NumPy: consequences of using 'np.save()' with 'allow_pickle=False'

An object array is just a normal numpy array where the dtype is object; this happens if the contents of the array aren't of the normal numerical types (like int or float, etc.). We can try out saving a numpy array with objects, just to test how this works. A simple kind of object would be a dict:

>>> import numpy as np
>>> a = np.array([{x: 1} for x in range(4)])
>>> a
array([{0: 1}, {1: 1}, {2: 1}, {3: 1}], dtype=object)
>>> np.save('test.pkl', a)

Loading this back works fine:

>>> np.load('test.pkl.npy')
array([{0: 1}, {1: 1}, {2: 1}, {3: 1}], dtype=object)

The array can't be saved without using pickle, though:

>>> np.save('test.pkl', a, allow_pickle=False)
...
ValueError: Object arrays cannot be saved when allow_pickle=False

The rule of thumb for pickles is that you're safe if you're loading a pickle that you made, but you should be careful about loading pickles that you got from somewhere else. For one thing, if you don't have the same libraries (or library versions) installed that were used to make the pickle, you might not be able to load the pickle (this is what's meant by portability above). Security is another potential concern; you can read a bit about how pickles can be abused in this article, for instance.