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Showing content from https://pandas.pydata.org/docs/reference/api/pandas.core.window.rolling.Rolling.sum.html below:

pandas.core.window.rolling.Rolling.sum#

Rolling.sum(numeric_only=False, engine=None, engine_kwargs=None)[source]#

Calculate the rolling sum.

Parameters:

numeric_onlybool, default False

Include only float, int, boolean columns.

Added in version 1.5.0.

enginestr, default None

'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

Added in version 1.3.0.

engine_kwargsdict, default None

For 'cython' engine, there are no accepted engine_kwargs
For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {'nopython': True, 'nogil': False, 'parallel': False}

Added in version 1.3.0.

Returns:

Series or DataFrame: Return type is the same as the original object with np.float64 dtype.

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

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