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.
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See also
sklearn.preprocessing.OneHotEncoder
sklearn.preprocessing.LabelEncoder
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(categories='auto', dtype=<... 'numpy.float64'>) >>> 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 (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. |
__init__(categories=’auto’, dtype=<class ‘numpy.float64’>)
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fit(X, y=None)
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Fit the OrdinalEncoder to X.
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fit_transform(X, y=None, **fit_params)
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Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
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get_params(deep=True)
[source]
Get parameters for this estimator.
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inverse_transform(X)
[source]
Convert the data back to the original representation.
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set_params(**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.
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transform(X)
[source]
Transform X to ordinal codes.
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© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html