How do I compare two numpy arrays of strings with the "in" operator to get a boolean array using a broadcast array?

Python allows a simple check if a string is contained in another string:

'ab' in 'abcd'

      

        
        
        
      

    

which is evaluated as True

.

Now take an array of strings numpy

and you can do this:

import numpy as np
A0 = np.array(['z', 'u', 'w'],dtype=object)

A0[:,None] != A0

      

        
        
        
      

    

The result in a boolean array:

array([[False,  True,  True],
       [ True, False,  True],
       [ True,  True, False]], dtype=bool)

      

        
        
        
      

    

Now let's take another array:

A1 = np.array(['u_w', 'u_z', 'w_z'],dtype=object)

      

        
        
        
      

    

I want to check where a string is A0

not contained in a string in A1

, essentially creating unique combinations, but the following does not yield a boolean array, only one boolean value, no matter how I write the indices:

A0[:,None] not in A1

      

        
        
        
      

    

I also tried using numpy.in1d

and np.ndarray.__contains__

, but those methods don't do the trick either.

Performance is an issue, so I want to take full advantage of the optimization numpy's

.

How can I achieve this?

EDIT:

I found it can be done like this:

fv = np.vectorize(lambda x,y: x not in y)
fv(A0[:,None],A1)

      

        
        
        
      

    

But as stated in the numpy

docs:

The vectorization feature is provided primarily for convenience, not performance. The implementation is essentially a for loop.

So this is the same as just looping through the array, and it would be nice to solve this without an explicit or implicit for loop.