We mentioned previously that the two most relevant characteristics of Python are its ability to integrate with other languages and its mature package system, which is well embodied by PyPI (the Python Package Index: pypi.org), a common repository for the majority of Python open source packages that are constantly maintained and updated.

The packages that we are now going to introduce are strongly analytical and they will constitute a complete data science toolbox. All of the packages are made up of extensively tested and highly optimized functions for both memory usage and performance, ready to achieve any scripting operation with successful execution. A walkthrough on how to install them is provided in the following section.

Partially inspired by similar tools present in R and MATLAB environments, we will explore how a few selected Python commands can allow you to efficiently handle data and then explore, transform, experiment, and learn from the same without having to write too much code or reinvent the wheel.