Several Python courses on NumPy focus on building programming or statistical examples intended to create a foundation for data science.
While this is important, I want to stay true to anyone who is just getting started working with data so the focus will be the practical usage of Python and NumPy for data analysis.This means not all of the features of NumPy will be covered, so I encourage you to learn more by looking at resources in the Further reading section. The history of the NumPy library has evolved from what was originally named Numerical Python. It was created as an open source project in 2001 by David Ascher, Paul Dubois, Konrad Hinsen, Jim Hugunin, and Travis Oliphant. According to the documentation, the purpose was to extend Python to allow the manipulation of large sets of objects organized in a grid-like fashion.
Python does not support arrays out of the box but does have a similar feature called...
