LabelEncoderTransform

class LabelEncoderTransform(in_column: str, out_column: Optional[str] = None, strategy: str = ImputerMode.mean)

Bases: etna.transforms.base.Transform

Encode categorical feature with value between 0 and n_classes-1.

Init LabelEncoderTransform.

Parameters

• in_column (str) – Name of column to be transformed

• out_column (Optional[str]) – Name of added column. If not given, use self.__repr__()

• strategy (str) –

  Filling encoding in not fitted values:

  • If “new_value”, then replace missing values with ‘-1’

  • If “mean”, then replace missing values using the mean in encoded column

  • If “none”, then replace missing values with None

Inherited-members

Methods

fit(df)	Fit Label encoder.
fit_transform(df)	May be reimplemented.
inverse_transform(df)	Inverse transforms dataframe.
transform(df)	Encode the in_column by fitted Label encoder.

fit(df: pandas.core.frame.DataFrame) → etna.transforms.encoders.categorical.LabelEncoderTransform

Fit Label encoder.

Parameters

df (pandas.core.frame.DataFrame) – Dataframe with data to fit the transform

Returns

Fitted transform

Return type

etna.transforms.encoders.categorical.LabelEncoderTransform

transform(df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

Encode the in_column by fitted Label encoder.

Parameters

df (pandas.core.frame.DataFrame) – Dataframe with data to transform

Returns

Dataframe with column with encoded values

Return type

pandas.core.frame.DataFrame