xorbits.pandas.get_dummies#

xorbits.pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None)[source]#

Convert categorical variable into dummy/indicator variables.

Each variable is converted in as many 0/1 variables as there are different values. Columns in the output are each named after a value; if the input is a DataFrame, the name of the original variable is prepended to the value.

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, 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 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 columns is None then all the columns with object, string, 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 bool) – Data type for new columns. Only a single dtype is allowed.

Returns

Dummy-coded data. If data contains other columns than the dummy-coded one(s), these will be prepended, unaltered, to the result.

Return type

DataFrame

See also

Series.str.get_dummies

Convert Series of strings to dummy codes.

from_dummies()

Convert dummy codes to categorical DataFrame.

Notes

Reference the user guide for more examples.

Examples

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

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

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

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

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

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

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

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

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

>>> 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

This docstring was copied from pandas.