Up until now, we have been discussing some of the basic nuts and bolts of Numpy ; in this section we will dive deep into computation on Numpy Arrays:

Up until now, we have been discussing some of the basic nuts and bolts of Numpy ; in this section we will dive deep into the reasons that Numpy is so important in the Python Data Science world.

The key to make the computation on Numpy arrays fast is to use vectorized operations, generally implemented through Numpy’s Universal functions (ufuncs). The vectorized approach is designed to push loop into the compiled layer that underlies Numpy, leading to much faster execution. Vectorized operations in Numpy are implemented via ufuncs, whose main purpose is to quickly execute repeated operations on values in Numpy arrays.

In order to read previous articles on Numpy, follow the below links:

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