get_dummies(data, prefix=None, prefix_sep='_', dummy_na: 'bool' = False, columns=None, sparse: 'bool' = False, drop_first: 'bool' = False, dtype: 'Dtype | None' = None) -> 'DataFrame'

Notes

Reference the user guide <reshaping.dummies> for more examples.

Parameters

data : array-like, Series, or DataFrame

Data of which to get dummy indicators.

prefix : str, list of str, or dict of str, default None

String to append DataFrame column names. Pass a list with length equal to the number of columns when calling get_dummies on a DataFrame. Alternatively, :None:None:`prefix` can be a dictionary mapping column names to prefixes.

prefix_sep : str, default '_'

If appending prefix, separator/delimiter to use. Or pass a list or dictionary as with :None:None:`prefix`.

dummy_na : bool, default False

Add a column to indicate NaNs, if False NaNs are ignored.

columns : list-like, default None

Column names in the DataFrame to be encoded. If :None:None:`columns` is None then all the columns with :None:None:`object` or category dtype will be converted.

sparse : bool, default False

Whether the dummy-encoded columns should be backed by a SparseArray (True) or a regular NumPy array (False).

drop_first : bool, default False

Whether to get k-1 dummies out of k categorical levels by removing the first level.

dtype : dtype, default np.uint8

Data type for new columns. Only a single dtype is allowed.

Returns

DataFrame

Dummy-coded data.

Convert categorical variable into dummy/indicator variables.

See Also

Series.str.get_dummies

Convert Series to dummy codes.

Examples

This example is valid syntax, but we were not able to check execution

>>> s = pd.Series(list('abca'))

This example is valid syntax, but we were not able to check execution

>>> pd.get_dummies(s)
   a  b  c
0  1  0  0
1  0  1  0
2  0  0  1
3  1  0  0

This example is valid syntax, but we were not able to check execution

>>> s1 = ['a', 'b', np.nan]

This example is valid syntax, but we were not able to check execution

>>> pd.get_dummies(s1)
   a  b
0  1  0
1  0  1
2  0  0

This example is valid syntax, but we were not able to check execution

>>> pd.get_dummies(s1, dummy_na=True)
   a  b  NaN
0  1  0    0
1  0  1    0
2  0  0    1

This example is valid syntax, but we were not able to check execution

>>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],
...                    'C': [1, 2, 3]})

This example is valid syntax, but we were not able to check execution

>>> pd.get_dummies(df, prefix=['col1', 'col2'])
   C  col1_a  col1_b  col2_a  col2_b  col2_c
0  1       1       0       0       1       0
1  2       0       1       1       0       0
2  3       1       0       0       0       1

This example is valid syntax, but we were not able to check execution

>>> pd.get_dummies(pd.Series(list('abcaa')))
   a  b  c
0  1  0  0
1  0  1  0
2  0  0  1
3  1  0  0
4  1  0  0

This example is valid syntax, but we were not able to check execution

>>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)
   b  c
0  0  0
1  1  0
2  0  1
3  0  0
4  0  0

This example is valid syntax, but we were not able to check execution

>>> pd.get_dummies(pd.Series(list('abc')), dtype=float)
     a    b    c
0  1.0  0.0  0.0
1  0.0  1.0  0.0
2  0.0  0.0  1.0

See :

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File: /pandas/core/reshape/reshape.py#811
type: <class 'function'>
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