date_range#

class pdcleaner.detection.datetimes.date_range(obj, detector=None, lower=Timestamp('1677-09-21 00:12:43.145224193'), upper=Timestamp('2262-04-11 23:47:16.854775807'), inclusive='both')[source]#

Bases: _DateTypeSeriesDetector

Detect if date value is between a given range.

Intended to be used by the detect method with the keyword ‘date_range’

>>> series.cleaner.detect.date_range(...)
>>> series.cleaner.detect('date_range',...)

This detection method flags values as potential errors wherever the corresponding Series element is outside the date range.

Note

NA values are not treated as errors.

Parameters:

lower (datetime) – Lower bound
upper (datetime) – Upper bound
inclusive ({“both”, “neither”, “left”, “right”}, default "both") – Include boundaries. Whether to set each bound as closed or open.

Examples

>>> series = pd.Series(['2022-10-01', '2021-06-11', '2019-04-03',' 2020-09-25'])
>>> series= pd.to_datetime(series)
>>> detector = series.cleaner.detect.date_range(lower='2020-06-15', upper='2022-08-05')
>>> print(detector.is_error())
0     True
1    False
2     True
3    False
dtype: bool

With only one bound specified

>>> detector = series.cleaner.detect.date_range(upper='2022-08-05')
>>> print(detector.is_error())
0     True
1    False
2    False
3    False
dtype: bool

Attributes Summary

`inclusive`	Keyword to indicate if boundaries are included {“both”, “neither”, “left”, “right”}
`index`	Indices of the rows detected as errors
`lower`	Lower bound
`n_errors`	Number of rows detected as errors
`name`
`obj`	The object (Series or DataFrame) containing the data to which the detection is applied
`upper`	Upper bound

Methods Summary

`detected`()	Series or DataFrame containing only the detected errors
`has_errors`()	Returns True if any error has been detected, False otherwise
`is_date`(date_str)	Check if value is in date format
`is_error`()	Return a boolean same-sized object indicating if the values are flagged as errors
`not_error`()	Return a boolean same-sized object indicating if the values are NOT flagged as errors
`report`()	prints a detection report
`valid`()	Series or DataFrame containing only the valid values

Attributes Documentation

inclusive#: Keyword to indicate if boundaries are included {“both”, “neither”, “left”, “right”}

index#: Indices of the rows detected as errors

lower#: Lower bound

n_errors#: Number of rows detected as errors

name = 'date_range'#

obj#: The object (Series or DataFrame) containing the data to which the detection is applied

upper#: Upper bound

Methods Documentation

detected()#: Series or DataFrame containing only the detected errors

has_errors() → bool#: Returns True if any error has been detected, False otherwise

static is_date(date_str)[source]#: Check if value is in date format

is_error() → Series#: Return a boolean same-sized object indicating if the values are flagged as errors

not_error() → Series#: Return a boolean same-sized object indicating if the values are NOT flagged as errors

report()#: prints a detection report

valid()#: Series or DataFrame containing only the valid values