email#

class pdcleaner.detection.web.email(obj, detector=None)[source]#

Bases: pattern

‘email’: Detect strings that do not match an email.

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

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

This detection method flags values as potential errors wherever the corresponding Series element does not match an email.

Note

Missing values (NaN) are not treated as errors

Examples

>>> series = pd.Series(['john_856_doe@gmail.com','john_doe','np.nan','john?doe@gmail.com'])
>>> detector = series.cleaner.detect.email()
>>> print(detector.detected())
1              john_doe
2                np.nan
3    john?doe@gmail.com
dtype: object

Attributes Summary

`case`	Case sensitivity
`flags`	Usage of Regex module flags
`index`	Indices of the rows detected as errors
`mode`	'match', 'fullmatch' or 'contains'
`n_errors`	Number of rows detected as errors
`name`
`obj`	The object (Series or DataFrame) containing the data to which the detection is applied
`pattern`	Character sequence or regular expression used to detect errors

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

case#: Case sensitivity

flags#: Usage of Regex module flags

index#: Indices of the rows detected as errors

mode#: ‘match’, ‘fullmatch’ or ‘contains’

n_errors#: Number of rows detected as errors

name = 'email'#

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

pattern#: Character sequence or regular expression used to detect errors

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