pandas.core.groupby.DataFrameGroupBy.pct_change#

DataFrameGroupBy.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