NumPy forms the basis of many Python libraries in the data science domain. Everything about data science starts with data and it comes in various formats.
Everything about data science starts with data and it comes in various formats. Numbers, images, texts, x-rays, sound, and video recordings are just some examples of data sources. Whatever the format data comes in, it needs to be converted to an array of numbers to be analyzed. Hence, it is crucial to effectively store and modify arrays of numbers in data science.
*NumPy *(Numerical Python) is a scientific computing package that provides numerous ways to create and operate on arrays of numbers. It forms the basis of many widely used Python libraries related to data science such as Pandas and Matplotlib.
In this post, I will go over 20 commonly used operations on NumPy arrays. These operations can be grouped under 4 main categories:
We first need to import NumPy:
import numpy as np
Random integers in a specific range
The first parameter determines the upper bound of the range. The lower bound is 0 by default but we can also specify it. The *size *parameter is used to specify the size, as expected.
We created a 3x2 array of integers between 2 and 10.
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