pandas.from_dummies#

pandas.from_dummies(data, sep=None, default_category=None)[source]#

Create a categorical DataFrame from a DataFrame of dummy variables.

Inverts the operation performed by get_dummies().

Added in version 1.5.0.

Parameters:
dataDataFrame

Data which contains dummy-coded variables in form of integer columns of 1’s and 0’s.

sepstr, 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_categoryNone, 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

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
On this page

This Page