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):
...
