Class ColumnTransformer (1.29.0)

ColumnTransformer(
    transformers: typing.Iterable[
        typing.Tuple[
            str,
            typing.Union[
                bigframes.ml.preprocessing.OneHotEncoder,
                bigframes.ml.preprocessing.StandardScaler,
                bigframes.ml.preprocessing.MaxAbsScaler,
                bigframes.ml.preprocessing.MinMaxScaler,
                bigframes.ml.preprocessing.KBinsDiscretizer,
                bigframes.ml.preprocessing.LabelEncoder,
                bigframes.ml.preprocessing.PolynomialFeatures,
                bigframes.ml.impute.SimpleImputer,
                bigframes.ml.compose.SQLScalarColumnTransformer,
            ],
            typing.Union[str, typing.Iterable[str]],
        ]
    ]
)

Applies transformers to columns of BigQuery DataFrames.

This estimator allows different columns or column subsets of the input to be transformed separately, and the features generated by each transformer will be concatenated to form a single feature space. This is useful for heterogeneous or columnar data to combine several feature extraction mechanisms or transformations into a single transformer.

Properties

transformers_

The collection of transformers as tuples of (name, transformer, column).

Methods

repr

__repr__()

Print the estimator's constructor with all non-default parameter values.

fit

fit(
    X: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    y=None,
) -> bigframes.ml.compose.ColumnTransformer

Fit all transformers using X.

Parameter
Name	Description
`X`	`bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series` The Series or DataFrame of shape (n_samples, n_features). Training vector, where `n_samples` is the number of samples and `n_features` is the number of features.

Returns
Type	Description
`ColumnTransformer`	Fitted estimator.

fit_transform

fit_transform(
    X: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    y: typing.Optional[
        typing.Union[
            bigframes.dataframe.DataFrame,
            bigframes.series.Series,
            pandas.core.frame.DataFrame,
            pandas.core.series.Series,
        ]
    ] = None,
) -> bigframes.dataframe.DataFrame

API documentation for fit_transform method.

get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

Parameter
Name	Description
`deep`	`bool, default True` Default `True`. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
Type	Description
`Dictionary`	A dictionary of parameter names mapped to their values.

to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._T

Save the transformer as a BigQuery model.

Parameters
Name	Description
`model_name`	`str` The name of the model.
`replace`	`bool, default False` Determine whether to replace if the model already exists. Default to False.

transform

transform(
    X: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ]
) -> bigframes.dataframe.DataFrame

Transform X separately by each transformer, concatenate results.

Parameter
Name	Description
`X`	`bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series` The Series or DataFrame to be transformed by subset.

Returns
Type	Description
`bigframes.dataframe.DataFrame`	Transformed result.