Python allows for a simple check if a string is contained in another string:
'ab' in 'abcd'
which evaluates to True.
Now take a numpy array of strings and you can do this:
import numpy as np
A0 = np.array(['z', 'u', 'w'],dtype=object)
A0[:,None] != A0
Resulting in a boolean array:
array([[False, True, True],
[ True, False, True],
[ True, True, False]], dtype=bool)
Lets now take another array:
A1 = np.array(['u_w', 'u_z', 'w_z'],dtype=object)
I want to check where a string of A0 is not contained in a string in A1, essentially creating unique combinations, but the following does not yield a boolean array, only a single boolean, regardless of 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 seem to do the trick either.
Performance is an issue here so I want to make full use of numpy's optimizations.
How do 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 the numpy docs state:
The vectorize function is provided primarily for convenience, not for performance. The implementation is essentially a for loop.
So this is the same as just looping over the array, and it would be nice to solve this without explicit or implicit for-loop.