pandas.get_dummies¶
- pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False)¶
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. Alternativly, 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.
New in version 0.16.1.
Returns: dummies : DataFrame or SparseDataFrame
Examples
>>> import pandas as pd >>> s = pd.Series(list('abca'))
>>> 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]
>>> get_dummies(s1) a b 0 1 0 1 0 1 2 0 0
>>> get_dummies(s1, dummy_na=True) a b NaN 0 1 0 0 1 0 1 0 2 0 0 1
>>> df = DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'], 'C': [1, 2, 3]})
>>> 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
See also Series.str.get_dummies.