xorbits.pandas.DataFrame.expanding#

DataFrame.expanding(min_periods=1, center=False, axis=0)#

Provide expanding window calculations.

Parameters

  • min_periods (int, default 1) – Minimum number of observations in window required to have a value; otherwise, result is np.nan.

  • center (bool, default False) –

    If False, set the window labels as the right edge of the window index.

    If True, set the window labels as the center of the window index.

    Deprecated since version 1.1.0.

  • axis (int or str, default 0) –

    If 0 or 'index', roll across the rows.

    If 1 or 'columns', roll across the columns.

    For Series this parameter is unused and defaults to 0.

  • method (str {'single', 'table'}, default 'single' (Not supported yet)) –

    Execute the rolling operation per single column or row ('single') or over the entire object ('table').

    This argument is only implemented when specifying engine='numba' in the method call.

    New in version 1.3.0.

Return type

Expanding subclass

See also

rolling

Provides rolling window calculations.

ewm

Provides exponential weighted functions.

Notes

See Windowing Operations for further usage details and examples.

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

min_periods

Expanding sum with 1 vs 3 observations needed to calculate a value.

>>> df.expanding(1).sum()  
     B
0  0.0
1  1.0
2  3.0
3  3.0
4  7.0
>>> df.expanding(3).sum()  
     B
0  NaN
1  NaN
2  3.0
3  3.0
4  7.0

This docstring was copied from pandas.core.frame.DataFrame.