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# pandas.api.typing.SeriesGroupBy.pct_change

SeriesGroupBy.pct_change(periods=1, fill_method=None, freq=None)

  Calculate pct_change of each value to previous entry in group.

  This method calculates the percentage change between the current and a prior element within each group, useful for computing growth rates.

  Parameters:
    periods int, default 1

      Periods to shift for calculating percentage change. Comparing with a period of 1 means adjacent elements are compared, whereas a period of 2 compares every other element.

    fill_method None

      Must be None. This argument will be removed in a future version of pandas.

    freq str, pandas offset object, or None, default None

      The frequency increment for time series data (e.g., ‘M’ for month-end). If None, the frequency is inferred from the index. Relevant for time series data only.

  Returns:
    Series or DataFrame

      Percentage changes within each group.

  See also

  `Series.pct_change`

    Apply function pct_change to a Series.

  `DataFrame.pct_change`

    Apply function pct_change to each row or column of a DataFrame.

  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).pct_change()
      a         NaN
      a    1.000000
      b         NaN
      b    0.333333
      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").pct_change()
                  b  c
          tuna    NaN    NaN
        salmon    1.5  1.000
       catfish    NaN    NaN
      goldfish    0.2  0.125