Numpy - quick tour

NumPy is mainly used for scientific, computational purposes and math. Often, data scientists, machine learning engineers prefer this particular library over the built-in functionality of the Python language, due to the huge number of effective solutions that can be used right out of the box. A significant advantage of NumPy is its speed in computational processes due to the use of the C language.

Let's take a quick look at the difference between Python lists and ndarray lists in NumPy.

Python lists are a mixture of linked lists and arrays. More precisely, the elements themselves are not stored in lists. Instead, each element of the list is stored at random locations in memory, and the addresses of those random locations are stored in the list. Also, lists store additional information about their properties, which also takes up additional space.

While, numpy has multi-dimensional arrays - ndarray that take up less space and perform math operations much faster. The con of using ndarray maybe that you can only store elements of the same data type. In the picture, you can see the structure of this data type.