pandas.get_dummies

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

Parameters:

data : array-like, Series, or DataFrame prefix : string, list of strings, or dict of strings, 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 : string, 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 or category dtype will be converted. sparse : bool, default False Whether the dummy columns should be sparse or not. Returns SparseDataFrame if data is a Series or if all columns are included. Otherwise returns a DataFrame with some SparseBlocks. drop_first : bool, default False Whether to get k-1 dummies out of k categorical levels by removing the first level. New in version 0.18.0. dtype : dtype, default np.uint8 Data type for new columns. Only a single dtype is allowed. New in version 0.23.0.

Returns:

dummies : DataFrame or SparseDataFrame

See also

Series.str.get_dummies

Examples

>>> import pandas as pd
>>> s = pd.Series(list('abca'))

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

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

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

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

>>> 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       1       0       0       1       0
1  2       0       1       1       0       0
2  3       1       0       0       0       1

>>> 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

>>> 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

>>> 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