Method ohlc

pandas/core/groupby/groupby.py:3543–3654

Compute open, high, low and close values of a group, excluding missing values. For multiple groupings, the result index will be a MultiIndex Returns ------- DataFrame Open, high, low and close values within each group. See Also

(self)

    @final
    def ohlc(self) -> DataFrame:
        """
        Compute open, high, low and close values of a group, excluding missing values.

        For multiple groupings, the result index will be a MultiIndex

        Returns
        -------
        DataFrame
            Open, high, low and close values within each group.

        See Also
        --------
        DataFrame.agg : Aggregate using one or more operations over the specified axis.
        DataFrame.resample : Resample time-series data.
        DataFrame.groupby : Group DataFrame using a mapper or by a Series of columns.

        Examples
        --------

        For SeriesGroupBy:

        >>> lst = [
        ...     "SPX",
        ...     "CAC",
        ...     "SPX",
        ...     "CAC",
        ...     "SPX",
        ...     "CAC",
        ...     "SPX",
        ...     "CAC",
        ... ]
        >>> ser = pd.Series([3.4, 9.0, 7.2, 5.2, 8.8, 9.4, 0.1, 0.5], index=lst)
        >>> ser
        SPX     3.4
        CAC     9.0
        SPX     7.2
        CAC     5.2
        SPX     8.8
        CAC     9.4
        SPX     0.1
        CAC     0.5
        dtype: float64
        >>> ser.groupby(level=0).ohlc()
             open  high  low  close
        CAC   9.0   9.4  0.5    0.5
        SPX   3.4   8.8  0.1    0.1

        For DataFrameGroupBy:

        >>> data = {
        ...     2022: [1.2, 2.3, 8.9, 4.5, 4.4, 3, 2, 1],
        ...     2023: [3.4, 9.0, 7.2, 5.2, 8.8, 9.4, 8.2, 1.0],
        ... }
        >>> df = pd.DataFrame(
        ...     data, index=["SPX", "CAC", "SPX", "CAC", "SPX", "CAC", "SPX", "CAC"]
        ... )
        >>> df