Python NumPy Library for Data Science

The Ultimate NumPy Tutorial for Data Science Beginners:

What is the NumPy library in Python?

NumPy stands for Numerical Python and is one of the most useful scientific libraries in Python programming. It provides support for large multidimensional array objects and various tools to work with them. Various other libraries like Pandas, Matplotlib, and Scikit–learn are built on top of this amazing library.

Python Lists vs NumPy Arrays – What’s the Difference?

If you’re familiar with Python, you might be wondering why use NumPy arrays when we already have Python lists? After all, these Python lists act as an array that can store elements of various types. This is a perfectly valid question and the answer to this is hidden in the way Python stores an object in memory.

A Python object is actually a pointer to a memory location that stores all the details about the object, like bytes and the value. Although this extra information is what makes Python a dynamically typed language, it also comes at a cost which becomes apparent when storing a large collection of objects, like in an array.

Python lists are essentially an array of pointers, each pointing to a location that contains the information related to the element. This adds a lot of overhead in terms of memory and computation. And most of this information is rendered redundant when all the objects stored in the list are of the same type!