Python's Other Statistics Tools

In the previous chapter, we considered Python's three main libraries, which make up the majority of a common data science/scientific computing pipeline: NumPy for multi-dimensional matrix computation, pandas for tabular data manipulation, and Matplotlib for data visualization.

Along the way, we have also discussed a number of supporting tools that complement those three libraries well; they are seaborn for the implementation of complex visualizations, SciPy for statistical and scientific computing capability, and scikit-learn for advanced data analysis needs.

Needless to say, there are also other tools and libraries that, even though they did not fit into our discussions well, offer unique and powerful capabilities for particular tasks in scientific computing. In this section, we will briefly consider some of them so that we can gain a comprehensive understanding of what Python tools are available for which specific tasks.

These tools...