utils.validation.check_exog

utils.validation.check_exog(exog, allow_nan=True, series_id='`exog`')

Validate that exog is a pandas Series or DataFrame.

This function ensures that exogenous variables meet basic requirements: - Must be a pandas Series or DataFrame - If Series, must have a name - Optionally warns if NaN values are present

Parameters

Name	Type	Description	Default
exog	Union[pd.Series, pd.DataFrame]	Exogenous variable/s included as predictor/s.	required
allow_nan	bool	If True, allows NaN values but issues a warning. If False, raises no warning about NaN values. Defaults to True.	True
series_id	str	Identifier of the series used in error messages. Defaults to “exog”.	'exog'

Raises

Name	Type	Description
	TypeError	If exog is not a pandas Series or DataFrame.
	ValueError	If exog is a Series without a name.

Warns

If allow_nan=True and exog contains NaN values.

Examples

>>> import pandas as pd
>>> import numpy as np
>>> from spotforecast2_safe.utils.validation import check_exog
>>>
>>> # Valid DataFrame
>>> exog_df = pd.DataFrame({"temp": [20, 21, 22], "humidity": [50, 55, 60]})
>>> check_exog(exog_df)  # No error
>>>
>>> # Valid Series with name
>>> exog_series = pd.Series([1, 2, 3], name="temperature")
>>> check_exog(exog_series)  # No error
>>>
>>> # Invalid: Series without name
>>> exog_no_name = pd.Series([1, 2, 3])
>>> try:
...     check_exog(exog_no_name)
... except ValueError as e:
...     print(f"Error: {e}")
Error: When `exog` is a pandas Series, it must have a name.
>>>
>>> # Invalid: not a Series/DataFrame
>>> try:
...     check_exog([1, 2, 3])
... except TypeError as e:
...     print(f"Error: {e}")
Error: `exog` must be a pandas Series or DataFrame. Got <class 'list'>.