pandas.Series.expanding#

Series.expanding(min_periods=1, axis=0, method='single')[source]#

Provide expanding window calculations.

Parameters
min_periodsint, default 1

Minimum number of observations in window required to have a value; otherwise, result is np.nan.

axisint 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.

methodstr {‘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.

New in version 1.3.0.

Returns
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