snowflake.snowpark.modin.plugin.extensions.window_overrides.Expanding.std

Expanding.std(ddof: int = 1, numeric_only: bool = False, engine: Optional[Literal['cython', 'numba']] = None, engine_kwargs: Optional[dict[str, bool]] = None)[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/window_overrides.py#L581-L598)

Compute the expanding 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.

  • 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.

  • None –

Returns:

Computed expanding 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.expanding(2).std()
          B
0       NaN
1  0.707107
2  1.000000
3  1.000000
4  1.707825
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