pandas.core.groupby.SeriesGroupBy.shift#

SeriesGroupBy.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