This rule raises an issue when an equality check is made against `numpy.nan`

.

## Why is this an issue?

The `numpy.nan`

is a floating point representation of Not a Number (NaN) used as a placeholder for undefined or missing values in
numerical computations.

Equality checks of variables against `numpy.nan`

in NumPy will always be `False`

due to the special nature of
`numpy.nan`

. This can lead to unexpected and incorrect results.

Instead of standard comparison the `numpy.isnan()`

function should be used.

### Code examples

#### Noncompliant code example

import numpy as np
x = np.nan
if x == np.nan: # Noncompliant: always False
...

#### Compliant solution

import numpy as np
x = np.nan
if np.isnan(x):
...

## Resources

### Documentation