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# pandas.Series.expanding

Series.expanding(min_periods=1, method='single')

  Provide expanding window calculations.

  An expanding window yields the value of an aggregation statistic with all the data available up to that point in time.

  Parameters:
    min_periods int, default 1

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

    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.

  Returns:
    pandas.api.typing.Expanding

      An instance of Expanding for further expanding window calculations, e.g. using the `sum` method.

  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