`sklearn.preprocessing`.OrdinalEncoder¶

class sklearn.preprocessing.OrdinalEncoder(*, categories='auto', dtype=<class 'numpy.float64'>, handle_unknown='error', unknown_value=None, encoded_missing_value=nan)[source]¶

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.

See also

OneHotEncoder: Performs a one-hot encoding of categorical features.
LabelEncoder: Encodes target labels with values between 0 and n_classes-1.

Notes

With a high proportion of nan values, inferring categories becomes slow with Python versions before 3.10. The handling of nan values was improved from Python 3.10 onwards, (c.f. bpo-43475).

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)

By default, OrdinalEncoder is lenient towards missing values by propagating them.

>>> import numpy as np
>>> X = [['Male', 1], ['Female', 3], ['Female', np.nan]]
>>> enc.fit_transform(X)
array([[ 1.,  0.],
       [ 0.,  1.],
       [ 0., nan]])

You can use the parameter encoded_missing_value to encode missing values.

>>> enc.set_params(encoded_missing_value=-1).fit_transform(X)
array([[ 1.,  0.],
       [ 0.,  1.],
       [ 0., -1.]])

Methods

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

fit(X, y=None)[source]¶

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)[source]¶

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)[source]¶

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].
If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:

feature_names_outndarray of str objects: Same as input features.

get_params(deep=True)[source]¶

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)[source]¶

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_output(*, transform=None)[source]¶

Set output container.

See Introducing the set_output API 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)[source]¶

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)[source]¶

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`¶

Release Highlights for scikit-learn 1.2

Categorical Feature Support in Gradient Boosting

Combine predictors using stacking

Time-related feature engineering

Poisson regression and non-normal loss

Partial Dependence and Individual Conditional Expectation Plots

Permutation Importance vs Random Forest Feature Importance (MDI)

sklearn.preprocessing.OrdinalEncoder¶

Examples using sklearn.preprocessing.OrdinalEncoder¶

`sklearn.preprocessing`.OrdinalEncoder¶

Examples using `sklearn.preprocessing.OrdinalEncoder`¶