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.

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:

dataarray-like, Series, or DataFrame: Data of which to get dummy indicators.
prefixstr, 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_sepstr, default ‘_’: If appending prefix, separator/delimiter to use. Or pass a list or dictionary as with prefix.
dummy_nabool, default False: Add a column to indicate NaNs, if False NaNs are ignored.
columnslist-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.
sparsebool, default False: Whether the dummy-encoded columns should be backed by a SparseArray (True) or a regular NumPy array (False).
drop_firstbool, default False: Whether to get k-1 dummies out of k categorical levels by removing the first level.
dtypedtype, default bool: Data type for new columns. Only a single dtype is allowed.

Returns:

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

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