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
iffreq
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