好吧，找到了一个方法，想在这里分享给其他有类似问题的人：

# Initialize variables to store the current interval for each group
current_group = None
current_start = None
current_end = None

# Lists to store the resulting data
result_groups = []
result_start = []
result_end = []

# Iterate through the DataFrame
for _, row in df.iterrows():
    group = row['id']
    start = row['start']
    end = row['end']
    
    # If the group changes, start a new interval
    if group != current_group:
        current_group = group
        current_start = start
        current_end = end
    else:
        # Check if the start time is within the current interval
        if start <= current_end:
            # Update the end time if the current row's end time is greater
            current_end = max(current_end, end)
        else:
            # Save the current interval and start a new one
            result_groups.append(current_group)
            result_start.append(current_start)
            result_end.append(current_end)
            current_start = start
            current_end = end

# Save the last interval
result_groups.append(current_group)
result_start.append(current_start)
result_end.append(current_end)

# Create a new DataFrame with the results
result_df = pd.DataFrame({'Group': result_groups, 'Start_Time': result_start, 'End_Time': result_end})

如果数据按"id"和"start"排序，我们可以创建额外的累积最大结束列，然后在出现新的组id或开始数据大于前一个累积最大结束的情况下拆分数据，并在每个获得的部分中获得第一个开始日期和最后一个累积最大结束日期作为聚合值：

df['cummax_end'] = df.groupby('id', as_index=False, sort=False)['end'].cummax()
is_new_group = df['id'] != df['id'].shift()
is_new_interval = df['start'] > df['cummax_end'].shift()
grouper = (is_new_group | is_new_interval).cumsum()

answer = df.groupby(grouper).agg({
    'id': 'first', 
    'start': 'first', 
    'cummax_end': 'last'
})

对测试数据

data = '''\
id |       start         |        end         |
---|---------------------|--------------------|
TA | 2022-05-20 06:30:36 | 2022-05-20 09:58:52|
TA | 2022-05-20 08:47:13 | 2022-05-20 08:57:47|
TA | 2022-05-20 08:44:11 | 2022-05-20 10:15:14|
TA | 2022-06-10 07:45:11 | 2022-06-10 10:15:14|
TA | 2022-06-10 07:55:11 | 2022-06-10 11:15:14|
BA | 2022-05-24 08:48:12 | 2022-05-24 10:57:27|
BA | 2022-05-24 10:48:29 | 2022-05-24 12:08:54|
RG | 2022-05-31 07:57:26 | 2022-05-31 08:09:46|
RG | 2022-05-31 08:06:50 | 2022-05-31 08:08:49|
RG | 2022-05-31 08:07:51 | 2022-05-31 08:18:37|
'''

df = pd.read_table(
    StringIO(data), 
    sep = ' *\| *', 
    skiprows=[1],
    engine='python',
    usecols=[0,1,2], 
    parse_dates=[1, 2],
)

我们得到输出

>>> print(answer.to_string(index=False))
id               start          cummax_end
TA 2022-05-20 06:30:36 2022-05-20 10:15:14
TA 2022-06-10 07:45:11 2022-06-10 11:15:14
BA 2022-05-24 08:48:12 2022-05-24 12:08:54
RG 2022-05-31 07:57:26 2022-05-31 08:18:37