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The Python language has been around for a long time and worn many hats. Its original implementation was started by Guido van Rossum in 1989 as a tool for system administration as an alternative to Bash scripts and C programs.1 Since its public release in 1991, it has evolved for use in a myriad of industries. These include everything from web-development, film, government, science, and business.2 I was first introduced to Python working in the film industry, where we used it to automate data management across departments and locations. In the last decade, Python has become a dominant tool in Data Science. This dominance evolved due to two developments: the Jupyter notebook, and powerful third-party libraries. In 2001 Fernando Perez began the IPython project, an interactive Python environment inspired by Maple and Mathematica notebooks.3 By 2014, the notebook-specific part of the project was split off as the Jupyter project. These notebooks have excelled for scientific and statistical work environments. In parallel with this development, third-party libraries for scientific and statistical computing were developed for Python. With so many applications, the functionality available to a Python programmer has grown immensely. With specialized packages for everything from opening web sockets to processing natural language text, there is more available than a beginning developer needs.