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