You can always take a look at the .size attribute. It is defined as an integer, and is zero (0) when there are no elements in the array:

Use with AIimport numpy as np
a = np.array([])

if a.size == 0:
    # Do something when `a` is empty

You didn't read the documentation, which says:

Return a new array of given shape and type, without initializing entries.

empty has nothing to do with creating an array that is "empty" in the sense of having no elements. It just means the array doesn't have its values initialized (i.e., they are unpredictable and depend on whatever happens to be in the memory allocated for the array).

You need the shape to be (0, 3) so you have the correct number of columns to stack but have actually no data inside:

import numpy as np
pointsarrray = np.empty((0,3))

pointsarrray
# array([], shape=(0, 3), dtype=float64)

Short answer

Use numpy.zeros instead of numpy.empty to get rid of the surprise garbage values in your new arrays.

details

The arrays created by numpy.zeros have all of their elements initialized to a "zero value". For arrays with dtype=str, this will be the empty string ''.

From the Numpy docs:

Notes

empty, unlike zeros, does not set the array values to zero, and may therefore be marginally faster. On the other hand, it requires the user to manually set all the values in the array, and should be used with caution.

The fact that it works in Python 3 (but not Python 2) is undefined behavior. Basically, it's a quirk of the implementation which the Numpy developers didn't plan. The best practice is to not rely on such things in your code. As you've seen, the outcome of an undefined behavior is not guaranteed to be consistent across versions, implementations, different computers that you run your code on, etc.

Also, it sounds like you might be a little bit confused about how Numpy arrays work. A numpy array starts off at a fixed size when you create it. This is unlike a normal Python list [], which grows dynamically as you add values to it.

Also, you don't need both index and i in _resize_array. Just use one or the other, like this:

for i in range(self.size):
    new_array[i] = self.array[i]

Aside from that your code is fine.