我有由DatetimeIndex索引的数据框（系列）
tag
2015-08-21 16:32:00 stationary
2015-08-21 16:33:00 automotive
2015-08-21 16:34:00 automotive
2015-08-21 17:27:00 stationary
2015-08-21 17:28:00 stationary
2015-08-21 17:29:00 stationary
2015-08-21 17:30:00 stationary
2015-08-21 17:31:00 stationary
2015-08-21 17:32:00 stationary
2015-08-24 16:55:00 automotive
2015-08-24 16:56:00 automotive
2015-08-24 16:57:00 automotive
2015-08-24 16:58:00 automotive
2015-08-24 16:59:00 stationary
2015-08-24 17:00:00 stationary
2015-08-24 17:01:00 stationary
想要分组标记和聚合时间索引，所以预期结果是
Start End Tag
- 2015-08-21 16:32:00 stationary
2015-08-21 16:34:00 2015-08-21 16:34:00 automotive
2015-08-21 17:27:00 2015-08-21 17:32:00 stationary
2015-08-24 16:55:00 2015-08-24 16:58:00 automotive
2015-08-24 16:59:00 2015-08-24 17:01:00 stationary
答案 0 :(得分：3)
您可以使用groupby和apply计划。
def func(group):
return pd.Series({'Start': group.index[0], 'End': group.index[-1], 'Tag': group['tag'].values[0]})
df.groupby((df.shift(1) != df).cumsum()['tag'], as_index=False).apply(func)
End Start Tag
0 2015-08-21 16:32:00 2015-08-21 16:32:00 stationary
1 2015-08-21 16:34:00 2015-08-21 16:33:00 automotive
2 2015-08-21 17:32:00 2015-08-21 17:27:00 stationary
3 2015-08-24 16:58:00 2015-08-24 16:55:00 automotive
4 2015-08-24 17:01:00 2015-08-24 16:59:00 stationary
答案 1 :(得分：2)
不确定您的预期结果是否正确，我认为这是正确的预期结果：
begin end tag
0 2015-08-21 16:32:00 2015-08-21 16:32:00 stationary
1 2015-08-21 16:33:00 2015-08-21 16:34:00 automotive
3 2015-08-21 17:27:00 2015-08-21 17:32:00 stationary
9 2015-08-24 16:55:00 2015-08-24 16:58:00 automotive
13 2015-08-24 16:59:00 2015-08-24 17:01:00 stationary
以下是如何获得它：
import pandas as pd
import numpy as np
from datetime import datetime
# Prepare data from your example
data = [
("2015-08-21 16:32:00", "stationary"),
("2015-08-21 16:33:00", "automotive"),
("2015-08-21 16:34:00", "automotive"),
("2015-08-21 17:27:00", "stationary"),
("2015-08-21 17:28:00", "stationary"),
("2015-08-21 17:29:00", "stationary"),
("2015-08-21 17:30:00", "stationary"),
("2015-08-21 17:31:00", "stationary"),
("2015-08-21 17:32:00", "stationary"),
("2015-08-24 16:55:00", "automotive"),
("2015-08-24 16:56:00", "automotive"),
("2015-08-24 16:57:00", "automotive"),
("2015-08-24 16:58:00", "automotive"),
("2015-08-24 16:59:00", "stationary"),
("2015-08-24 17:00:00", "stationary"),
("2015-08-24 17:01:00", "stationary")]
data = [(datetime.strptime(x[0], "%Y-%m-%d %H:%M:%S"), x[1]) for x in data]
df = pd.DataFrame(data, columns=['ts', 'tag']).sort('ts')
df['is_first'] = df.tag != df.tag.shift()
df['is_last'] = df.tag != df.tag.shift(-1)
# Fill begin timestamp, only on first occurences
df['begin'] = df.ts
df.loc[~df.is_first, 'begin'] = pd.NaT
# Fill end timestamp, only on last occurences
df['end'] = df.ts
df.loc[~df.is_last, 'end'] = pd.NaT
# Fill NaT with next end
df['end'] = df['end'].bfill()
# Restrict to changes
df = df[df.is_first]
# Remove useless columns
df = df[['begin', 'end', 'tag']].sort('begin')