modin.pandas.Expanding.var¶

Expanding.var(ddof: int = 1, numeric_only: bool = False, engine: Optional[Literal['cython', 'numba']] = None, engine_kwargs: Optional[dict[str, bool]] = None)[source]¶

Compute the expanding var.

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 var 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).var()
          B
0       NaN
1  0.500000
2  1.000000
3  1.000000
4  2.916667

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