OneHotEncoder

class feature_engine.encoding.OneHotEncoder(top_categories=None, drop_last=False, drop_last_binary=False, variables=None, ignore_format=False)

The OneHotEncoder() replaces categorical variables by a set of binary variables representing each one of the unique categories in the variable.

The encoder has the option to create k or k-1 binary variables, where k is the number of unique categories.

The encoder has the additional option to generate binary variables only for the most popular categories, that is, the categories that are shared by the majority of the observations in the dataset. This behaviour can be specified with the parameter top_categories.

The encoder will encode only categorical variables by default (type ‘object’ or ‘categorical’). You can pass a list of variables to encode. Alternatively, the encoder will find and encode all categorical variables (type ‘object’ or ‘categorical’).

With ignore_format=True you have the option to encode numerical variables as well. The procedure is identical, you can either enter the list of variables to encode, or the transformer will automatically select all variables.

The encoder first finds the categories to be encoded for each variable (fit). The encoder then creates one dummy variable per category for each variable (transform).

Note

New categories in the data to transform, that is, those that did not appear in the training set, will be ignored (no binary variable will be created for them). This means that observations with categories not present in the train set, will be encoded as 0 in all the binary variables.

Also Note

The original categorical variables are removed from the returned dataset when we apply the transform() method. In their place, the binary variables are returned.

More details in the User Guide.

Parameters

top_categories: int, default=None

If None, dummy variables will be created for each unique category of the variable. Alternatively, we can indicate in the number of most frequent categories to encode. In this case, dummy variables will be created only for those popular categories and the rest will be ignored, i.e., they will show the value 0 in all the binary variables. Note that if top_categories is not None, the parameter drop_last is ignored.

drop_last: boolean, default=False

Only used if top_categories = None. It indicates whether to create dummy variables for all the categories (k dummies), or if set to True, it will ignore the last binary variable and return k-1 dummies.

drop_last_binary: boolean, default=False

Whether to return 1 or 2 dummy variables for binary categorical variables. When a categorical variable has only 2 categories, then the second dummy variable created by one hot encoding can be completely redundant. Setting this parameter to True, will ensure that for every binary variable in the dataset, only 1 dummy is created.

variables: list, default=None

The list of categorical variables that will be encoded. If None, the encoder will find and transform all variables of type object or categorical by default. You can also make the transformer accept numerical variables, see the parameter ignore_format.

ignore_format: bool, default=False

This transformer operates only on variables of type object or categorical. To override this behaviour and allow the transformer to transform numerical variables as well, set to True.

If ignore_format is False, the encoder will automatically select variables of type object or categorical, or check that the variables entered by the user are of type object or categorical. If True, the encoder will select all variables or accept all variables entered by the user, including those cast as numeric.

In short, set to True when you want to encode numerical variables.

Attributes

encoder_dict_:

Dictionary with the categories for which dummy variables will be created.

variables_:

The group of variables that will be transformed.

variables_binary_:

List with binary variables identified in the data. That is, variables with only 2 categories.

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.

Notes

If the variables are intended for linear models, it is recommended to encode into k-1 or top categories. If the variables are intended for tree based algorithms, it is recommended to encode into k or top n categories. If feature selection will be performed, then also encode into k or top n categories. Linear models evaluate all features during fit, while tree based models and many feature selection algorithms evaluate variables or groups of variables separately. Thus, if encoding into k-1, the last variable / category will not be examined.

References

One hot encoding of top categories was described in the following article:

1

Niculescu-Mizil, et al. “Winning the KDD Cup Orange Challenge with Ensemble Selection”. JMLR: Workshop and Conference Proceedings 7: 23-34. KDD 2009 http://proceedings.mlr.press/v7/niculescu09/niculescu09.pdf

Examples

>>> import pandas as pd
>>> from feature_engine.encoding import OneHotEncoder
>>> X = pd.DataFrame(dict(x1 = [1,2,3,4], x2 = ["a", "a", "b", "c"]))
>>> ohe = OneHotEncoder()
>>> ohe.fit(X)
>>> ohe.transform(X)
   x1  x2_a  x2_b  x2_c
0   1     1     0     0
1   2     1     0     0
2   3     0     1     0
3   4     0     0     1

Methods

fit:	Learn the unique categories per variable
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:	Replace the categorical variables by the binary variables.

fit(X, y=None)

Learns the unique categories per variable. If top_categories is indicated, it will learn the most popular categories. Alternatively, it learns all unique categories per variable.

Parameters

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

The training input samples. Can be the entire dataframe, not just seleted variables.

y: pandas series, default=None

Target. It is not needed in this encoded. 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.

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_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)

inverse_transform is not implemented for this transformer.

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)

Replaces the categorical variables by the binary variables.

Parameters

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

The data to transform.

Returns

X_new: pandas dataframe.

The transformed dataframe. The shape of the dataframe will be different from the original as it includes the dummy variables in place of the of the original categorical ones.

rtype

DataFrame ..