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TableTransformer

Bases: ABC

Learn a transformation for a set of columns in a Table and transform another Table with the same columns.

Source code in src/safeds/data/tabular/transformation/_table_transformer.py
class TableTransformer(ABC):
    """Learn a transformation for a set of columns in a `Table` and transform another `Table` with the same columns."""

    def __hash__(self) -> int:
        """
        Return a deterministic hash value for a table transformer.

        Returns
        -------
        hash:
            The hash value.
        """
        added = self.get_names_of_added_columns() if self.is_fitted() else []
        changed = self.get_names_of_changed_columns() if self.is_fitted() else []
        removed = self.get_names_of_removed_columns() if self.is_fitted() else []
        return _structural_hash(self.__class__.__qualname__, self.is_fitted(), added, changed, removed)

    @abstractmethod
    def fit(self, table: Table, column_names: list[str] | None) -> TableTransformer:
        """
        Learn a transformation for a set of columns in a table.

        This transformer is not modified.

        Parameters
        ----------
        table : Table
            The table used to fit the transformer.
        column_names : list[str] | None
            The list of columns from the table used to fit the transformer. If `None`, all columns are used.

        Returns
        -------
        fitted_transformer : TableTransformer
            The fitted transformer.
        """

    @abstractmethod
    def transform(self, table: Table) -> Table:
        """
        Apply the learned transformation to a table.

        The table is not modified.

        Parameters
        ----------
        table : Table
            The table to which the learned transformation is applied.

        Returns
        -------
        transformed_table : Table
            The transformed table.

        Raises
        ------
        TransformerNotFittedError
            If the transformer has not been fitted yet.
        """

    @abstractmethod
    def get_names_of_added_columns(self) -> list[str]:
        """
        Get the names of all new columns that have been added by the transformer.

        Returns
        -------
        added_columns : list[str]
            A list of names of the added columns, ordered as they will appear in the table.

        Raises
        ------
        TransformerNotFittedError
            If the transformer has not been fitted yet.
        """

    @abstractmethod
    def get_names_of_changed_columns(self) -> list[str]:
        """
         Get the names of all columns that have been changed by the transformer.

        Returns
        -------
        changed_columns : list[str]
             A list of names of changed columns, ordered as they appear in the table.

        Raises
        ------
         TransformerNotFittedError
             If the transformer has not been fitted yet.
        """

    @abstractmethod
    def get_names_of_removed_columns(self) -> list[str]:
        """
        Get the names of all columns that have been removed by the transformer.

        Returns
        -------
        removed_columns : list[str]
            A list of names of the removed columns, ordered as they appear in the table the transformer was fitted on.

        Raises
        ------
        TransformerNotFittedError
            If the transformer has not been fitted yet.
        """

    @abstractmethod
    def is_fitted(self) -> bool:
        """
        Check if the transformer is fitted.

        Returns
        -------
        is_fitted : bool
            Whether the transformer is fitted.
        """

    def fit_and_transform(self, table: Table, column_names: list[str] | None = None) -> Table:
        """
        Learn a transformation for a set of columns in a table and apply the learned transformation to the same table.

        The table is not modified. If you also need the fitted transformer, use `fit` and `transform` separately.

        Parameters
        ----------
        table : Table
            The table used to fit the transformer. The transformer is then applied to this table.
        column_names : list[str] | None
            The list of columns from the table used to fit the transformer. If `None`, all columns are used.

        Returns
        -------
        transformed_table : Table
            The transformed table.
        """
        return self.fit(table, column_names).transform(table)

__hash__()

Return a deterministic hash value for a table transformer.

Returns:

Name Type Description
hash int

The hash value.

Source code in src/safeds/data/tabular/transformation/_table_transformer.py
def __hash__(self) -> int:
    """
    Return a deterministic hash value for a table transformer.

    Returns
    -------
    hash:
        The hash value.
    """
    added = self.get_names_of_added_columns() if self.is_fitted() else []
    changed = self.get_names_of_changed_columns() if self.is_fitted() else []
    removed = self.get_names_of_removed_columns() if self.is_fitted() else []
    return _structural_hash(self.__class__.__qualname__, self.is_fitted(), added, changed, removed)

fit(table, column_names) abstractmethod

Learn a transformation for a set of columns in a table.

This transformer is not modified.

Parameters:

Name Type Description Default
table Table

The table used to fit the transformer.

required
column_names list[str] | None

The list of columns from the table used to fit the transformer. If None, all columns are used.

required

Returns:

Name Type Description
fitted_transformer TableTransformer

The fitted transformer.

Source code in src/safeds/data/tabular/transformation/_table_transformer.py
@abstractmethod
def fit(self, table: Table, column_names: list[str] | None) -> TableTransformer:
    """
    Learn a transformation for a set of columns in a table.

    This transformer is not modified.

