Method shift

pandas/core/frame.py:6580–6799

Shift index by desired number of periods with an optional time `freq`. When `freq` is not passed, shift the index without realigning the data. If `freq` is passed (in this case, the index must be date or datetime, or it will raise a `NotImplementedError`), the index

(
        self,
        periods: int | Sequence[int] = 1,
        freq: Frequency | None = None,
        axis: Axis = 0,
        fill_value: Hashable = lib.no_default,
        suffix: str | None = None,
    )

        return res if inplace else res.__finalize__(self)

    def shift(
        self,
        periods: int | Sequence[int] = 1,
        freq: Frequency | None = None,
        axis: Axis = 0,
        fill_value: Hashable = lib.no_default,
        suffix: str | None = None,
    ) -> DataFrame:
        """
        Shift index by desired number of periods with an optional time `freq`.

        When `freq` is not passed, shift the index without realigning the data.
        If `freq` is passed (in this case, the index must be date or datetime,
        or it will raise a `NotImplementedError`), the index will be
        increased using the periods and the `freq`. `freq` can be inferred
        when specified as "infer" as long as either freq or inferred_freq
        attribute is set in the index.

        Parameters
        ----------
        periods : int or Sequence
            Number of periods to shift. Can be positive or negative.
            If an iterable of ints, the data will be shifted once by each int.
            This is equivalent to shifting by one value at a time and
            concatenating all resulting frames. The resulting columns will have
            the shift suffixed to their column names. For multiple periods,
            axis must not be 1.
        freq : DateOffset, tseries.offsets, timedelta, or str, optional
            Offset to use from the tseries module or time rule (e.g. 'EOM').
            If `freq` is specified then the index values are shifted but the
            data is not realigned. That is, use `freq` if you would like to
            extend the index when shifting and preserve the original data.
            If `freq` is specified as "infer" then it will be inferred from
            the freq or inferred_freq attributes of the index. If neither of
            those attributes exist, a ValueError is thrown.
        axis : {0 or 'index', 1 or 'columns', None}, default None
            Shift direction. For `Series` this parameter is unused and defaults to 0.
        fill_value : object, optional
            The scalar value to use for newly introduced missing values.
            the default depends on the dtype of `self`.
            For Boolean and numeric NumPy data types, ``np.nan`` is used.
            For datetime, timedelta, or period data, etc. :attr:`NaT` is used.
            For extension dtypes, ``self.dtype.na_value`` is used.
        suffix : str, optional
            If str and periods is an iterable, this is added after the column
            name and before the shift value for each shifted column name.
            For `Series` this parameter is unused and defaults to `None`.

        Returns
        -------
        DataFrame
            Copy of input object, shifted.

        See Also
        --------
        Index.shift : Shift values of Index.
        DatetimeIndex.shift : Shift values of DatetimeIndex.
        PeriodIndex.shift : Shift values of PeriodIndex.