pandas.core.groupby.DataFrameGroupBy.pct_change#
- DataFrameGroupBy.pct_change(periods=1, fill_method=<no_default>, limit=<no_default>, freq=None, axis=<no_default>)[source]#
Calculate pct_change of each value to previous entry in group.
- Returns:
- Series or DataFrame
Percentage changes within each group.
See also
Series.groupby
Apply a function groupby to a Series.
DataFrame.groupby
Apply a function groupby 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