pandas 1.4.2

NotesParametersReturns

Notes

See Windowing Operations <window.expanding> for further usage details and examples.

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
axis : int or str, default 0

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

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

method : str {'single', 'table'}, default 'single'

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.

versionadded

Returns

``Expanding`` subclass

Provide expanding window calculations.

See Also

ewm

Provides exponential weighted functions.

rolling

Provides rolling window calculations.

Examples

This example is valid syntax, but we were not able to check execution
>>> 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.

This example is valid syntax, but we were not able to check execution
>>> df.expanding(1).sum()
     B
0  0.0
1  1.0
2  3.0
3  3.0
4  7.0
This example is valid syntax, but we were not able to check execution
>>> df.expanding(3).sum()
     B
0  NaN
1  NaN
2  3.0
3  3.0
4  7.0
See :

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File: /pandas/core/window/expanding.py#45
type: <class 'type'>
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