Find unsorted indices after using numpy.searchsorted
There are a few answers dancing around this already, but just to make it clear all you need to do is use sort[rank].
# Setupids = np.array([22, 5, 4, 0, 100])targets = np.array([5, 0])sort = np.argsort(ids)rank = np.searchsorted(ids, targets, sorter=sort)print(sort[rank])# array([1, 3])
I think I've come up with something.
We can construct a 'cipher' or sorts: key = numpy.arange(len(ids)) applying the initial sorter to this key then gives the reverse mapping: revsort = key[np.argsort(ids)]
edit: as @birico points out, key[sort] is identical to sort itself!
>>> sort = np.argsort(ids)>>> ids[sort][0,4,5,22,100]>>> found = np.searchsorted(ids, targets, sorter=sort)>>> found[2,0]>>> sort[found][1,3]