As noted above, NumPy arrays behave a lot like other Python objects, for the sake of convenience. For instance, they can be indexed like lists; arr[0] accesses the first element of a NumPy array.

NumPy arrays require far less storage area than other Python lists, and they are faster and more convenient to use, making it a great option to increase the performance of Machine Learning models ...

Arrays in Python ... to access your data. A list is also an example of a variable that stores multiple values, but has some slight differences. When using lists in Python, you store a series ...

Gommers added, "Really long-term I expect the NumPy 'execution engine' (i.e., the C and Python code that does the heavy lifting for fast array operations) to become less and less relevant ...