DatetimeSubtraction#
- class feature_engine.datetime.DatetimeSubtraction(variables=None, reference=None, new_variables_names=None, output_unit='D', missing_values='ignore', drop_original=False, dayfirst=False, yearfirst=False, utc=None, format=None)[source]#
DatetimeSubtraction() applies datetime subtraction between a group of datetime variables and one or more datetime features, adding the resulting variables to the dataframe.
DatetimeSubtraction() works with variables cast as datetime or object. It subtracts the variables listed in the parameter
reference
from those listed in the parametervariables
.More details in the User Guide.
- Parameters
- variables: list
The list of datetime variables that the reference variables will be subtracted from (left side of the subtraction operation).
- reference: list
The list of datetime reference variables that will be subtracted from
variables
(right side of the subtraction operation).- new_variables_names: list, default=None
Names of the new variables. You have the option to pass a list with the names you’d like to assing to the new variables. If
None
, the transformer will assign arbitrary names.- output_unit: string, default=’D’
The string representation of the output unit of the datetime differences. The default is
D
for day. This parameter is passed tonumpy.timedelta64
. Other possible values areY
for year,M
for month,W
for week,h
for hour,m
for minute,s
for second,ms
for millisecond,us
orμs
for microsecond,ns
for nanosecond,ps
for picosecond,fs
for femtosecond andas
for attosecond.- missing_values: string, default=’raise’
Indicates if missing values should be ignored or raised. If
'raise'
the transformer will return an error if the the datasets tofit
ortransform
contain missing values. If'ignore'
, missing data will be ignored when learning parameters or performing the transformation.- drop_original: bool, default=”False”
If
True
, the variables listed invariables
andreference
will be dropped from the dataframe after the computation of the new features.- dayfirst: bool, default=”False”
Specify a date parse order if arg is str or is list-like. If True, parses dates with the day first, e.g. 10/11/12 is parsed as 2012-11-10. Same as in
pandas.to_datetime
.- yearfirst: bool, default=”False”
Specify a date parse order if arg is str or is list-like. Same as in
pandas.to_datetime
.If True parses dates with the year first, e.g. 10/11/12 is parsed as 2010-11-12.
If both dayfirst and yearfirst are True, yearfirst is preceded.
- utc: bool, default=None
Return UTC DatetimeIndex if True (converting any tz-aware datetime.datetime objects as well). Same as in
pandas.to_datetime
.- format: str, default None
The strftime to parse time, e.g. “%d/%m/%Y”. Check pandas
to_datetime()
for more information on choices. If you have variables with different formats pass “mixed”, to infer the format for each element individually. This is risky, and you should probably use it along with dayfirst, according to pandas’ documentation.
- Attributes
- variables_:
The list with datetime variables from which the variables in
reference
will be substracted. It is created after the transformer corroborates that the variables invariables
are, or can be parsed to datetime.- reference_:
The list with the datetime variables that will be subtracted from
variables_
. It is created after the transformer corroborates that the variables inreference
are, or can be parsed to datetime.- feature_names_in_:
List with the names of features seen during
fit
.- n_features_in_:
The number of features in the train set used in fit.
Examples
>>> import pandas as pd >>> from feature_engine.datetime import DatetimeSubtraction >>> X = pd.DataFrame({ >>> "date1": ["2022-09-18", "2022-10-27", "2022-12-24"], >>> "date2": ["2022-08-18", "2022-08-27", "2022-06-24"]}) >>> dtf = DatetimeSubtraction(variables=["date1"], reference=["date2"]) >>> dtf.fit(X) >>> dtf.transform(X) date1 date2 date1_sub_date2 0 2022-09-18 2022-08-18 31.0 1 2022-10-27 2022-08-27 61.0 2 2022-12-24 2022-06-24 183.0
Methods
fit:
This transformer does not learn parameters.
fit_transform:
Fit to data, then transform it.
get_feature_names_out:
Get output feature names for transformation.
get_params:
Get parameters for this estimator.
set_params:
Set the parameters of this estimator.
transform:
Create new features.
- fit(X, y=None)[source]#
This transformer does not learn any parameter.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features]
The training input samples. Can be the entire dataframe, not just the variables to transform.
- y: pandas Series, or np.array. Default=None.
It is not needed in this transformer. You can pass y or None.
- fit_transform(X, y=None, **fit_params)[source]#
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- Parameters
- Xarray-like of shape (n_samples, n_features)
Input samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_paramsdict
Additional fit parameters.
- Returns
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
- get_feature_names_out(input_features=None)[source]#
Get output feature names for transformation. In other words, returns the variable names of transformed dataframe.
- Parameters
- input_featuresarray or list, default=None
This parameter exits only for compatibility with the Scikit-learn pipeline.
If
None
, thenfeature_names_in_
is used as feature names in.If an array or list, then
input_features
must matchfeature_names_in_
.
- Returns
- feature_names_out: list
Transformed feature names.
- get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- get_params(deep=True)[source]#
Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
- set_params(**params)[source]#
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.