# Python – scipy.linalg.norm

ScipyScientific ComputingProgramming

The norm() function of the scipy.linalg package is used to return one of eight different matrix norms or one of an infinite number of vector norms.

## Syntax

scipy.linalg.norm(x)

Where x is an input array or a square matrix.

## Example 1

Let us consider the following example −

# Importing the required libraries from scipy
from scipy import linalg
import numpy as np

# Define the input array
x = np.array([7 , 4])
print("Input array:\n", x)

# Calculate the L2 norm
r = linalg.norm(x)

# Calculate the L1 norm
s = linalg.norm(x, 3)

# Display the norm values
print("Norm Value of r :", r)
print("Norm Value of s :", s)

## Output

The above program will generate the following output −

Input array:
[7 4]
Norm Value of r : 8.06225774829855
Norm Value of s : 7.410795055420619

## Example 2

Let us take another example −

# Importing the required libraries from scipy
from scipy import linalg
import numpy as np

# Define the input array
x = np.array([[ 6, 7, 8], [9, -1, -2]])
print("Input Array :\n", x)

# Calculate the L2 norm
p = linalg.norm(x)

# Calculate the L1 norm
q = linalg.norm(x, axis=1)

# Display the norm values
print("Norm Values of P :", p)
print("Norm Values of Q :", q)

## Output

It will produce the following output −

Input Array :
[[ 6 7 8]
[ 9 -1 -2]]
Norm Values of P : 15.329709716755891
Norm Values of Q : [12.20655562 9.2736185 ]