castable#

class pdcleaner.detection.types.castable(obj, detector=None, target=None, **kwargs)[source]#

Bases: _SeriesDetector

Detect elements that cannot be casted into target type.

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

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

This detection method flags elements in an object Series as errors when they can not be converted (casted) into a given type.

For example,

1.2 is not castable as an integer
ABC is not castable as a number/float
2022-02-28 is castable as a date, but 2022-02-31 is not

The expected thousands and decimal separators can be customized, so that, for example: 100,000.0 can be seen as 100000.

Note

NA values are not treated as errors.

Parameters:

target ({"int", "number", "date", "boolean"}, required parameter) – The target type that the value will be casted into
decimal (str, Optional) – Specific decimal separator in case of number type
thousands (str, Optional) – Specific thousand separator in case of number type
bool_values (dict,Optional) – Specific value of True and False in case of boolean type

Raises:

ValueError – When target is not among the allowed values, or target is not defined.
ValueError – When detector is applied to float or int series

Examples

>>> import pandas as pd
>>> import pdcleaner

>>> series = pd.Series(['1','2.0','1.2','A','100', '22/05/1975'], dtype='object')
>>> detector = series.cleaner.detect('castable', target='int')
>>> print(detector.is_error())
0    False
1    False
2     True
3     True
4    False
5     True
dtype: bool

>>> detector = series.cleaner.detect('castable', target='date')
>>> print(detector.is_error())
0     True
1     True
2     True
3     True
4     True
5    False
dtype: bool

>>> detector = series.cleaner.detect('castable', target='number')
>>> detector.is_error()
0    False
1    False
2    False
3    False
4    False
5     True
dtype: bool

>>> series = pd.Series(['Yes','No','No','Yes','Ok', 'Nok'], dtype='object')
>>> detector = series.cleaner.detect('castable', target='boolean',
                                            bool_values={"Yes":True, "No":False})
>>> detector.is_error()
0    False
1    False
2    False
3    False
4     True
5     True
dtype: bool

Missing values are not treated as errors.

>>> series = pd.Series(['1','2.0','1.2','A','100', np.nan], dtype='object')
>>> detector = series.cleaner.detect('castable', target='number')
>>> print(detector.is_error())
0    False
1    False
2    False
3     True
4    False
5    False
dtype: bool

Attributes Summary

`bool_values`	Specific value for True and False
`decimal`	Specific decimal separator
`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
`target`	Target type that value will be checked
`thousands`	Specific thousand separator

Methods Summary

`check_separators`(series)	Method to replace specific separator before applying detector
`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

bool_values#: Specific value for True and False

decimal#: Specific decimal separator

index#: Indices of the rows detected as errors

n_errors#: Number of rows detected as errors

name = 'castable'#

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

target#: Target type that value will be checked

thousands#: Specific thousand separator

Methods Documentation

check_separators(series: Series) → Series[source]#: Method to replace specific separator before applying detector

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