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pandas.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False)
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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 withobject
orcategory
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.
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.
Returns
??-
dummies : DataFrame or SparseDataFrame
See also
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
pandas.get_dummies()
2017-01-12 04:50:24
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