mars.dataframe.get_dummies#
- mars.dataframe.get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False, dtype=None)[源代码]#
Convert categorical variable into dummy/indicator variables.
- 参数
data (array-like, Series, or DataFrame) – Data of which to get dummy indicators.
prefix (str, 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_sep (str, 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-encoded columns should be backed by a
SparseArray
(True) or a regular NumPy array (False).drop_first (bool, default False) – Whether to get k-1 dummies out of k categorical levels by removing the first level.
dtype (dtype, default np.uint8) – Data type for new columns. Only a single dtype is allowed.
- 返回
Dummy-coded data.
- 返回类型
实际案例
>>> import mars.dataframe as md >>> s = md.Series(list('abca'))
>>> md.get_dummies(s).execute() a b c 0 1 0 0 1 0 1 0 2 0 0 1 3 1 0 0
>>> s1 = ['a', 'b', np.nan]
>>> md.get_dummies(s1).execute() a b 0 1 0 1 0 1 2 0 0
>>> md.get_dummies(s1, dummy_na=True).execute() a b NaN 0 1 0 0 1 0 1 0 2 0 0 1
>>> df = md.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'], ... 'C': [1, 2, 3]})
>>> md.get_dummies(df, prefix=['col1', 'col2']).execute() 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
>>> md.get_dummies(pd.Series(list('abcaa'))).execute() a b c 0 1 0 0 1 0 1 0 2 0 0 1 3 1 0 0 4 1 0 0
>>> md.get_dummies(pd.Series(list('abcaa')), drop_first=True).execute() b c 0 0 0 1 1 0 2 0 1 3 0 0 4 0 0
>>> md.get_dummies(pd.Series(list('abc')), dtype=float).execute() a b c 0 1.0 0.0 0.0 1 0.0 1.0 0.0 2 0.0 0.0 1.0