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Module « pandas »

Fonction from_dummies - module pandas

Signature de la fonction from_dummies

def from_dummies(data: 'DataFrame', sep: 'None | str' = None, default_category: 'None | Hashable | dict[str, Hashable]' = None) -> 'DataFrame' 

Description

help(pandas.from_dummies)

Create a categorical ``DataFrame`` from a ``DataFrame`` of dummy variables.

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

.. versionadded:: 1.5.0

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.

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,
or boolean values.

Examples
--------
>>> df = pd.DataFrame({"a": [1, 0, 0, 1], "b": [0, 1, 0, 0],
...                    "c": [0, 0, 1, 0]})

>>> df
   a  b  c
0  1  0  0
1  0  1  0
2  0  0  1
3  1  0  0

>>> pd.from_dummies(df)
0     a
1     b
2     c
3     a

>>> df = pd.DataFrame({"col1_a": [1, 0, 1], "col1_b": [0, 1, 0],
...                    "col2_a": [0, 1, 0], "col2_b": [1, 0, 0],
...                    "col2_c": [0, 0, 1]})

>>> df
      col1_a  col1_b  col2_a  col2_b  col2_c
0       1       0       0       1       0
1       0       1       1       0       0
2       1       0       0       0       1

>>> pd.from_dummies(df, sep="_")
    col1    col2
0    a       b
1    b       a
2    a       c

>>> df = pd.DataFrame({"col1_a": [1, 0, 0], "col1_b": [0, 1, 0],
...                    "col2_a": [0, 1, 0], "col2_b": [1, 0, 0],
...                    "col2_c": [0, 0, 0]})

>>> df
      col1_a  col1_b  col2_a  col2_b  col2_c
0       1       0       0       1       0
1       0       1       1       0       0
2       0       0       0       0       0

>>> pd.from_dummies(df, sep="_", default_category={"col1": "d", "col2": "e"})
    col1    col2
0    a       b
1    b       a
2    d       e

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