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# Find a matrix or vector norm using NumPy

To find a matrix or vector norm we use function numpy.linalg.norm() of Python library Numpy. This function returns one of the seven matrix norms or one of the infinite vector norms depending upon the value of its parameters.

Syntax: numpy.linalg.norm(x, ord=None, axis=None)
Parameters:
x: input
ord: order of norm
axis: None, returns either a vector or a matrix norm and if it is an integer value, it specifies the axis of x along which the vector norm will be computed

Example 1:

## Python3

 `# import library` `import` `numpy as np`   `# initialize vector` `vec ``=` `np.arange(``10``)`   `# compute norm of vector` `vec_norm ``=` `np.linalg.norm(vec)`   `print``(``"Vector norm:"``)` `print``(vec_norm)`

Output:

```Vector norm:
16.881943016134134```

The above code computes the vector norm of a vector of dimension (1, 10)
Example 2:

## Python3

 `# import library` `import` `numpy as np`   `# initialize matrix` `mat ``=` `np.array([[ ``1``, ``2``, ``3``],` `               ``[``4``, ``5``, ``6``]])`   `# compute norm of matrix` `mat_norm ``=` `np.linalg.norm(mat)`   `print``(``"Matrix norm:"``)` `print``(mat_norm)`

Output:

```Matrix norm:
9.539392014169456```

Here, we get the matrix norm for a matrix of dimension (2, 3)
Example 3:
To compute matrix norm along a particular axis –

## Python3

 `# import library` `import` `numpy as np`     `mat ``=` `np.array([[ ``1``, ``2``, ``3``],` `               ``[``4``, ``5``, ``6``]])`   `# compute matrix num along axis ` `mat_norm ``=` `np.linalg.norm(mat, axis ``=` `1``)`   `print``(``"Matrix norm along particular axis :"``)` `print``(mat_norm)`

Output:

```Matrix norm along particular axis :
[3.74165739 8.77496439]```

This code generates a matrix norm and the output is also a matrix of shape (1, 2)
Example 4:

## Python3

 `# import library` `import` `numpy as np`   `# initialize vector` `vec ``=` `np.arange(``9``)`   `# convert vector into matrix` `mat ``=` `vec.reshape((``3``, ``3``))`   `# compute norm of vector` `vec_norm ``=` `np.linalg.norm(vec)`   `print``(``"Vector norm:"``)` `print``(vec_norm)`   `# computer norm of matrix` `mat_norm ``=` `np.linalg.norm(mat)`   `print``(``"Matrix norm:"``)` `print``(mat_norm)`

Output:

```Vector norm:
14.2828568570857
Matrix norm:
14.2828568570857```

From the above output, it is clear if we convert a vector into a matrix, or if both have same elements then their norm will be equal too.

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