One thing to note regarding the performance of code written using pandas is that parts of its critical code paths were written using Cython (we covered Cython in Chapter 6, Generic Optimization Options).
Given the popularity of pandas, there are many ways to install it onto your system. It all depends on the type of setup you have.
The recommended way is to directly install the Anaconda Python distribution (docs.continuum.io/anaconda/), which comes packed with pandas and the rest of the SciPy stack (such...