How to apply numpy.linalg.norm to each row of a matrix?

Question

I have a 2D matrix and I want to take norm of each row. But when I use numpy.linalg.norm(X) directly, it takes the norm of the whole matrix.

I can take

Answer 1

Much faster than the accepted answer is

numpy.sqrt(numpy.einsum('ij,ij->i', a, a))

Note the log-scale:

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Code to reproduce the plot:

import numpy
import perfplot


def sum_sqrt(a):
    return numpy.sqrt(numpy.sum(numpy.abs(a)**2, axis=-1))


def apply_norm_along_axis(a):
    return numpy.apply_along_axis(numpy.linalg.norm, 1, a)


def norm_axis(a):
    return numpy.linalg.norm(a, axis=1)


def einsum_sqrt(a):
    return numpy.sqrt(numpy.einsum('ij,ij->i', a, a))


perfplot.show(
    setup=lambda n: numpy.random.rand(n, 3),
    kernels=[sum_sqrt, apply_norm_along_axis, norm_axis, einsum_sqrt],
    n_range=[2**k for k in range(20)],
    logx=True,
    logy=True,
    xlabel='len(a)'
    )