dask.dataframe.api.Rolling.sum

Rolling.sum(*args, **kwargs)

Calculate the rolling sum.

This docstring was copied from pandas.api.typing.Rolling.sum.

Some inconsistencies with the Dask version may exist.

This is equivalent to applying numpy.sum over each rolling window. Missing values (NaN) are excluded from the calculation.

Parameters:

numeric_onlybool, default False (Not supported in Dask)

Include only float, int, boolean columns.

enginestr, default None (Not supported in Dask)

• '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 setting compute.use_numba

engine_kwargsdict, default None (Not supported in Dask)

• For 'cython' engine, there are no accepted engine_kwargs

• For 'numba' engine, the engine can accept nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {'nogil': False, 'parallel': False}.

Returns:

Series or DataFrame

Return type is the same as the original object with np.float64 dtype.

See also

Series.rolling

Calling rolling with Series data.

DataFrame.rolling

Calling rolling with DataFrames.

Series.sum

Aggregating sum for Series.

DataFrame.sum

Aggregating sum for DataFrame.

Notes

See Numba engine and Numba (JIT compilation) 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