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# What is the np.linalg.norm() method in NumPy? Educative Answers Team

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In NumPy, the np.linalg.norm() function is used to calculate one of the eight different matrix norms or one of the vector norms.

## Syntax

numpy.linalg.norm(x, ord=None, axis=None, keepdims=False)


## Parameters

1. x: This is an input array.
2. ord: This stands for “order”. The different orders of the norm are given below:
order norm for matrix norm for vector
None Frobenius norm 2-norm
fro Frobenius norm
nuc nuclear norm
inf max(sum(abs(x), axis=1) max(abs(x))
-inf min(sum(abs(x), axis=1)) min(abs(x))
0 sum(x != 0)
1 max(sum(abs(x), axis=0)) as below
-1 min(sum(abs(x), axis=0)) as below
2 2-norm (largest sing. value) as below
-2 smallest singular value as below
other sum(abs(x)**ord)**(1./ord)
1. axis: If the axis is an integer, then the vector norm is computed for the axis of x.If the axis is a 2-tuple, the matrix norms of specified matrices are computed. If the axis is None, then either a vector norm (when x is 1-D) or a matrix norm (when x is 2-D) is returned.

2. keepdims: It receives a boolean value. If its value is​ true, then the axes that are normed over are left in the result as dimensions with size one. Otherwise, the axes which are normed over are kept in the result.

## Code

from numpy import linalg as LNG import numpy as np# creating an array using arrange function# -3, -2, -1, 0, 1, 2, 3, 4, 5 a = np.arange(9) - 3# converting the array into a matrix of 3x3a = a.reshape((3,3))# Printing matrixprint(a)# computing norm for a matrixprint(LNG.norm(a))# computing vector normsprint(LNG.norm(a, axis=0))

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