Count Encoder

class category_encoders.count.CountEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, handle_unknown='value', handle_missing='value', min_group_size=None, combine_min_nan_groups=None, min_group_name=None, normalize=False)[source]

Count encoding for categorical features.

For a given categorical feature, replace the names of the groups with the group counts.

Parameters:

verbose: int: integer indicating verbosity of output. 0 for none.
cols: list: a list of columns to encode, if None, all string and categorical columns will be encoded.
drop_invariant: bool: boolean for whether or not to drop columns with 0 variance.
return_df: bool: boolean for whether to return a pandas DataFrame from transform (otherwise it will be a numpy array).
handle_missing: str: how to handle missing values at fit time. Options are ‘error’, ‘return_nan’, and ‘value’. Default ‘value’, which treat NaNs as a countable category at fit time.
handle_unknown: str, int or dict of {columnoption, …}.: how to handle unknown labels at transform time. Options are ‘error’ ‘return_nan’, ‘value’ and int. Defaults to None which uses NaN behaviour specified at fit time. Passing an int will fill with this int value.
normalize: bool or dict of {columnbool, …}.: whether to normalize the counts to the range (0, 1). See Pandas value_counts for more details.
min_group_size: int, float or dict of {columnoption, …}.: the minimal count threshold of a group needed to ensure it is not combined into a “leftovers” group. Default value is 0.01. If float in the range (0, 1), min_group_size is calculated as int(X.shape[0] * min_group_size). Note: This value may change type based on the normalize variable. If True this will become a float. If False, it will be an int.
min_group_name: None, str or dict of {columnoption, …}.: Set the name of the combined minimum groups when the defaults become too long. Default None. In this case the category names will be joined alphabetically with a _ delimiter. Note: The default name can be long and may keep changing, for example, in cross-validation.
combine_min_nan_groups: bool or dict of {columnbool, …}.: whether to combine the leftovers group with NaN group. Default True. Can also be forced to combine with ‘force’ meaning small groups are effectively counted as NaNs. Force can only be used when ‘handle_missing’ is ‘value’ or ‘error’. Note: Will not force if it creates a binary or invariant column.

Methods

`combine_min_categories`(X)	Combine small categories into a single category.
`fit`(X[, y])	Fits the encoder according to X and y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_feature_names_in`()	Returns the names of all input columns present when fitting.
`get_feature_names_out`([input_features])	Returns the names of all transformed / added columns.
`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.
`set_transform_request`(*[, override_return_df])	Request metadata passed to the `transform` method.
`transform`(X[, override_return_df])	Perform the transformation to new categorical data.

get_feature_names

combine_min_categories(X)[source]: Combine small categories into a single category.

fit(X, y=None, **kwargs)

Fits the encoder according to X and y.

Parameters:

Xarray-like, shape = [n_samples, n_features]: Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like, shape = [n_samples]: Target values.

Returns:

selfencoder: Returns self.

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.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

get_feature_names_in() → List[str]: Returns the names of all input columns present when fitting. These columns are necessary for the transform step.

get_feature_names_out(input_features=None) → ndarray

Returns the names of all transformed / added columns.

Note that in sklearn the get_feature_names_out function takes the feature_names_in as an argument and determines the output feature names using the input. A fit is usually not necessary and if so a NotFittedError is raised. We just require a fit all the time and return the fitted output columns.

Returns:

feature_names: np.ndarray: A numpy array with all feature names transformed or added. Note: potentially dropped features (because the feature is constant/invariant) are not included!

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_output(*, transform=None)

Set output container.

See sphx_glr_auto_examples_miscellaneous_plot_set_output.py 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)

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.

set_transform_request(*, override_return_df: bool | None | str = '$UNCHANGED$') → CountEncoder

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to transform.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

override_return_dfstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for override_return_df parameter in transform.

Returns:

selfobject: The updated object.

transform(X, override_return_df=False)

Perform the transformation to new categorical data.

Parameters:

Xarray-like, shape = [n_samples, n_features]
override_return_dfbool: override self.return_df to force to return a data frame

Returns:

parray or DataFrame, shape = [n_samples, n_features_out]: Transformed values with encoding applied.