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# pandas.api.typing.SeriesGroupBy.ohlc

SeriesGroupBy.ohlc()

  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
           2022  2023
      SPX   1.2   3.4
      CAC   2.3   9.0
      SPX   8.9   7.2
      CAC   4.5   5.2
      SPX   4.4   8.8
      CAC   3.0   9.4
      SPX   2.0   8.2
      CAC   1.0   1.0
      >>> df.groupby(level=0).ohlc()
          2022                 2023
          open high  low close open high  low close
      CAC  2.3  4.5  1.0   1.0  9.0  9.4  1.0   1.0
      SPX  1.2  8.9  1.2   2.0  3.4  8.8  3.4   8.2

  For Resampler:

      >>> ser = pd.Series(
      ...     [1, 3, 2, 4, 3, 5],
      ...     index=pd.DatetimeIndex(
      ...         [
      ...             "2023-01-01",
      ...             "2023-01-10",
      ...             "2023-01-15",
      ...             "2023-02-01",
      ...             "2023-02-10",
      ...             "2023-02-15",
      ...         ]
      ...     ),
      ... )
      >>> ser.resample("MS").ohlc()
                  open  high  low  close
      2023-01-01     1     3    1      2
      2023-02-01     4     5    3      5