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.