The NumPy package is the workhorse of data analysis, machine learning, and scientific computing in the python ecosystem. It vastly simplifies manipulating and crunching vectors and matrices. Some of python’s leading package rely on NumPy as a fundamental piece of their infrastructure (examples include scikit-learn, SciPy, pandas, and tensorflow). Beyond the ability to slice and dice numeric data, mastering numpy will give you an edge when dealing and debugging with advanced usecases in these libraries.

In this post, we’ll look at some of the main ways to use NumPy and how it can represent different types of data (tables, images, text…etc) before we an serve them to machine learning models.

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