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# pandas.api.typing.SeriesGroupBy.shift

SeriesGroupBy.shift(periods=1, freq=None, fill_value=<no_default>, suffix=None)

  Shift each group by periods observations.

  If freq is passed, the index will be increased using the periods and the freq.

  Parameters:
    periods int | Sequence[int], default 1

      Number of periods to shift. If a list of values, shift each group by each period.

    freq str, optional

      Frequency string.

    fill_value optional

      The scalar value to use for newly introduced missing values.

      Changed in version 2.1.0: Will raise a `ValueError` if `freq` is provided too.

    suffix str, optional

      A string to add to each shifted column if there are multiple periods. Ignored otherwise.

  Returns:
    Series or DataFrame

      Object shifted within each group.

  See also

  `Index.shift`

    Shift values of Index.

  Examples

  For SeriesGroupBy:

      >>> lst = ["a", "a", "b", "b"]
      >>> ser = pd.Series([1, 2, 3, 4], index=lst)
      >>> ser
      a    1
      a    2
      b    3
      b    4
      dtype: int64
      >>> ser.groupby(level=0).shift(1)
      a    NaN
      a    1.0
      b    NaN
      b    3.0
      dtype: float64

  For DataFrameGroupBy:

      >>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]]
      >>> df = pd.DataFrame(
      ...     data,
      ...     columns=["a", "b", "c"],
      ...     index=["tuna", "salmon", "catfish", "goldfish"],
      ... )
      >>> df
                 a  b  c
          tuna   1  2  3
        salmon   1  5  6
       catfish   2  5  8
      goldfish   2  6  9
      >>> df.groupby("a").shift(1)
                    b    c
          tuna    NaN  NaN
        salmon    2.0  3.0
       catfish    NaN  NaN
      goldfish    5.0  8.0