Getting the indices of several elements in a NumPy array at once

This is a simple one-liner using the numpy-indexed package (disclaimer: I am its author):

import numpy_indexed as npi
idx = npi.indices(b, a)

The implementation is fully vectorized, and it gives you control over the handling of missing values. Moreover, it works for nd-arrays as well (for instance, finding the indices of rows of a in b).

You could use in1d and nonzero (or where for that matter):

>>> np.in1d(b, a).nonzero()[0]
array([0, 1, 4])

This works fine for your example arrays, but in general the array of returned indices does not honour the order of the values in a. This may be a problem depending on what you want to do next.

In that case, a much better answer is the one @Jaime gives here, using searchsorted:

>>> sorter = np.argsort(b)
>>> sorter[np.searchsorted(b, a, sorter=sorter)]
array([0, 1, 4])

This returns the indices for values as they appear in a. For instance:

a = np.array([1, 2, 4])
b = np.array([4, 2, 3, 1])

>>> sorter = np.argsort(b)
>>> sorter[np.searchsorted(b, a, sorter=sorter)]
array([3, 1, 0]) # the other method would return [0, 1, 3]