Empty numpy array boolean contradiction [duplicate]

Question

I accidentally found something in Numpy, which I can't really understand. If I check an empty Numpy array for any true value

    np.array([]).any()

it will evaluate to false, whereas if I check all values to be true

    np.array([]).all()

it evaluates to true. This appears weird to me since no value is true but at the same time all values are true...

Answer 1

This isn't a bug, it returns True because all values are not equal to zero which is the criteria for returning True see the following note:

Not a Number (NaN), positive infinity and negative infinity evaluate to True because these are not equal to zero.

compare with the following:

In[102]:

np.array([True,]).all()
Out[102]: True

This would be equivalent to an array full of NaN which would return True

Answer 2

The logic you are seeing is not specific to NumPy. This is a Python convention which has been implemented in NumPy:

• any returns True if any value is True. Otherwise False.
• all returns True if no value is False. Otherwise True.

See the pseuedo-code in the docs to see the logic in pure Python.

In the case of np.array([]).any() or any([]), there are no True values, because you have a 0-dimensional array or a 0-length list. Therefore, the result is False.

In the case of np.array([]).all() or all([]), there are no False values, because you have a 0-dimensional array or a 0-length list. Therefore, the result is True.