utils.validation.check_exog_dtypes

utils.validation.check_exog_dtypes(
    exog,
    call_check_exog=True,
    series_id='`exog`',
)

Check that exogenous variables have valid data types (int, float, category).

This function validates that the exogenous variables (Series or DataFrame) contain only supported data types: integer, float, or category. It issues a warning if other types (like object/string) are found, as these may cause issues with some machine learning estimators.

It also strictly enforces that categorical columns must have integer categories.

Parameters

Name	Type	Description	Default
exog	Union[pd.Series, pd.DataFrame]	Exogenous variables to check.	required
call_check_exog	bool	If True, calls check_exog() first to ensure basic validity. Defaults to True.	True
series_id	str	Identifier used in warning/error messages. Defaults to “exog”.	'exog'

Raises

Name	Type	Description
	TypeError	If categorical columns contain non-integer categories.

Warns

If columns with unsupported data types (not int, float, category) are found.

Examples

>>> import pandas as pd
>>> import numpy as np
>>> from spotforecast2_safe.utils.validation import check_exog_dtypes
>>>
>>> # Valid types (float, int)
>>> df_valid = pd.DataFrame({
...     "a": [1.0, 2.0, 3.0],
...     "b": [1, 2, 3]
... })
>>> check_exog_dtypes(df_valid)  # No warning
>>>
>>> # Invalid type (object/string)
>>> df_invalid = pd.DataFrame({
...     "a": [1, 2, 3],
...     "b": ["x", "y", "z"]
... })
>>> check_exog_dtypes(df_invalid)
... # Issues DataTypeWarning about column 'b'
>>>
>>> # Valid categorical (with integer categories)
>>> df_cat = pd.DataFrame({"a": [1, 2, 1]})
>>> df_cat["a"] = df_cat["a"].astype("category")
>>> check_exog_dtypes(df_cat)  # No warning