Python is convenient and flexible, yet notably slower than other languages for raw computational speed. The Python ecosystem has compensated with tools that make crunching numbers at scale in Python both fast and convenient.
NumPy is one of the most common Python tools developers and data scientists use for assistance with computing at scale. It provides libraries and techniques for working with arrays and matrices, all backed by code written in high-speed languages like C, C++, and Fortran. And, all of NumPy’s operations take place outside the Python runtime, so they aren’t constrained by Python’s limitations.
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