evalml.data_checks.DateTimeNaNDataCheck.validate¶
-
DateTimeNaNDataCheck.validate(X, y=None)[source]¶ Checks if any datetime columns contain NaN values.
- Parameters
X (ww.DataTable, pd.DataFrame, np.ndarray) – Features.
y (ww.DataColumn, pd.Series, np.ndarray) – Ignored. Defaults to None.
- Returns
dict with a DataCheckError if NaN values are present in datetime columns.
- Return type
dict
Example
>>> import pandas as pd >>> import woodwork as ww >>> import numpy as np >>> dates = np.arange(np.datetime64('2017-01-01'), np.datetime64('2017-01-08')) >>> dates[0] = np.datetime64('NaT') >>> ww_input = ww.DataTable(pd.DataFrame(dates, columns=['index'])) >>> dt_nan_check = DateTimeNaNDataCheck() >>> assert dt_nan_check.validate(ww_input) == {"warnings": [], ... "actions": [], ... "errors": [DataCheckError(message='Input datetime column(s) (index) contains NaN values. Please impute NaN values or drop these rows or columns.', ... data_check_name=DateTimeNaNDataCheck.name, ... message_code=DataCheckMessageCode.DATETIME_HAS_NAN, ... details={"columns": 'index'}).to_dict()]}