xorbits.pandas.window.Rolling.sum#
- Rolling.sum(*args, **kwargs)[source]#
Calculate the rolling sum.
- Parameters
numeric_only (bool, default False (Not supported yet)) –
Include only float, int, boolean columns.
New in version 1.5.0(pandas).
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 1.3.0(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 1.3.0(pandas).
- Returns
Return type is the same as the original object with
np.float64
dtype.- Return type
See also
pandas.Series.rolling
Calling rolling with Series data.
pandas.DataFrame.rolling
Calling rolling with DataFrames.
pandas.Series.sum
Aggregating sum for Series.
pandas.DataFrame.sum
Aggregating sum for DataFrame.
Notes
See window.numba_engine and enhancingperf.numba for extended documentation and performance considerations for the Numba engine.
Examples
>>> s = pd.Series([1, 2, 3, 4, 5]) >>> s 0 1 1 2 2 3 3 4 4 5 dtype: int64
>>> s.rolling(3).sum() 0 NaN 1 NaN 2 6.0 3 9.0 4 12.0 dtype: float64
>>> s.rolling(3, center=True).sum() 0 NaN 1 6.0 2 9.0 3 12.0 4 NaN dtype: float64
For DataFrame, each sum is computed column-wise.
>>> df = pd.DataFrame({"A": s, "B": s ** 2}) >>> df A B 0 1 1 1 2 4 2 3 9 3 4 16 4 5 25
>>> df.rolling(3).sum() A B 0 NaN NaN 1 NaN NaN 2 6.0 14.0 3 9.0 29.0 4 12.0 50.0
This docstring was copied from pandas.core.window.rolling.Rolling.