Numpy Lookup (Map, or Point)

Question

I have a large numpy array:

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

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

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

How can I quickly (no python dictionaries, no copies of A, no python loops) replace the values in A so that become the indicies of the 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 for not being able to do this off the top of my head, nor find it in the documentation. Easy points!

Answer 1

Here you go

A = array([[32, 32, 99,  9, 45],  # A
   [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 I suggested:

D = np.choose(C,ii).reshape(A.shape)

But I realized that that had limitations when you went to larger arrays. Instead, borrowing from @unutbu's clever reply:

D = np.argsort(B)[C].reshape(A.shape)

Or the one-liner

np.argsort(B)[np.digitize(A.reshape(-1,),np.sort(B)) - 1].reshape(A.shape)

Which I found to be faster or slower than @unutbu's code depending on the size of the arrays under consideration and the number of unique values.