# 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:

`False`

Code 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:

`True`

Code 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:

`True`

Code 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:

`False`

Code 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`.