pandas.core.groupby.DataFrameGroupBy.shift — pandas 2.2.3 documentation (original) (raw)

DataFrameGroupBy.shift(periods=1, freq=None, axis=<no_default>, fill_value=<no_default>, suffix=None)[source]#

Shift each group by periods observations.

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

Parameters:

periodsint | Sequence[int], default 1

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

freqstr, optional

Frequency string.

axisaxis to shift, default 0

Shift direction.

Deprecated since version 2.1.0: For axis=1, operate on the underlying object instead. Otherwise the axis keyword is not necessary.

fill_valueoptional

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.

suffixstr, 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