Beginner’s Guide to NumPy: A Must Have Python Library in Data Scientist Toolkit

NumPy, which stands for Numerical Python, is a fundamental library for mathematical computations. This library can be used for different functions in Linear algebra, Matrix computations, Fourier Transforms etc. In Python, array.array function is limited to only one dimension which can be substituted with NumPy for multi dimensional operations. One can compute the multidimensional array functions like the figure below, In this article we will go through some of the important built-in function of NumPy to understand the logic and mathematics behind it. By using NumPy, you can speed up your workflow, and interface with other packages in the Python ecosystem, like scikit-learn, that use NumPy under the hood. Let us dive into computing the math behind these. 1. Arrays An array is a set of elements of a data type. The shape of the NumPy array can be defined with an enclosed tuple of positive integers. The order of a NumPy array is given by the dimensions. We can index and slice a parti