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In a world full of data, working with matrices and arrays is extremely important. That's where NumPy comes in handy. With its blazing speed and relatively easy-to-use interface, it has become the most used Python library for working with arrays.

Let's now discuss the speed of NumPy and where it comes from. Despite being a Python library, it is mostly written in C, a low-level language that allows for fast computations.

Another contributing factor to NumPy's speed is vectorization. Essentially, vectorization involves transforming an algorithm from operating on a single value at a time to operating on a set of values (vector) at once, which is performed under the hood at the CPU level.