Skip to main content
`Ctrl`+`K`

# pandas.api.typing.DataFrameGroupBy.expanding

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

  Return an expanding grouper, providing expanding functionality per group.

  Each group’s expanding window includes all prior rows within that group up to and including the current row.

  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 expanding 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.ExpandingGroupby

      An object that supports expanding transformations over each group.

  See also

  `Series.expanding`

    Expanding transformations for Series.

  `DataFrame.expanding`

    Expanding transformations for DataFrames.

  `Series.groupby`

    Apply a function groupby to a Series.

  `DataFrame.groupby`

    Apply a function groupby.

  Examples

      >>> df = pd.DataFrame(
      ...     {
      ...         "Class": ["A", "A", "A", "B", "B", "B"],
      ...         "Value": [10, 20, 30, 40, 50, 60],
      ...     }
      ... )
      >>> df
      Class  Value
      0     A     10
      1     A     20
      2     A     30
      3     B     40
      4     B     50
      5     B     60

      >>> df.groupby("Class").expanding().mean()
              Value
      Class
      A     0   10.0
            1   15.0
            2   20.0
      B     3   40.0
            4   45.0
            5   50.0