Find indices of a list of values in a not sorted numpy array

Question

I'm referring to a similar question: Find indices of a list of values in a numpy array

In that case we have a master array that is sorted and another array of which we want to find the index in the master array.

master = np.array([1,2,3,4,5])
search = np.array([4,2,2,3])

The suggested solution was:

>>> master = np.array([1,2,3,4,5])
>>> search = np.array([4,2,2,3])
>>>np.searchsorted(master, search)
array([3, 1, 1, 2])

But what if master is not sorted? for example if i have two arrays like this where the first one is not sorted:

>>>master = np.array([2,3,5,4,1])
>>>search = np.array([3,2,1,4,5])

i get:

>>> np.searchsorted(master, search)
array([1, 0, 0, 2, 5])

But instead i would like:

array([1,0,4,3,2])

i.e. the indices of items in search in master.

How do i get them possibly with a native function of numpy?(not using [np.where(master==i) for i in search] )

Thanks

EDIT: In this case the search array is a permutation of master. Then i would like to find how the index of master are permuted to give a permuted array like search.

As general case, search array contain some item that maybe contained or not in the master such as:

>>>master = np.array([2,3,5,4,1])
>>>search = np.array([1,4,7])

Answer 1

Disclaimer: I wrote this answer for an earlier revision of the question. If you want to solve the problem in the appendix (when we aren't just looking for a permutation of an array), see Will's answer.

If all else fails, you need to sort your master array temporarily, then invert the sort order needed for this after matching the elements:

import numpy as np

master = np.array([2,3,5,4,1])
search = np.array([3,2,1,4,5])

# sorting permutation and its reverse
sorti = np.argsort(master)
sorti_inv = np.empty(sorti.shape,dtype=np.int64)
sorti_inv[sorti] = np.arange(sorti.size)

# get indices in sorted version
tmpind = np.searchsorted(master,search,sorter=sorti)

# transform indices back to original array with inverse permutation
final_inds = tmpind[sorti_inv]

The result of the above is correctly

array([1, 0, 4, 3, 2])

---

As you noted in a comment, your specific search and master are permutations of each other. In this case you can alternatively sort both arrays, and use the inverse permutation combined with the other direct permutation:

sorti = np.argsort(master)
sorti_inv = np.empty(sorti.shape,dtype=np.int64)
sorti_inv[sorti] = np.arange(sorti.size)
sorti_s = np.argsort(search)
final_inds = sorti_s[sorti_inv]

One should consider the effort needed to search two arrays vs searching one array in the sorted version of another. I really can't tell which one's faster.