pandas.core.resample.Resampler.ohlc#

Resampler.ohlc(*args, **kwargs)[source]#

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.

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