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modin.pandas.DataFrame.shift¶

DataFrame.shift(periods: int = 1, freq=None, axis: Union[int, Literal['index', 'columns', 'rows']] = 0, fill_value: Hashable = _NoDefault.no_default) → DataFrame[source]¶

Shift data by desired number of periods along axis and replace columns with fill_value (default: None).

Snowpark pandas does not support freq currently.

Parameters:

periods (int) – Number of periods to shift. Can be positive or negative.
freq (not supported, default None) –
axis ({0 or 'index', 1 or 'columns', None}, default None) – Shift direction.
fill_value (object, optional) – The scalar value to use for newly introduced missing values. the default depends on the dtype of self. For numeric data, np.nan is used. For datetime, timedelta, or period data, etc. NaT is used. For extension dtypes, self.dtype.na_value is used.

Returns:

Copy of input object, shifted.

Return type:

DataFrame

Examples

>>> df = pd.DataFrame({"Col1": [10, 20, 15, 30, 45],
...                    "Col2": [13, 23, 18, 33, 48],
...                    "Col3": [17, 27, 22, 37, 52]},
...                   index=pd.date_range("2020-01-01", "2020-01-05"))
>>> df
            Col1  Col2  Col3
2020-01-01    10    13    17
2020-01-02    20    23    27
2020-01-03    15    18    22
2020-01-04    30    33    37
2020-01-05    45    48    52

>>> df.shift(periods=3)
            Col1  Col2  Col3
2020-01-01   NaN   NaN   NaN
2020-01-02   NaN   NaN   NaN
2020-01-03   NaN   NaN   NaN
2020-01-04  10.0  13.0  17.0
2020-01-05  20.0  23.0  27.0

>>> df.shift(periods=1, axis="columns")
            Col1  Col2  Col3
2020-01-01  None    10    13
2020-01-02  None    20    23
2020-01-03  None    15    18
2020-01-04  None    30    33
2020-01-05  None    45    48

>>> df.shift(periods=3, fill_value=0)
            Col1  Col2  Col3
2020-01-01     0     0     0
2020-01-02     0     0     0
2020-01-03     0     0     0
2020-01-04    10    13    17
2020-01-05    20    23    27