value#

class pdcleaner.detection.values.value(obj, detector=None, value=None, check_type=True, forbidden=False)[source]#

Bases: _SeriesDetector

Detect class values different from a value.

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

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

This detection method flags values as potential errors wherever the corresponding Series element is different or not from a given value.

Note

NA values are not treated as errors.

Parameters:

value (value) – Authorized value
forbidden (bool (Default: False)) – If forbidden=True, errors are elements equal to value
check_type (Bool (Default: True)) – Checks the type of the value if True (3.0 is not the same type as 3)

Raises:

ValueError – when value is None

Examples

>>> series = pd.Series(['cat','cat','dog','bird'])
>>> detector = series.cleaner.detect.value(value='cat')
>>> print(detector.is_error())
0    False
1    False
2     True
3     True
dtype: bool

By default, the type of value and data is checked and must be identical

>>> series = pd.Series([5, 5.0])
>>> detector = series.cleaner.detect.value(value=5)
>>> print(detector.is_error())
0    False
1    True
dtype: bool

>>> series = pd.Series([5, 5.0])
>>> detector = series.cleaner.detect.value(value=5, check_type=False)
>>> print(detector.is_error())
0    False
1    False
dtype: bool

Missing values are not treated as errors.

>>> series = pd.Series(['cat',np.nan,'dog','bird'])
>>> detector = series.cleaner.detect.value(value='cat')
>>> print(detector.is_error())
0    False
1    False
2     True
3     True
dtype: bool

Use the forbidden=True argument to detect a given value as an error

>>> series= pd.Series([1, 2, 3])
>>> detector = series.cleaner.detect('value', value=1, forbidden=True)
>>> print(detector.is_error())
0     True
1    False
2    False
dtype: bool

Attributes Summary

`check_type`	Authorized value
`forbidden`	Is given value a forbidden one (or an expected) ?
`index`	Indices of the rows detected as errors
`n_errors`	Number of rows detected as errors
`name`
`obj`	The object (Series or DataFrame) containing the data to which the detection is applied
`value`	Authorized value

Methods Summary

`detected`()	Series or DataFrame containing only the detected errors
`has_errors`()	Returns True if any error has been detected, False otherwise
`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

check_type#: Authorized value

forbidden#: Is given value a forbidden one (or an expected) ?

index#: Indices of the rows detected as errors

n_errors#: Number of rows detected as errors

name = 'value'#

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

value#: Authorized value

Methods Documentation

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

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

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