NumPy, which stands for Numerical Python, is a library consisting of multidimensional array objects and a collection of routines for processing those arrays. Using NumPy, mathematical and logical operations on arrays can be performed. It is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. Why is numpy so popular? Quite simply, because it’s faster than regular Python arrays, which lack numpy’s optimized and pre-compiled C code that does all the heavy lifting. Another reason is that numpy arrays and operations are vectorized, which means they lack explicit looping or indexing in the code. This makes the code not only more readable, but also more similar to standard mathematical notation.&nbsp; Contents&nbsp;&nbsp;• Features of Numpy&nbsp;&nbsp;• Operations in Numpy&nbsp;&nbsp;• Uses of Numpy&nbsp;&nbsp;• Comparison of list vs Numpy

Introduction to Numerical Python (Numpy)

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Introduction to Numerical Python (Numpy)

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