Function from_dummies

pandas/core/reshape/encoding.py:372–589

Create a categorical ``DataFrame`` from a ``DataFrame`` of dummy variables. Inverts the operation performed by :func:`~pandas.get_dummies`. Parameters ---------- data : DataFrame Data which contains dummy-coded variables in form of integer columns of 1's and 0'

(
    data: DataFrame,
    sep: None | str = None,
    default_category: None | Hashable | dict[str, Hashable] = None,
)

@set_module("pandas")
def from_dummies(
    data: DataFrame,
    sep: None | str = None,
    default_category: None | Hashable | dict[str, Hashable] = None,
) -> DataFrame:
    """
    Create a categorical ``DataFrame`` from a ``DataFrame`` of dummy variables.

    Inverts the operation performed by :func:`~pandas.get_dummies`.

    Parameters
    ----------
    data : DataFrame
        Data which contains dummy-coded variables in form of integer columns of
        1's and 0's.
    sep : str, default None
        Separator used in the column names of the dummy categories they are
        character indicating the separation of the categorical names from the prefixes.
        For example, if your column names are 'prefix_A' and 'prefix_B',
        you can strip the underscore by specifying sep='_'.
    default_category : None, Hashable or dict of Hashables, default None
        The default category is the implied category when a value has none of the
        listed categories specified with a one, i.e. if all dummies in a row are
        zero. Can be a single value for all variables or a dict directly mapping
        the default categories to a prefix of a variable. The default category
        will be coerced to the dtype of ``data.columns`` if such coercion is
        lossless, and will raise otherwise.

    Returns
    -------
    DataFrame
        Categorical data decoded from the dummy input-data.

    Raises
    ------
    ValueError
        * When the input ``DataFrame`` ``data`` contains NA values.
        * When the input ``DataFrame`` ``data`` contains column names with separators
          that do not match the separator specified with ``sep``.
        * When a ``dict`` passed to ``default_category`` does not include an implied
          category for each prefix.
        * When a value in ``data`` has more than one category assigned to it.
        * When ``default_category=None`` and a value in ``data`` has no category
          assigned to it.
    TypeError
        * When the input ``data`` is not of type ``DataFrame``.
        * When the input ``DataFrame`` ``data`` contains non-dummy data.
        * When the passed ``sep`` is of a wrong data type.
        * When the passed ``default_category`` is of a wrong data type.

    See Also
    --------
    :func:`~pandas.get_dummies` : Convert ``Series`` or ``DataFrame`` to dummy codes.
    :class:`~pandas.Categorical` : Represent a categorical variable in classic.

    Notes
    -----
    The columns of the passed dummy data should only include 1's and 0's,