Introduction to Numpy Library in Python

July 05, 2022

Introduction to Numpy Library in Python

NUMPY - Introduction

Introduction

NUMPY stands for Numerical Python. Numpy is one of the libraries for the python programming language. It is used for high level mathematical calculations. It has the large collection of functions for working with the multi-dimensional arrays, matrices, linear algebra, Fourier transform, etc.

Installation of Numpy

1. Open the command or terminal on your system.

2. Enter the following command below.

#installation of NUMPY using PIP
pip install numpy

(OR)

#installation of NUMPY using CONDA
conda install numpy

3. Now, NUMPY installed on your system. To verify that use the command below.

#check for version of NUMPY
numpy --version

Tip : Use Anaconda Navigator, it comes with built-in software of JUPYTER NOTEBOOKS and pre-installed packages like Numpy, Pandas, Scipy, etc.

Comparison of Lists and Numpy

In Python lists, we can multiply the 2-dimensional matrices with the help of loops.

   for (i = 0; i < rows; i++): {
   for (j = 0; j < columns; j++): {
   c[i][j] = a[i][j]*b[i][j];
   }
   }

In Numpy ndarray, we can easily multiply arrays with faster execution and simply by,

c = a * b

How to import Numpy in your program?

It is very simple to import Numpy to our program. Follow the code below.

import numpy

Also we can give alias name to the Numpy package.

import numpy as np

Examples

Sample program using Numpy

import numpy as np

   maths = np.array([90, 67, 98, 100, 37])
   science = np.array([100, 87, 47, 89, 45])
   print("Maths marks : \n", maths)
   print("Science marks : \n", science)

student_marks = np.array([maths, science], dtype = int)
print("Student marks of Maths and Science : \n", student_marks)

Output

Maths marks :
[ 90 67 98 100 37]

Science marks :
[100 87 47 89 45]

   Student marks of Maths and Science :
    [[ 90 67 98 100 37]
    [100 87 47 89 45]]

Conclusion

Numpy is an vectorized code which provides more concise and easy to read. Let us explore the all Numpy functions on upcoming articles.

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