# 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:

 `# import library ` `import` `numpy as np ` ` `  `# initialze 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:

 `# 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 –

 `# 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:

 `# import library ` `import` `numpy as np ` ` `  `# initialze 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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