# Numpy – Check if Array is all Zero

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:

```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 –

• 