pandas.core.groupby.SeriesGroupBy.pct_change — pandas 3.0.0.dev0+2103.g41968a550a documentation (original) (raw)

SeriesGroupBy.pct_change(periods=1, fill_method=None, freq=None)[source]#

Calculate pct_change of each value to previous entry in group.

Parameters:

periodsint, 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_methodNone

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

Deprecated since version 2.1: All options of fill_method are deprecated except fill_method=None.

freqstr, 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.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