0Ph.ddlZddlZddlmZdZdZdS)N)suppressct|tj o-t|tjot j|S)aTest if x is NaN. This function is meant to overcome the issue that np.isnan does not allow non-numerical types as input, and that np.nan is not float('nan'). Parameters ---------- x : any type Any scalar value. Returns ------- bool Returns true if x is NaN, and false otherwise. Examples -------- >>> import numpy as np >>> from sklearn.utils._missing import is_scalar_nan >>> is_scalar_nan(np.nan) True >>> is_scalar_nan(float("nan")) True >>> is_scalar_nan(None) False >>> is_scalar_nan("") False >>> is_scalar_nan([np.nan]) False ) isinstancenumbersIntegralRealmathisnan)xs V/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/sklearn/utils/_missing.py is_scalar_nanr sA@ q'* + ++  q', ' '  JqMMcrtt5ddlm}||ucdddS#1swxYwYdS)aTest if x is pandas.NA. We intentionally do not use this function to return `True` for `pd.NA` in `is_scalar_nan`, because estimators that support `pd.NA` are the exception rather than the rule at the moment. When `pd.NA` is more universally supported, we may reconsider this decision. Parameters ---------- x : any type Returns ------- boolean r)NANF)r ImportErrorpandasr)r rs r is_pandas_nar/s +  Bw 5s ,00)r r contextlibrr rrr rsT ###Lr