DatetimeOrdinal

class feature_engine.datetime.DatetimeOrdinal(variables=None, missing_values='raise', start_date=None, drop_original=True)

DatetimeOrdinal transforms datetime variables into their ordinal representation. The ordinal representation is an integer value representing the number of days since January 1, 0001 in the Gregorian calendar.

Optionally, a start_date can be provided to set a custom reference point, making the ordinal values relative to this date (starting from 1).

More details in the User Guide.

Parameters

variables: str, list, default=None

List of the variables to convert into ordinal. If None, the transformer will find and select all datetime variables, including variables of type object that can be converted to datetime.

missing_values: string, default=’raise’

Indicates if missing values should be ignored or raised. If ‘raise’ the transformer will return an error if the datasets passed to fit or transform contain missing values. If ‘ignore’, missing data will be ignored when performing the transformation.

start_date: str, datetime.datetime, default=None

A reference date from which the ordinal values will be calculated. If provided, the ordinal value of start_date will be 1, the day after will be 2, and so on. Days before start_date will take negative values. If None, the transformation will represent the number of days since January 1, 0001. start_date can be a string (e.g., “YYYY-MM-DD”) or a datetime object.

drop_original: bool, default=True

If True, the original datetime variables will be dropped from the dataframe after the transformation.

Attributes

variables_:

List of variables to convert into ordinals.

start_date_ordinal_:

The ordinal value of the provided start_date, if applicable.

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.

See also

feature_engine.datetime.DatetimeFeatures

feature_engine.datetime.DatetimeSubtraction

Examples

>>> import pandas as pd
>>> from feature_engine.datetime import DatetimeOrdinal
>>> X = pd.DataFrame(dict(date = ["2023-01-01", "2023-01-02", "2023-01-03"]))
>>> dtf = DatetimeOrdinal(start_date="2023-01-01")
>>> dtf.fit(X)
>>> dtf.transform(X)
   date_ordinal
0             1
1             2
2             3

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:	Add the ordinal datetime features.

fit(X, y=None)

This transformer does not learn any parameter.

Finds datetime variables or checks that the variables selected by the user can be converted to datetime.

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=None

It is not needed in this transformer. You can pass y or None.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

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. Pass only if the estimator accepts additional params in its fit method.

Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)

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, then feature_names_in_ is used as feature names in.

• If an array or list, then input_features must match feature_names_in_.

Returns

feature_names_out: list

Transformed feature names.

rtype

List[Union[str, int]] ..

get_metadata_routing()

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)

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)

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.

transform(X)

Calculate ordinal representation of datetime features and add them to the dataframe.

Parameters

X: pandas dataframe of shape = [n_samples, n_features]

The data to transform.

Returns

X_new: Pandas dataframe, shape = [n_samples, n_features x n_df_features]

The dataframe with the original variables plus the new features.

rtype

DataFrame ..