# Check if all elements in NumPy Array are False

This tutorial will discuss about unique ways to check if all elements in numpy array are false.

## Technique 1: Using numpy.all() method

We can use the `numpy.all()` method to check if all elements of a NumPy array are False or not.

Compare the NumPy array with a boolean value `False`, and it will return a NumPy Array of boolean values. A `True` value in this boolean NumPy Array represents that the corresponding value in the original array is `False`. Then to confirm if all the values in this boolean array are True or not, pass this boolean array to the `numpy.all()` method. If it returns True, then it means that all the values in the orignal NumPy array are `False`.

Let’s see the complete example,

```import numpy as np

# Create an example NumPy array of boolean values
arr = np.array([False, False, False, False, False])

# Check if NumPy array contains only False Values
if np.all(arr == False):
print("All elements in NumPy Array are False")
else:
print("Not all elements in NumPy Array are False")
```

Output

```All elements in NumPy Array are False

```

## Technique 2: Using numpy.count_nonzero() method

As `False` in Python evaluates to zero. So, we can call the `count_nonzero()` method of NumPy module to get the count of non-zero or non-False values in a NumPy Array. If it returns zero, then it means that all the values in NumPy array are False.

Let’s see the complete example,

```import numpy as np

# Create an example NumPy array of boolean values
arr = np.array([False, False, False, False, False])

# Check if NumPy array contains only False Values
if np.count_nonzero(arr) == 0:
print("All elements in NumPy Array are False")
else:
print("Not all elements in NumPy Array are False")
```

Output

```All elements in NumPy Array are False

```

## Summary

We learned about two different ways to check if all values in a NumPy Array are False or not in Python.

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