skrub.DatetimeEncoder#

Usage examples at the bottom of this page.

class skrub.DatetimeEncoder(*, resolution='hour', add_day_of_the_week=False, add_total_seconds=True, errors='coerce')[source]#

Transforms each datetime column into several numeric columns for temporal features (e.g year, month, day…).

If the dates are timezone aware, all the features extracted will correspond to the provided timezone.

Parameters:
resolution{“year”, “month”, “day”, “hour”, “minute”, “second”,

“microsecond”, “nanosecond”, None}, default=”hour” Extract up to this resolution. E.g., resolution="day" generates the features “year”, “month”, “day” only. If None, no such feature will be created (but day of the week and total seconds may still be extracted, see below).

add_day_of_the_weekbool, default=False

Add day of the week feature as a numerical feature from 0 (Monday) to 6 (Sunday).

add_total_secondsbool, default=True

Add the total number of seconds since Epoch.

errors{‘coerce’, ‘raise’}, default=”coerce”

During transform: - If "coerce", then invalid parsing will be set as pd.NaT. - If "raise", then invalid parsing will raise an exception.

See also

GapEncoder

Encode dirty categories (strings) by constructing latent topics with continuous encoding.

MinHashEncoder

Encode string columns as a numeric array with the minhash method.

SimilarityEncoder

Encode string columns as a numeric array with n-gram string similarity.

Examples

>>> enc = DatetimeEncoder(add_total_seconds=False)
>>> X = [['2022-10-15'], ['2021-12-25'], ['2020-05-18'], ['2019-10-15 12:00:00']]
>>> enc.fit(X)
DatetimeEncoder(add_total_seconds=False)

The encoder will output a transformed array with four columns (“year”, “month”, “day”, “hour”):

>>> enc.transform(X)
array([[2022.,   10.,   15.,    0.],
       [2021.,   12.,   25.,    0.],
       [2020.,    5.,   18.,    0.],
       [2019.,   10.,   15.,   12.]])
Attributes:
column_indices_list of int

Indices of the datetime-parsable columns.

index_to_format_dict[int, str]

Mapping from column indices to their datetime formats.

index_to_features_dict[int, list[str]]

Dictionary mapping the column names to the list of datetime features extracted for each column.

n_features_out_int

Number of features of the transformed data.

Methods

fit(X[, y])

Fit the instance to X.

fit_transform(X[, y])

Fit to data, then transform it.

get_feature_names_out([input_features])

Get output feature names for transformation.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

transform(X[, y])

Transform X by replacing each datetime column with corresponding numerical features.
fit(X, y=None)[source]#

Fit the instance to X.

Select datetime-parsable columns and generate the list of datetime feature to extract.

Parameters:
Xarray_like, shape (n_samples, n_features)

Input data. Columns that can’t be converted into pandas.DatetimeIndex and numerical values will be dropped.

yNone

Unused, only here for compatibility.

Returns:
DatetimeEncoder

Fitted DatetimeEncoder instance (self).

fit_transform(X, y=None, **fit_params)[source]#

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.

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.

Feature names are formatted like: “<column_name>_<new_feature>” if the original data has column names, otherwise with format “<column_index>_<new_feature>” where <new_feature> is one of {“year”, “month”, “day”, “hour”, “minute”, “second”, “microsecond”, “nanosecond”, “day_of_week”}.

Parameters:
input_featuresNone

Unused, only here for compatibility.

Returns:
feature_nameslist of str

List of 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_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:
transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

  • “default”: Default output format of a transformer

  • “pandas”: DataFrame output

  • None: Transform configuration is unchanged

Returns:
selfestimator instance

Estimator instance.

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.

transform(X, y=None)[source]#

Transform X by replacing each datetime column with corresponding numerical features.

Parameters:
Xarray_like of shape (n_samples, n_features)

The data to transform, where each column is a datetime feature.

yNone

Unused, only here for compatibility.

Returns:
X_outndarray of shape (n_samples, n_features_out_)

Transformed input.

Examples using skrub.DatetimeEncoder#

Encoding: from a dataframe to a numerical matrix for machine learning

Encoding: from a dataframe to a numerical matrix for machine learning

Handling datetime features with the DatetimeEncoder

Handling datetime features with the DatetimeEncoder

Self-aggregation on MovieLens

Self-aggregation on MovieLens