modin.pandas.SeriesGroupBy.shift¶

SeriesGroupBy.shift(periods: int = 1, freq: int = None, axis: Union[int, Literal['index', 'columns', 'rows']] = 0, fill_value: Any = None)[source]¶

Shift each group by periods observations.

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

Parameters:

• periods (int, default 1) – Number of periods to shift.

• freq (str, optional) – Frequency string.

• axis (axis to shift, default 0) – Shift direction. Snowpark pandas does not yet support axis=1.

• fill_value (optional) – The scalar value to use for newly introduced missing values.

Returns:

Object shifted within each group.

Return type:

Series or DataFrame

Examples

For SeriesGroupBy:

>>> lst = ['a', 'a', 'b', 'b']
>>> ser = pd.Series([1, 2, 3, 4], index=lst)

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>>> ser
a    1
a    2
b    3
b    4
dtype: int64

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>>> ser.groupby(level=0).shift(1)
a    NaN
a    1.0
b    NaN
b    3.0
dtype: float64

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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"])

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>>> df
          a  b  c
tuna      1  2  3
salmon    1  5  6
catfish   2  5  8
goldfish  2  6  9

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>>> df.groupby("a").shift(1)
            b    c
tuna      NaN  NaN
salmon    2.0  3.0
catfish   NaN  NaN
goldfish  5.0  8.0

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