Python equivalent of MATLAB's “ismember” function

Question

After many attempts trying optimize code, it seems that one last resource would be to attempt to run the code below using multiple cores. I don\'t know exactly how to conver

Answer 1

sfstewman's excellent answer most likely solved the issue for you.

I'd just like to add how you can achieve the same exclusively in numpy.

I make use of numpy's unique an in1d functions.

B_unique_sorted, B_idx = np.unique(B, return_index=True)
B_in_A_bool = np.in1d(B_unique_sorted, A, assume_unique=True)

• B_unique_sorted contains the unique values in B sorted.
• B_idx holds for these values the indices into the original B.
• B_in_A_bool is a boolean array the size of B_unique_sorted that stores whether a value in B_unique_sorted is in A.
  Note: I need to look for (unique vals from B) in A because I need the output to be returned with respect to B_idx
  Note: I assume that A is already unique.

Now you can use B_in_A_bool to either get the common vals

B_unique_sorted[B_in_A_bool]

and their respective indices in the original B

B_idx[B_in_A_bool]

Finally, I assume that this is significantly faster than the pure Python for-loop although I didn't test it.