NumPy all()

Summary: in this tutorial, you’ll learn how to use the numpy all() function that returns True if all elements in an array evaluate True.

Introduction to the numpy all() function #

The numpy all() function returns True if all elements in an array (or along a given axis) evaluate to True.

The following shows the syntax of the all() function:

numpy.all(a, axis=None, out=None, keepdims=<no value>, *, where=<no value>)Code language: Python (python)

In this syntax, a is a numpy array or an array-like object e.g., a list.

If the input array contains all numbers, the all() function returns True if all numbers are nonzero or False if least one number is zero. The reason is that all non-zero numbers evaluate to True while zero evaluates to False.

NumPy all() function examples #

Let’s take some examples of using the all() function.

1) Using numpy all() function on 1-D array examples #

The following example uses the all() function to test whether all numbers in an array are non-zero:

import numpy as np

result = np.all([0, 1, 2, 3])
print(result)Code language: Python (python)

Output:

FalseCode language: Python (python)

The result is False because the array has zero at index 0.

import numpy as np


result = np.all(np.array([-1, 2, 3]))
print(result)Code language: Python (python)

Output:

TrueCode language: Python (python)

This example returns True because all numbers in the array are nonzero. You can pass an array-like object e.g., a list to the all() function. For example:

import numpy as np


result = np.all([-1, 2, 3])
print(result)Code language: Python (python)

Output:

TrueCode language: Python (python)

2) Using the numpy all() function with a multidimensional array example #

The following example uses the all() function to test if all elements of a multidimensional array evaluate to True:

import numpy as np

a = np.array([[0, 1], [2, 3]])
result = np.all(a, axis=0)
print(result)Code language: Python (python)

Output:

import numpy as np

a = np.array([
    [0, 1],
    [2, 3]
])
result = np.all(a, axis=0)
print(result)Code language: Python (python)

Output:

FalseCode language: Python (python)

Also, you can evaluate elements along an axis by passing the axis argument like this:

import numpy as np

a = np.array([
    [0, 1],
    [2, 3]]
)
result = np.all(a, axis=0)
print(result)Code language: Python (python)

Output:

[False  True]Code language: Python (python)

And axis-1:

import numpy as np

a = np.array([
    [0, 1],
    [2, 3]
])
result = np.all(a, axis=1)
print(result)Code language: Python (python)

Output:

[False  True]Code language: Python (python)

Summary #

• Use the numpy all() function to test whether all elements in an array or along an axis evaluate to True.