One Hot

class category_encoders.one_hot.OneHotEncoder(verbose=0, cols=None, drop_invariant=False, return_df=True, handle_missing='value', handle_unknown='value', use_cat_names=False)[source]

Onehot (or dummy) coding for categorical features, produces one feature per category, each binary.

Parameters
verbose: int

integer indicating verbosity of the output. 0 for none.

cols: list

a list of columns to encode, if None, all string 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).

use_cat_names: bool

if True, category values will be included in the encoded column names. Since this can result in duplicate column names, duplicates are suffixed with ‘#’ symbol until a unique name is generated. If False, category indices will be used instead of the category values.

handle_unknown: str

options are ‘error’, ‘return_nan’, ‘value’, and ‘indicator’. The default is ‘value’.

‘error’ will raise a ValueError at transform time if there are new categories. ‘return_nan’ will encode a new value as np.nan in every dummy column. ‘value’ will encode a new value as 0 in every dummy column. ‘indicator’ will add an additional dummy column (in both training and test data).

handle_missing: str

options are ‘error’, ‘return_nan’, ‘value’, and ‘indicator’. The default is ‘value’.

‘error’ will raise a ValueError if missings are encountered. ‘return_nan’ will encode a missing value as np.nan in every dummy column. ‘value’ will encode a missing value as 0 in every dummy column. ‘indicator’ will treat missingness as its own category, adding an additional dummy column (whether there are missing values in the training set or not).

References

1

Contrast Coding Systems for Categorical Variables, from

https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/

2

Gregory Carey (2003). Coding Categorical Variables, from

http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf

Attributes
category_mapping
feature_names

Methods

fit(X[, y])

Fits the encoder according to X and y.

fit_transform(X[, y])

Fit to data, then transform it.

get_dummies(X_in)

Convert numerical variable into dummy variables

get_feature_names()

Returns the names of all transformed / added columns.

get_params([deep])

Get parameters for this estimator.

inverse_transform(X_in)

Perform the inverse transformation to encoded data.

reverse_dummies(X, mapping)

Convert dummy variable into numerical variables

set_params(**params)

Set the parameters of this estimator.

transform(X[, override_return_df])

Perform the transformation to new categorical data.

generate_mapping

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 and inverse 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.

kwargs: dict.

additional encoder specific parameters like regularisation.

Attributes
category_mapping
feature_names

Methods

fit(X[, y])

Fits the encoder according to X and y.

fit_transform(X[, y])

Fit to data, then transform it.

get_dummies(X_in)

Convert numerical variable into dummy variables

get_feature_names()

Returns the names of all transformed / added columns.

get_params([deep])

Get parameters for this estimator.

inverse_transform(X_in)

Perform the inverse transformation to encoded data.

reverse_dummies(X, mapping)

Convert dummy variable into numerical variables

set_params(**params)

Set the parameters of this estimator.

transform(X[, override_return_df])

Perform the transformation to new categorical data.

generate_mapping

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_dummies(X_in)[source]

Convert numerical variable into dummy variables

Parameters
X_in: DataFrame
Returns
dummiesDataFrame
get_feature_names() List[str]

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 (because the feature is constant/invariant) are not included!

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.

inverse_transform(X_in)[source]

Perform the inverse transformation to encoded data.

Parameters
X_inarray-like, shape = [n_samples, n_features]
Returns
p: array, the same size of X_in
reverse_dummies(X, mapping)[source]

Convert dummy variable into numerical variables

Parameters
XDataFrame
mapping: list-like

Contains mappings of column to be transformed to it’s new columns and value represented

Returns
numerical: DataFrame
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, 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.