To check if all the values in a Numpy array are zero or not, you can use a combination of the equality operator `==`

and the `all()`

function. The idea is to compare the array with `0`

using the `==`

operator and check if all the values in the resulting boolean array are `True`

or not using the `all`

function.

The following is the syntax –

import numpy as np # check if numpy array is all zero (ar == 0).all()

There are other methods as well, for example –

- Convert the array to a set and check if the set contains only 0.
- Iterate through the array and return
`False`

if you encounter any non-zero value.

Let’s now look at the methods mentioned above with the help of some examples.

## Example 1 – Check if Array is all zero using `all()`

function

If you compare an array with a scaler value, the resulting array would be a boolean array with `True`

for the array values that were equal to the scaler value and `False`

otherwise.

In this method, we compare the array with `0`

and check if all the values in the resulting boolean array are `True`

or not using the numpy array `all()`

function.

Let’s look at an example

import numpy as np # create two arrays ar1 = np.array([0, 0, 0, 0]) ar2 = np.array([0, None, 0, 0]) # check if array is all zero print((ar1 == 0).all()) print((ar2 == 0).all())

Output:

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True False

Here, we created two arrays. `ar1`

with all values as `0`

and `ar2`

with only some values as `0`

(not all) and then check if these arrays are all zero or not using our method.

We get `True`

for `ar1`

and `False`

for `ar2`

, which is the correct result.

## Example 2 – Using a set

The idea here is that if the array has only `0`

values, then the only unique value in the array should be `0`

. We can get the unique values in an array by converting it to a set. Let’s look at an example.

import numpy as np # create two arrays ar1 = np.array([0, 0, 0, 0]) ar2 = np.array([0, None, 0, 0]) # check if array is all zero print((len(set(ar1)) == 1) and (0 in set(ar1))) print((len(set(ar2)) == 1) and (0 in set(ar2)))

Output:

True False

We get the same result as above.

## Example 3 – Iterate through the array

Alternatively, we can iterate through the entire array, element by element, and check if each element is `0`

or not. If we encounter a non-zero element, we return `False`

.

import numpy as np # create two arrays ar1 = np.array([0, 0, 0, 0]) ar2 = np.array([0, None, 0, 0]) # function to check if array is all zero def is_array_all_zero(ar): if len(ar) == 0: return False for val in ar: if val == 0: continue else: return False return True # check if array is all zero print(is_array_all_zero(ar1)) print(is_array_all_zero(ar2))

Output:

True False

We get the same result as above. You can think of this method as a more verbose version (and less optimized) version of method 1.

You might also be interested in –

- Numpy – Check If Array has any Duplicates
- Numpy – Check If Array is Monotonically Decreasing
- Numpy – Check If Array is Monotonically Increasing

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