Diving into the world of NumPy

NumPy provides high-performance array types and routines for manipulating these arrays in Python. These arrays are useful for processing large datasets where performance is crucial. NumPy forms the base for the numerical and scientific computing stack in Python. Under the hood, NumPy makes use of low-level libraries for working with vectors and matrices, such as the Basic Linear Algebra Subprograms (BLAS) package, to accelerate computations.

Traditionally, the NumPy package is imported under the shorter alias np, which can be accomplished using the following import statement:

import numpy as np

This convention is used in the NumPy documentation and in the wider scientific Python ecosystem (SciPy, pandas, and so on).

The basic type provided by the NumPy library is the ndarray type (henceforth referred to as a NumPy array). Generally, you won’t create your own instances of this type, and will instead use one of the helper routines such...