sklearndf.transformation.LabelEncoderDF#
- class sklearndf.transformation.LabelEncoderDF(*args, **kwargs)[source]#
Encode target labels with value between 0 and n_classes-1.
Note
This class is a wrapper around class
sklearn.preprocessing.LabelEncoder
. It provides enhanced support forpandas
data frames, and otherwise delegates all attribute access and method calls to an associatedLabelEncoder
instance.- Bases:
- Metaclasses:
Method summary
Make an unfitted clone of this estimator.
Fit this estimator using the given inputs.
Fit this transformer using the given inputs, then transform the inputs.
Make a new wrapped DF estimator, delegating to a given native estimator that has already been fitted.
See
sklearn.utils.get_metadata_routing()
Get the parameters for this estimator.
Inverse-transform the given inputs.
See
sklearn.utils.set_output()
Set the parameters of this estimator.
Render this object as an expression.
Transform the given inputs.
Attribute summary
Name assigned to an
Index
or aSeries
with the names of the features used to fit aEstimatorDF
.Name assigned to a
Series
with the original feature names before transformation.The pandas column index with the names of the features used to fit this estimator.
A pandas series, mapping the output features resulting from the transformation to the original input features.
A pandas column index with the names of the features produced by this transformer
True
if this object is fitted,False
otherwise.The number of features used to fit this estimator.
The number of outputs used to fit this estimator.
The native estimator that this wrapper delegates to.
The name(s) of the output(s) this estimator was fitted to, or
None
if this estimator was not fitted to any outputs.Definitions
- clone()#
Make an unfitted clone of this estimator.
- Return type:
- Returns:
the unfitted clone
- fit(X, y=None, **fit_params)#
Fit this estimator using the given inputs.
- Parameters:
- Return type:
- Returns:
self
- fit_transform(X, y=None, **fit_params)#
Fit this transformer using the given inputs, then transform the inputs.
- Parameters:
- Return type:
- Returns:
the transformed inputs
- classmethod from_fitted(estimator, features_in, n_outputs)#
Make a new wrapped DF estimator, delegating to a given native estimator that has already been fitted.
- Parameters:
estimator (
LabelEncoder
) – the fitted native estimator to use as the delegatefeatures_in (
Index
) – the column names of X used for fitting the estimatorn_outputs (
int
) – the number of outputs in y used for fitting the estimator
- Return type:
- Returns:
the wrapped data frame estimator
- get_metadata_routing()#
See
sklearn.utils.get_metadata_routing()
- get_params(deep=True)#
Get the parameters for this estimator.
- inverse_transform(X)#
Inverse-transform the given inputs.
The inputs must have the same features as the inputs used to fit this transformer. The features can be provided in any order since they are identified by their column names.
- set_output(*, transform=None)#
See
sklearn.utils.set_output()
- set_params(**params)#
Set the parameters of this estimator.
Valid parameter keys can be obtained by calling
get_params()
.- Parameters:
params (
Any
) – the estimator parameters to set- Return type:
- Returns:
self
- to_expression()#
Render this object as an expression.
- Return type:
- Returns:
the expression representing this object
- transform(X)#
Transform the given inputs.
The inputs must have the same features as the inputs used to fit this transformer. The features can be provided in any order since they are identified by their column names.
- COL_FEATURE = 'feature'#
Name assigned to an
Index
or aSeries
with the names of the features used to fit aEstimatorDF
.
- COL_FEATURE_ORIGINAL = 'feature_original'#
Name assigned to a
Series
with the original feature names before transformation.
- property feature_names_in_: Index#
The pandas column index with the names of the features used to fit this estimator.
- Raises:
AttributeError – this estimator is not fitted
- property feature_names_original_: Series#
A pandas series, mapping the output features resulting from the transformation to the original input features.
The index of the resulting series consists of the names of the output features; the corresponding values are the names of the original input features.
- Raises:
AttributeError – this transformer is not fitted
- property feature_names_out_: Index#
A pandas column index with the names of the features produced by this transformer
- Raises:
AttributeError – this transformer is not fitted
- property n_features_in_: int#
The number of features used to fit this estimator.
- Raises:
AttributeError – this estimator is not fitted
- property n_outputs_: int#
The number of outputs used to fit this estimator.
- Raises:
AttributeError – this estimator is not fitted
- property native_estimator: LabelEncoderDF#
The native estimator that this wrapper delegates to.
- property output_names_: list[str] | None#
The name(s) of the output(s) this estimator was fitted to, or
None
if this estimator was not fitted to any outputs.- Raises:
AttributeError – this estimator is not fitted