class sklearn.preprocessing.OrdinalEncoder(categories='auto', dtype=<class 'numpy.float64'>)[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.

Read more in the User Guide.

Changed in version 0.20.1.

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.

categories_list of arrays

The categories of each feature determined during fitting (in order of the features in X and corresponding with the output of transform).

See also


Performs a one-hot encoding of categorical features.


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


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.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)


fit(self, X[, y])

Fit the OrdinalEncoder to X.

fit_transform(self, X[, y])

Fit to data, then transform it.

get_params(self[, deep])

Get parameters for this estimator.

inverse_transform(self, X)

Convert the data back to the original representation.

set_params(self, \*\*params)

Set the parameters of this estimator.

transform(self, X)

Transform X to ordinal codes.

__init__(self, categories='auto', dtype=<class 'numpy.float64'>)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y=None)[source]

Fit the OrdinalEncoder to X.

Xarray-like, shape [n_samples, n_features]

The data to determine the categories of each feature.


Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline.

fit_transform(self, 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.

Xnumpy array of shape [n_samples, n_features]

Training set.

ynumpy array of shape [n_samples]

Target values.


Additional fit parameters.

X_newnumpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(self, deep=True)[source]

Get parameters for this estimator.

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

paramsmapping of string to any

Parameter names mapped to their values.

inverse_transform(self, X)[source]

Convert the data back to the original representation.

Xarray-like or sparse matrix, shape [n_samples, n_encoded_features]

The transformed data.

X_trarray-like, shape [n_samples, n_features]

Inverse transformed array.

set_params(self, **params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.


Estimator parameters.


Estimator instance.

transform(self, X)[source]

Transform X to ordinal codes.

Xarray-like, shape [n_samples, n_features]

The data to encode.

X_outsparse matrix or a 2-d array

Transformed input.