    Parameters
    ----------
    table : Table
        The table used to fit the transformer.
    column_names : list[str] | None
        The list of columns from the table used to fit the transformer. If `None`, all columns are used.

    Returns
    -------
    fitted_transformer : TableTransformer
        The fitted transformer.
    """

fit_and_transform(table, column_names=None)

Learn a transformation for a set of columns in a table and apply the learned transformation to the same table.

The table is not modified. If you also need the fitted transformer, use fit and transform separately.

Parameters:

Name Type Description Default
table Table

The table used to fit the transformer. The transformer is then applied to this table.

required
column_names list[str] | None

The list of columns from the table used to fit the transformer. If None, all columns are used.

None

Returns:

Name Type Description
transformed_table Table

The transformed table.

Source code in src/safeds/data/tabular/transformation/_table_transformer.py
def fit_and_transform(self, table: Table, column_names: list[str] | None = None) -> Table:
    """
    Learn a transformation for a set of columns in a table and apply the learned transformation to the same table.

    The table is not modified. If you also need the fitted transformer, use `fit` and `transform` separately.

    Parameters
    ----------
    table : Table
        The table used to fit the transformer. The transformer is then applied to this table.
    column_names : list[str] | None
        The list of columns from the table used to fit the transformer. If `None`, all columns are used.

    Returns
    -------
    transformed_table : Table
        The transformed table.
    """
    return self.fit(table, column_names).transform(table)

get_names_of_added_columns() abstractmethod

Get the names of all new columns that have been added by the transformer.

Returns:

Name Type Description
added_columns list[str]

A list of names of the added columns, ordered as they will appear in the table.

Raises:

Type Description
TransformerNotFittedError

If the transformer has not been fitted yet.

Source code in src/safeds/data/tabular/transformation/_table_transformer.py
@abstractmethod
def get_names_of_added_columns(self) -> list[str]:
    """
    Get the names of all new columns that have been added by the transformer.

    Returns
    -------
    added_columns : list[str]
        A list of names of the added columns, ordered as they will appear in the table.

    Raises
    ------
    TransformerNotFittedError
        If the transformer has not been fitted yet.
    """

get_names_of_changed_columns() abstractmethod

Get the names of all columns that have been changed by the transformer.

Returns:

Name Type Description
changed_columns list[str]

A list of names of changed columns, ordered as they appear in the table.

Raises:

Type Description
TransformerNotFittedError

If the transformer has not been fitted yet.

Source code in src/safeds/data/tabular/transformation/_table_transformer.py
@abstractmethod
def get_names_of_changed_columns(self) -> list[str]:
    """
     Get the names of all columns that have been changed by the transformer.

    Returns
    -------
    changed_columns : list[str]
         A list of names of changed columns, ordered as they appear in the table.

    Raises
    ------
     TransformerNotFittedError
         If the transformer has not been fitted yet.
    """

get_names_of_removed_columns() abstractmethod

Get the names of all columns that have been removed by the transformer.

Returns:

Name Type Description
removed_columns list[str]

A list of names of the removed columns, ordered as they appear in the table the transformer was fitted on.

Raises:

Type Description
TransformerNotFittedError

If the transformer has not been fitted yet.

Source code in src/safeds/data/tabular/transformation/_table_transformer.py
@abstractmethod
def get_names_of_removed_columns(self) -> list[str]:
    """
    Get the names of all columns that have been removed by the transformer.

    Returns
    -------
    removed_columns : list[str]
        A list of names of the removed columns, ordered as they appear in the table the transformer was fitted on.

    Raises
    ------
    TransformerNotFittedError
        If the transformer has not been fitted yet.
    """

is_fitted() abstractmethod

Check if the transformer is fitted.

Returns:

Name Type Description
is_fitted bool

Whether the transformer is fitted.

Source code in src/safeds/data/tabular/transformation/_table_transformer.py
@abstractmethod
def is_fitted(self) -> bool:
    """
    Check if the transformer is fitted.

    Returns
    -------
    is_fitted : bool
        Whether the transformer is fitted.
    """

transform(table) abstractmethod

Apply the learned transformation to a table.

The table is not modified.

Parameters:

Name Type Description Default
table Table

The table to which the learned transformation is applied.

required

Returns:

Name Type Description
transformed_table Table

The transformed table.

Raises:

Type Description
TransformerNotFittedError

If the transformer has not been fitted yet.

Source code in src/safeds/data/tabular/transformation/_table_transformer.py
@abstractmethod
def transform(self, table: Table) -> Table:
    """
    Apply the learned transformation to a table.

    The table is not modified.

    Parameters
    ----------
    table : Table
        The table to which the learned transformation is applied.

    Returns
    -------
    transformed_table : Table
        The transformed table.

    Raises
    ------
    TransformerNotFittedError
        If the transformer has not been fitted yet.
    """