modin.pandas.Rolling.std¶

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]¶

Compute the rolling std.

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 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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