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modin.pandas.Rolling.stdΒΆ

Rolling.std(ddof: int = 1, numeric_only: bool = False, *args: Any, engine: Optional[Literal['cython', 'numba']] = None, engine_kwargs: Optional[dict[str, bool]] = None, **kwargs: Any)[source]ΒΆ

Compute the rolling std.

Parameters:

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

  • ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

  • *args (tuple) – 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 (dict) – Keyword arguments to be passed into func.

Returns:

Computed rolling std 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).std()
          B
0       NaN
1  0.707107
2  0.707107
3       NaN
4       NaN
>>> df.rolling(2, min_periods=1).std(ddof=0)
     B
0  0.0
1  0.5
2  0.5
3  0.0
4  0.0
>>> df.rolling(3, min_periods=1, center=True).std()
          B
0  0.707107
1  1.000000
2  0.707107
3  1.414214
4       NaN
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