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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.

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