sklearn.preprocessing.OrdinalEncoder

class sklearn.preprocessing.OrdinalEncoder(*, categories='auto', dtype=<class 'numpy.float64'>, handle_unknown='error', unknown_value=None)

Encode categorical features as an integer array.

The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The features are converted to ordinal integers. This results in a single column of integers (0 to n_categories - 1) per feature.

Read more in the User Guide.

New in version 0.20.

Parameters

categories‘auto’ or a list of array-like, default=’auto’

Categories (unique values) per feature:

• ‘auto’ : Determine categories automatically from the training data.

• list : categories[i] holds the categories expected in the ith column. The passed categories should not mix strings and numeric values, and should be sorted in case of numeric values.

The used categories can be found in the categories_ attribute.

dtypenumber type, default np.float64

Desired dtype of output.

handle_unknown{‘error’, ‘use_encoded_value’}, default=’error’

When set to ‘error’ an error will be raised in case an unknown categorical feature is present during transform. When set to ‘use_encoded_value’, the encoded value of unknown categories will be set to the value given for the parameter unknown_value. In inverse_transform, an unknown category will be denoted as None.

New in version 0.24.

unknown_valueint or np.nan, default=None

When the parameter handle_unknown is set to ‘use_encoded_value’, this parameter is required and will set the encoded value of unknown categories. It has to be distinct from the values used to encode any of the categories in fit. If set to np.nan, the dtype parameter must be a float dtype.

New in version 0.24.

Attributes

categories_list of arrays

The categories of each feature determined during fit (in order of the features in X and corresponding with the output of transform). This does not include categories that weren’t seen during fit.

n_features_in_int

Number of features seen during fit.

New in version 1.0.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

See also

OneHotEncoder

Performs a one-hot encoding of categorical features.

LabelEncoder

Encodes target labels with values between 0 and n_classes-1.

Examples

Given a dataset with two features, we let the encoder find the unique values per feature and transform the data to an ordinal encoding.

>>> from sklearn.preprocessing import OrdinalEncoder
>>> enc = OrdinalEncoder()
>>> X = [['Male', 1], ['Female', 3], ['Female', 2]]
>>> enc.fit(X)
OrdinalEncoder()
>>> enc.categories_
[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]
>>> enc.transform([['Female', 3], ['Male', 1]])
array([[0., 2.],
       [1., 0.]])

>>> enc.inverse_transform([[1, 0], [0, 1]])
array([['Male', 1],
       ['Female', 2]], dtype=object)

Methods

fit(X[, y])	Fit the OrdinalEncoder to X.
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
inverse_transform(X)	Convert the data back to the original representation.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Transform X to ordinal codes.

fit(X, y=None)

Fit the OrdinalEncoder to X.

Parameters

Xarray-like of shape (n_samples, n_features)

The data to determine the categories of each feature.

yNone

Ignored. This parameter exists only for compatibility with Pipeline.

Returns

selfobject

Fitted encoder.

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_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)

Convert the data back to the original representation.

Parameters

Xarray-like of shape (n_samples, n_encoded_features)

The transformed data.

Returns

X_trndarray of shape (n_samples, n_features)

Inverse transformed array.

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)

Transform X to ordinal codes.

Parameters

Xarray-like of shape (n_samples, n_features)

The data to encode.

Returns

X_outndarray of shape (n_samples, n_features)

Transformed input.

Examples using sklearn.preprocessing.OrdinalEncoder

Categorical Feature Support in Gradient Boosting

Combine predictors using stacking

Time-related feature engineering

Poisson regression and non-normal loss