sklearn.preprocessing.OrdinalEncoder  
class sklearn.preprocessing.OrdinalEncoder(*, categories='auto', dtype=<class 'numpy.float64'>, handle_unknown='error', unknown_value=None) [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.  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.      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) [source]
 
Fit the OrdinalEncoder to X.  Parameters 
 
Xarray-like, 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 
 self
   
  
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_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 or sparse matrix, shape [n_samples, n_encoded_features] 

The transformed data.    Returns 
 
X_trarray-like, shape [n_samples, n_features] 

Inverse transformed array.     
  
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, shape [n_samples, n_features] 

The data to encode.    Returns 
 
X_outsparse matrix or a 2-d array 

Transformed input.     
 
 Examples using sklearn.preprocessing.OrdinalEncoder
 
  Categorical Feature Support in Gradient Boosting  

  Combine predictors using stacking  

  Poisson regression and non-normal loss