numpy

Regardless of the data, the first step in analyzing them is transforming them into an array of numbers. The fundamental process of doing data science is efficiently is storing and manipulating numerical arrays. Because of this Python has specialized tools for handling numerical arrays

NumPy Package
Pandas Package

This tutorial only focuses on the NumPy package. However, Pandas package documentation will be provided in the latter days. The entire code for the tutorial can be found on my?GitHub Repository?below:

Tanu-N-Prabhu/Python
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github.com

What is NumPy?

NumPy stands for?Numerical Python.?NumPy provides an efficient way to store and operate on dense data buffers. With the help of the NumPy library, we can create and operate with arrays to store the data. In some ways, NumPy’s arrays are like Python’s list built-in function.

How to execute NumPy and check its version?

Let’s get started. You can execute the below programs in your favorite python editors like?PyCharm,?Sublime Text, or Notebooks like?Jupyter?and?Google Colab. It's basically your preference to choose an IDE of your choice. I am using?Google Colab?to write my code because it gives me tons of options to provides good documentation. Also, visit the?NumPy website?to get more guidelines about the installation process.

Once you have installed the NumPy on your IDE you need to import it. It’s often a good practice to check the version of the library. So to install the NumPy library you need to use the below code.

import numpy: