xorbits.pandas.from_dummies#

xorbits.pandas.from_dummies(data: DataFrame, sep: None | str = None, default_category: None | Hashable | dict[str, Hashable] = None) → DataFrame[source]#

Create a categorical DataFrame from a DataFrame of dummy variables.

Inverts the operation performed by get_dummies().

New in version 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

Categorical data decoded from the dummy input-data.

Return type

DataFrame

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

get_dummies()

Convert Series or DataFrame to dummy codes.

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

This docstring was copied from pandas.