Summary

In this chapter, we tuned the performance of the sentiment analysis script from Chapter 9, Analyzing Textual Data and Social Media. Using profiling, Cython, and various improvements, we doubled the execution speed of that example. We also used multiprocessing, Joblib, Jug, and MPI via IPython Parallel to take advantage of parallelization.

This was the last chapter of this book. Of course, the learning process should not stop. Change the code to suit your needs. It's always nice to have a private data analysis project, even if it is just for practice. If you can't think of a project, join a competition on http://www.kaggle.com/. They have several competitions with nice prizes.