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 used when ‘handle_missing’ is ‘value’ or ‘error’. Note: Will not force if it creates an binary or invariant column.

Methods

combine_min_categories(X)

Combine small categories into a single category.

fit(X[, y])

Fit encoder according to X.

fit_transform(X[, y])

Fit to data, then transform it.

get_feature_names()

Returns the names of all transformed / added columns.

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.

transform(X[, y, override_return_df])

Perform the transformation to new categorical data.

combine_min_categories(X)[source]

Combine small categories into a single category.

fit(X, y=None, **kwargs)[source]

Fit encoder according to X.

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.

get_feature_names()[source]

Returns the names of all transformed / added columns.

Returns
feature_names: list

A list with all feature names transformed or added. Note: potentially dropped features are not included!

transform(X, y=None, override_return_df=False)[source]

Perform the transformation to new categorical data.

Parameters
Xarray-like, shape = [n_samples, n_features]
yarray-like, shape = [n_samples]
Returns
parray, shape = [n_samples, n_numeric + N]

Transformed values with encoding applied.