See Windowing Operations <window.expanding>
for further usage details and examples.
Minimum number of observations in window required to have a value; otherwise, result is np.nan
.
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.
If 0
or 'index'
, roll across the rows.
If 1
or 'columns'
, roll across the columns.
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.
Provide expanding window calculations.
ewm
Provides exponential weighted functions.
rolling
Provides rolling window calculations.
>>> 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 7.0This 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 7.0See :
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