xorbits.pandas.groupby.SeriesGroupBy.std#
- SeriesGroupBy.std(**kw)#
Compute standard deviation of groups, excluding missing values.
For multiple groupings, the result index will be a MultiIndex.
- Parameters
ddof (int, default 1 (Not supported yet)) – Degrees of freedom.
engine (str, default None (Not supported yet)) –
'cython'
: Runs the operation through C-extensions from cython.'numba'
: Runs the operation through JIT compiled code from numba.None
: Defaults to'cython'
or globally settingcompute.use_numba
New in version 1.4.0(pandas).
engine_kwargs (dict, default None (Not supported yet)) –
For
'cython'
engine, there are no acceptedengine_kwargs
For
'numba'
engine, the engine can acceptnopython
,nogil
andparallel
dictionary keys. The values must either beTrue
orFalse
. The defaultengine_kwargs
for the'numba'
engine is{{'nopython': True, 'nogil': False, 'parallel': False}}
New in version 1.4.0(pandas).
numeric_only (bool, default False (Not supported yet)) –
Include only float, int or boolean data.
New in version 1.5.0(pandas).
Changed in version 2.0.0(pandas): numeric_only now defaults to
False
.
- Returns
Standard deviation of values within each group.
- Return type
See also
Series.groupby
Apply a function groupby to a Series.
DataFrame.groupby
Apply a function groupby to each row or column of a DataFrame.
Examples
For SeriesGroupBy:
>>> lst = ['a', 'a', 'a', 'b', 'b', 'b'] >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst) >>> ser a 7 a 2 a 8 b 4 b 3 b 3 dtype: int64 >>> ser.groupby(level=0).std() a 3.21455 b 0.57735 dtype: float64
For DataFrameGroupBy:
>>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]} >>> df = pd.DataFrame(data, index=['dog', 'dog', 'dog', ... 'mouse', 'mouse', 'mouse', 'mouse']) >>> df a b dog 1 1 dog 3 4 dog 5 8 mouse 7 4 mouse 7 4 mouse 8 2 mouse 3 1 >>> df.groupby(level=0).std() a b dog 2.000000 3.511885 mouse 2.217356 1.500000
This docstring was copied from pandas.core.groupby.generic.SeriesGroupBy.