python - Numpy Lookup (Map, or Point) -

i have large numpy array:

array([[32, 32, 99,  9, 45],  #        [99, 45,  9, 45, 32],        [45, 45, 99, 99, 32],        [ 9,  9, 32, 45, 99]]) 

and large-ish array of unique values in particular order:

array([ 99, 32, 45, 9])       # b 

how can (no python dictionaries, no copies of a, no python loops) replace values in a become indicies of values in b?:

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

i feel reaaly dumb not being able off top of head, nor find in documentation. easy points!

here go

a = array([[32, 32, 99,  9, 45],  #    [99, 45,  9, 45, 32],    [45, 45, 99, 99, 32],    [ 9,  9, 32, 45, 99]])  b = array([ 99, 32, 45, 9])  ii = np.argsort(b) c = np.digitize(a.reshape(-1,),np.sort(b)) - 1 

originally suggested:

d = np.choose(c,ii).reshape(a.shape) 

but realized that had limitations when went larger arrays. instead, borrowing @unutbu's clever reply:

d = np.argsort(b)[c].reshape(a.shape) 

or one-liner

np.argsort(b)[np.digitize(a.reshape(-1,),np.sort(b)) - 1].reshape(a.shape) 

which found faster or slower @unutbu's code depending on size of arrays under consideration , number of unique values.