Question

我有一个像下面给出的pandas数据框

 override func viewDidAppear(_ animated: Bool) {
    super.viewDidAppear(animated)

    AppUtility.lockOrientation(.portrait)
    // Or to rotate and lock
    // AppUtility.lockOrientation(.portrait, andRotateTo: .portrait)

}

override func viewWillDisappear(_ animated: Bool) {
    super.viewWillDisappear(animated)

    // Don't forget to reset when view is being removed
    AppUtility.lockOrientation(.all)
}

我想要

start_time                     end_time   value                                
2017-01-09 21:49:55 2017-01-09 21:55:41  150.0
2017-01-09 21:55:41 2017-01-09 21:58:46    4.0
2017-01-09 22:00:55 2017-01-09 23:13:00  144.0

由于前两行是连续事件，我将其加入一行并添加了它们的值。任何建议如何进行。

Answer 1

假设您的数据框已按时间排序

from datetime import datetime
import pandas as pd

'''
start_time                     end_time   value                                
2017-01-09 21:49:55 2017-01-09 21:55:41  150.0
2017-01-09 21:55:41 2017-01-09 21:58:46    4.0
2017-01-09 22:00:55 2017-01-09 23:13:00  144.0
'''

# your dataframe

df = pd.DataFrame({'start_time': [datetime(2017,1,9,21,49,55), datetime(2017,1,9,21,55,41),datetime(2017,1,9,22,00,55)], \
                    'end_time': [datetime(2017,1,9,21,55,41), datetime(2017,1,9,21,58,46),datetime(2017,1,9,23,13,00)], \
                    'value': [150.0, 4.0, 144.0]})

获取连续时间范围的第一个start_time，以及连续时间范围的最新end_time：

df['start_time_'] = df['start_time'].loc[df['end_time'].shift(1) != df['start_time']]
df['end_time_'] = df['end_time'].loc[df['end_time'] != df['start_time'].shift(-1)]

print(df)

现在df如下所示：

  start_time          end_time             value start_time_         end_time_
0 2017-01-09 21:49:55 2017-01-09 21:55:41  150.0 2017-01-09 21:49:55                 NaT
1 2017-01-09 21:55:41 2017-01-09 21:58:46    4.0                 NaT 2017-01-09 21:58:46
2 2017-01-09 22:00:55 2017-01-09 23:13:00  144.0 2017-01-09 22:00:55 2017-01-09 23:13:00

然后填写NA值：

df['start_time_'].fillna(method='ffill',inplace=True)
df['end_time_'].fillna(method='bfill',inplace=True)

使用start_time_，end_time_列来替换start_time，end_time列。并删除start_time_，end_time_列：

df['start_time'] = df['start_time_']
df['end_time'] = df['end_time_']
del df['start_time_'] 
del df['end_time_']

然后分组和总和：

df = df.groupby(['start_time', 'end_time'], as_index=False).sum()

print(df)

结果如下：

           start_time            end_time  value
0 2017-01-09 21:49:55 2017-01-09 21:58:46  154.0
1 2017-01-09 22:00:55 2017-01-09 23:13:00  144.0

Answer 2

可能不是最优雅的方式，但它有点有效：

df = pd.DataFrame({"start":[1,2,4], "end":[2,3,5], 'val':[1,1,1]})
tmp = df.set_index('end', drop=0).join(df.set_index('start', drop=0), how='left', rsuffix='_r')
tmp.val = tmp.val_r.fillna(0) + tmp.val
df = tmp[~tmp.end.isin(tmp.end_r)][['start', 'end', 'val']]
df.index = range(len(df))

如何在pandas DataFrame中将2行与连续时间戳组合？

2 个答案: