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Comparing arrays

A comparison between two arrays returns the elementwise result of the comparison:

>>> import numpy as np
>>> arr1 = np.array([[0, 1, 2],
...                 [3, 4, 5]])
>>> arr2 = np.array([[1, 1, 2],
...                  [3, 4, 6]])
>>> arr1 == arr2
array([[False,  True,  True],
       [ True,  True, False]], dtype=bool)

Sometimes we want to know if two arrays are equal, in the sense that all the elements of the two arrays are equal to each other. For this we use np.all. np.all accepts an array as input, and returns True if all the elements are non-zero [1].

>>> np.all([1, 2, 3])
True

Python assumes that True == 1 and False == 0 for this test of non-zero:

>>> np.all([True, True, True])
True

>>> np.all([1, 2, 0])
False
>>> np.all([True, False, True])
False

To ask whether all the elements in two arrays are equal, we can pass the result of the element-wise comparison to np.all:

>>> np.all(arr1 == arr2)
False

>>> arr3 = arr1.copy()
>>> np.all(arr1 == arr3)
True

Sometimes we want to know if any of the values in an array are non-zero [1]. Enter np.any:

>>> np.any([False, False, False])
False

>>> np.any([False, False, True])
True

>>> np.any(arr1 == arr2)
True

>>> np.any(arr1 != arr3)
False

Footnotes

[1]

(1, 2) For numerical arrays, testing whether an element is “non-zero” has the obvious meaning of element != 0. For boolean arrays non-zero means element == True. For other array types, non-zero means bool(element) == True where bool uses the Kind-of True algorithm to return True or False from an element.