I have a main numpy array a and I have another numpy array b. What I want to do is go through each element of b and check if that element exists in a. Keep in mind that both a and b are pretty massive, so I would like to avoid O(N) search times.

I know np.searchsorted(a,b) exists, but this provides an index at which I need to place b. This does not tell me if an element of b is present in a right off the bat.

My question is, is there a binary search algorithm built into numpy that simply reports True or False if an element from b exists in a? I am aware that I can write one but if there is a vectorized that is readily available, I could save some time.

Any advice would be appreciated!

CodePudding user response：

Once you have completed the sorted search you can check if the elements at those indices are equal to the elements in b:

a = numpy.array([1,2,3,4,7])
b = numpy.array([1,4,5,7])
x = numpy.searchsorted(a,b)
boolean_array = a[x] == b

searchsorted indicates that with the default side = 'left' it ensures : a[i-1] < v <= a[i] so if a[i] is equal to the corresponding element in b then it gives the match you want.