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There is a newer version: 4.23.0.17664

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

   NumPy API Reference - numpy.nan 
   NumPy API Reference - numpy.isnan()