datetime_nan_data_check

Data check that checks each column in the input for datetime features and will issue an error if NaN values are present.

Module Contents

Classes Summary

DateTimeNaNDataCheck

Check each column in the input for datetime features and will issue an error if NaN values are present.

Attributes Summary

error_contains_nan

Contents

class evalml.data_checks.datetime_nan_data_check.DateTimeNaNDataCheck

Check each column in the input for datetime features and will issue an error if NaN values are present.

Methods

name	Return a name describing the data check.
validate	Check if any datetime columns contain NaN values.

name(cls)

Return a name describing the data check.

validate(self, X, y=None)

Check if any datetime columns contain NaN values.

Parameters

• X (pd.DataFrame, np.ndarray) – Features.

• y (pd.Series, np.ndarray) – Ignored. Defaults to None.

Returns

dict with a DataCheckError if NaN values are present in datetime columns.

Return type

dict

Examples

>>> import pandas as pd
>>> import numpy as np
...
>>> dates = [['2-1-21', '3-1-21'],
...         ['2-2-21', '3-2-21'],
...         ['2-3-21', '3-3-21'],
...         ['2-4-21', '3-4-21']]
>>> df = pd.DataFrame(dates, columns=['index', "days"])
>>> dt_nan_dc = DateTimeNaNDataCheck()
>>> assert dt_nan_dc.validate(df) == {'warnings': [], 'errors': [], 'actions': []}

The first value in the column “index” is replaced with NaT, which will raise an error in this data check.

>>> dates[0][0] = np.datetime64('NaT')
>>> df = pd.DataFrame(dates, columns=['index', "days"])
>>> assert dt_nan_dc.validate(df) == {
...     'warnings': [],
...     'errors': [{'message': 'Input datetime column(s) (index) contains NaN values. Please impute NaN values or drop these rows or columns.',
...                 'data_check_name': 'DateTimeNaNDataCheck',
...                 'level': 'error',
...                 'details': {'columns': ['index'], 'rows': None},
...                 'code': 'DATETIME_HAS_NAN'}],
...     'actions': []}

The value None will be treated the same way.

>>> dates[0][1] = None
>>> df = pd.DataFrame(dates, columns=['index', "days"])
>>> assert dt_nan_dc.validate(df) == {
...     'warnings': [],
...     'errors': [{'message': 'Input datetime column(s) (index, days) contains NaN values. Please impute NaN values or drop these rows or columns.',
...                 'data_check_name': 'DateTimeNaNDataCheck',
...                 'level': 'error',
...                 'details': {'columns': ['index', 'days'], 'rows': None},
...                 'code': 'DATETIME_HAS_NAN'}],
...     'actions': []}

As will pd.NA.

>>> dates[0][1] = pd.NA
>>> df = pd.DataFrame(dates, columns=['index', "days"])
>>> assert dt_nan_dc.validate(df) == {
...     'warnings': [],
...     'errors': [{'message': 'Input datetime column(s) (index, days) contains NaN values. Please impute NaN values or drop these rows or columns.',
...                 'data_check_name': 'DateTimeNaNDataCheck',
...                 'level': 'error',
...                 'details': {'columns': ['index', 'days'], 'rows': None},
...                 'code': 'DATETIME_HAS_NAN'}],
...     'actions': []}

evalml.data_checks.datetime_nan_data_check.error_contains_nan = Input datetime column(s) ({}) contains NaN values. Please impute NaN values or drop these rows...

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