CODEWITHSUNDEEP
pythonarraysnumpylogicconditional-statements
James Carter
James Carter

Reputation: 833

Understanding the use of any() and all() in numpy arrays

What's the difference between the following:

a = np.array([2,3,4])
b = np.array([2,7,8])

if a.any() == b.all():
   print('yes')

and

a = np.array([2,3,4])
b = np.array([2,7,8])

if a.any() == b.any():
   print('yes')

In both situations, 'yes' is printed.

Upvotes: 3

Views: 16696

Answers (3)

yoabau
yoabau

Reputation: 61

I think the original post was due to a misunderstanding in combining logical operation (such as ==) and methods np.any() and np.all() when comparing 2 tables. Following the answer from @user2653663, I thought it is worth to complete it by the following example:

    import numpy as np
    a = np.asarray([1,2,3])
    b = np.asarray([1,0,1])
    print((a == b).any())
    print((a == b).all())
    True
    False

The first print will return True as the compare operation "cell by cell" found at least 1 equal cell in both tables; which is in our case a[0] == b[0]. As the second returned False because not all cells are equals. You can easily visualize why by doing:

print(a == b)
[ True False False]

Upvotes: 1

Davide Fiocco
Davide Fiocco

Reputation: 5914

On 1D numpy arrays of integers like yours, any will give you True if and only if some element is non-zero, whereas all will give you True if and only if all elements are non-zero.

So your first snippet of code translates into:
"Print yes if the answer to the question 'Is there some non-zero element in a?' is the same as the answer to 'Are all elements of b non-zero'?".

and the second into:
"Print yes if the answer to the question 'Is there some non-zero element in a?' is the same as the answer to 'Is there some non-zero element in b?'".

Upvotes: 1

user2653663
user2653663

Reputation: 2948

any() and all() are intended for boolean arrays. any() returns True if there's any values that are equal to True in the array. all() returns True if all values in the array are equal to True. For integers/floats the functionality is similar, except that they return True if the value 0 is not found in the array. In your example, since both a.any() and a.all() will return True, it follows that a.any() == a.all().

Try executing the following code to see how it works in practice.

a = np.asarray([1,2,3])
b = np.asarray([-1,0,1])
c = np.asarray([True, False])

print(a.any())
print(a.all())

print(b.any())
print(b.all())

print(c.any())
print(c.all())

Upvotes: 3

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