modin.pandas.Rolling.mean¶

Rolling.mean(numeric_only: bool = False, *args: Any, engine: Optional[Literal['cython', 'numba']] = None, engine_kwargs: Optional[dict[str, bool]] = None, **kwargs: Any)[source]¶

Compute the rolling mean.

Parameters:

• numeric_only (bool, default False) – Include only float, int, boolean columns.

• *args – Positional arguments to pass to func.

• engine (str, default None 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

  This parameter is ignored in Snowpark pandas. The execution engine will always be Snowflake.

• engine_kwargs (dict, default None 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}.

  This parameter is ignored in Snowpark pandas. The execution engine will always be Snowflake.

• **kwargs – Keyword arguments to be passed into func.

Returns:

Computed rolling mean of values.

Return type:

Series or DataFrame

Examples

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0
>>> df.rolling(2, min_periods=1).mean()
     B
0  0.0
1  0.5
2  1.5
3  2.0
4  4.0
>>> df.rolling(2, min_periods=2).mean()
     B
0  NaN
1  0.5
2  1.5
3  NaN
4  NaN
>>> df.rolling(3, min_periods=1, center=True).mean()
     B
0  0.5
1  1.0
2  1.5
3  3.0
4  4.0

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