Learn Python by Building Data Science Applications

Summary

In this final chapter, we covered multiple topics on code performance and quality and discussed a few important technologies beyond Python. In particular, we discussed how the combination of efficient code, a better understanding of requirements, and smart usage of appropriate data structures can significantly speed up the performance of code—in our case, a hundred times more performant! Then, we discussed how we can deal with big data by computing in parallel on multiple CPUs—or multiple machines in the cluster.

In the second part of this chapter, we discussed a few ways to keep code quality under control—by running sophisticated non-deterministic test suits, automating code formatting, and tracking code maintainability. 

Both code ...