NumPy Introduction

This article discusses Python Numpy with examples in hopes that it will be helpful for developers.

NumPy – What is it?

The Python library Numpy is utilized for working with arrays, so let’s begin with a Numpy introduction.

Travis Oliphant invented NumPy in 2005. You can utilize it freely as it is a public (open source) project.

The NumPy acronym stands for Numerical Python.

Python’s NumPy library is mostly written in Python, but most of the parts that need efficient processing when written in C or C++.

Furthermore, it supports functions for dealing with linear algebra, Fourier transforms, and matrices.

Does NumPy Is Faster Than Lists?

Arrays in NumPy are kept in a continuous location in memory, so processes can find and work with them very rapidly.

In data science, this phenomenon is called the locality of reference.

NumPy is more efficient than lists due to this reason. It is also designed to work with the most recent CPU architectures.

NumPy Uses

The Python language has lists that can be utilized as arrays, but they are time-consuming.

NumPy aims to generate an array object that are up to 50 times quicker than regular Python lists.

NumPy has an array object called ndarray that offers a lot of useful functions that help working with it very easy.

In data science, where speed and resources are essential, arrays are commonly utilized.

Data Science: The research of data science is a branch of computer science that focuses on storing, analyzing and extracting insights from data.

Install NumPy

If Python and PIP are already installed on a system, installing NumPy is simple and straightforward, as it can be easily done by using pip or other package managers.

To install NumPy using pip, the command you would use is:

pip install numpy

If the above command for installing NumPy through pip fails, you can try using a Python distribution that already includes NumPy, such as Anaconda or Spyder.

These distributions come with a wide range of packages pre-installed, including NumPy, which means you don’t have to go through the process of installing it separately.

You can download and install Anaconda or Spyder, then you can use the package right away.

Import NumPy

Once you have successfully installed NumPy, you can utilize it in your Python application by importing it utilizing the import keyword.

import numpy

After importing, the NumPy library is now accessible and ready for use.


import numpy mrx_arr = numpy.array(["Apple", "Samsung", "OnePlus", "Xiomi", "Oppo", "Sony", "Huawei"]) print(mrx_arr)


import numpy mrx_arr = numpy.array([7, 4, 8.245, 9]) print(mrx_arr)

NumPy as npy

NumPy is commonly imported and referenced as ‘npy’ alias.

alias: In Python, an alias is an alternative name used to refer to the identical object

Utilize the “as” keyword to establish an alternate name while importing a module.

import numpy as npy

By utilizing the alias ‘np’, the NumPy package can be referred to rather than its original name ‘numpy’.


import numpy as npy mrx_arr = npy.array([7, 4, 8.245, 9]) print(mrx_arr)


import numpy as npy mrx_arr = npy.array(["Apple", "Samsung", "OnePlus", "Xiomi", "Oppo", "Sony", "Huawei"]) print(mrx_arr)

Check NumPy Version

The version number for the package is located in the version attribute.


import numpy as npy mrx = npy.__version__ print(mrx)





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