Efficient way to compute intersecting values between two numpy arrays

Question

I have a bottleneck in my program which is caused by the following:

A = numpy.array([10,4,6,7,1,5,3,4,24,1,1,9,10,10,18]) B = numpy.array([1,4,5,6,7,8,9])  C = numpy.array([i for i in A if i in B]) 

The expected outcome for C is the following:

C = [4 6 7 1 5 4 1 1 9] 

Is there a more efficient way of doing this operation?

Note that array A contains repeating values and they need to be taken into account. I wasn't able to use set intersection since taking the intersection will omit the repeating values, returning just [1,4,5,6,7,9].

Also note this is only a simple demonstration. The actual array sizes can be in the order of thousands, to well over millions.

Answer 1

You can use np.in1d:

>>> A[np.in1d(A, B)] array([4, 6, 7, 1, 5, 4, 1, 1, 9]) 

np.in1d returns a boolean array indicating whether each value of A also appears in B. This array can then be used to index A and return the common values.

It's not relevant to your example, but it's also worth mentioning that if A and B each contain unique values then np.in1d can be sped up by setting assume_unique=True:

np.in1d(A, B, assume_unique=True) 

You might also be interested in np.intersect1d which returns an array of the unique values common to both arrays (sorted by value):

>>> np.intersect1d(A, B) array([1, 4, 5, 6, 7, 9]) 

Answer 2

Use numpy.in1d:

>>> A[np.in1d(A, B)] array([4, 6, 7, 1, 5, 4, 1, 1, 9])