False == 0 and True == 1. If your mask is a list, not ndarray, you might get some unexpected behavior:
>>> a = np.array([1,2,3]) >>> mask_list = [True, False, True] >>> a[mask_list] __main__:1: FutureWarning: in the future, boolean array-likes will be handled as a boolean array index array([2, 1, 2])
where this array consists of [1], a [0] and a [1], like
>>> a[np.array([1,0,1])] array([2, 1, 2])
On the other hand:
>>> mask_array = np.array(mask_list) >>> mask_array array([ True, False, True], dtype=bool) >>> a[mask_array] array([1, 3])
The warning tells you that ultimately a[mask_list] will give you the same thing as a[mask_array] (which you probably wanted it to give you first.)
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