Errors with indexing when using numpy delete and enumerate

Python 3.9

I have a numpy ndarray of strings. The actual array has thousands of strings, but let's say:

words_master = ['CARES' 'BARES' 'CANES' 'TARES' 'PARES' 'BANES' 'BALES' 'CORES' 'BORES'
 'MARES']

I am trying to create a function that returns a list where the strings containing a given character have been deleted. This works as a while loop and if statement:

                index = 0
                temp = []
                while index != len(words_master):
                    idx = words_master[index]
                    if 'A' in idx:
                        temp.append(index)
                    index += 1
                words_master = np.delete(words_master, temp)

Since this is still a for loop and if statement, I'm wondering if it can be made more efficient using a list comprehension.

My best guess at this would be:

words_master = np.delete(words_master, np.argwhere([x for x, item in enumerate(words_master) if 'A' in item]))

Logic here is that np.delete will take the initial array and then delete all items at the indexes set by np.argwhere. However, it gives this output:

['CARES' 'BORES' 'MARES']

It appears that it ignores the first and last elements?

Other oddities: if I use 'CARES' in item, it returns the list without making any changes:

['CARES' 'BARES' 'CANES' 'TARES' 'PARES' 'BANES' 'BALES' 'CORES' 'BORES'
 'MARES']

And if I use any other parameter ('MARES' or 'M' or 'O') it seems to return the full list without the first word:

['BARES' 'CANES' 'TARES' 'PARES' 'BANES' 'BALES' 'CORES' 'BORES' 'MARES']

I tried:

• Playing around with the index, for instance using (reversed(list(enumerate.. or making the list of indices -1. However, these result in the same type of patterns but just displaced.
• Using np.where() instead, but am having similar problems.

I'm wondering if there is a clean way to fix that? Or is the while loop/if statement the best bet?

Edit: to the question "why not use list", I read that numpy arrays are a lot faster than python lists, and when I tested this same for-loop except using a python list with the remove() function, it was 10x slower on a larger dataset.

source https://stackoverflow.com/questions/70760190/errors-with-indexing-when-using-numpy-delete-and-enumerate