66 lines
1.4 KiB
Python
66 lines
1.4 KiB
Python
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import math
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import numbers
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from contextlib import suppress
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def is_scalar_nan(x):
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"""Test if x is NaN.
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This function is meant to overcome the issue that np.isnan does not allow
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non-numerical types as input, and that np.nan is not float('nan').
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Parameters
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----------
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x : any type
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Any scalar value.
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Returns
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-------
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bool
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Returns true if x is NaN, and false otherwise.
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.utils._missing import is_scalar_nan
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>>> is_scalar_nan(np.nan)
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True
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>>> is_scalar_nan(float("nan"))
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True
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>>> is_scalar_nan(None)
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False
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>>> is_scalar_nan("")
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False
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>>> is_scalar_nan([np.nan])
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False
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"""
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return (
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not isinstance(x, numbers.Integral)
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and isinstance(x, numbers.Real)
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and math.isnan(x)
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)
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def is_pandas_na(x):
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"""Test if x is pandas.NA.
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We intentionally do not use this function to return `True` for `pd.NA` in
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`is_scalar_nan`, because estimators that support `pd.NA` are the exception
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rather than the rule at the moment. When `pd.NA` is more universally
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supported, we may reconsider this decision.
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Parameters
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----------
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x : any type
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Returns
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-------
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boolean
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"""
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with suppress(ImportError):
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from pandas import NA
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return x is NA
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return False
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