我想使用progressPercentage从1.0变为100的条件，将原始数据帧过滤并拆分为多个数据帧，如下例所示：
输入：
id_B, ts_B,course,weight,Phase,remainingTime,progressPercentage
id1,2017-04-27 01:35:30,cotton,3.5,A,01:15:00,23.0
id1,2017-04-27 01:37:30,cotton,3.5,B,01:13:00,24.0
id1,2017-04-27 01:38:00,cotton,3.5,B,01:13:00,24.0
id1,2017-04-27 01:38:30,cotton,3.5,C,01:13:00,24.0
id1,2017-04-27 01:39:00,cotton,3.5,C,00:02:00,99.0
id1,2017-04-27 01:39:30,cotton,3.5,C,00:01:00,100.0
id1,2017-04-27 01:40:00,cotton,3.5,Finish,00:01:00,100.0
id1,2017-04-27 02:35:30,cotton,3.5,A,03:15:00,1.0
id1,2017-04-27 02:36:00,cotton,3.5,A,03:14:00,2.0
id1,2017-04-27 02:36:30,cotton,3.5,A,03:14:00,2.0
id1,2017-04-27 02:37:00,cotton,3.5,B,03:13:00,3.0
id1,2017-04-27 02:37:30,cotton,3.5,B,03:13:00,4.0
id1,2017-04-27 02:38:00,cotton,3.5,B,03:13:00,5.0
id1,2017-04-27 02:38:30,cotton,3.5,C,03:13:00,98.0
id1,2017-04-27 02:39:00,cotton,3.5,C,00:02:00,99.0
id1,2017-04-27 02:39:30,cotton,3.5,C,00:01:00,100.0
id1,2017-04-27 02:40:00,cotton,3.5,Finish,00:01:00,100.0
id2,2017-04-27 03:36:00,cotton,3.5,A,03:15:00,1.0
id2,2017-04-27 03:36:30,cotton,3.5,A,03:14:00,1.0
id2,2017-04-27 03:37:00,cotton,3.5,B,03:13:00,2.0
id2,2017-04-27 03:37:30,cotton,3.5,B,03:13:00,2.0
id2,2017-04-27 03:38:00,cotton,3.5,B,03:13:00,3.0
id2,2017-04-27 03:38:30,cotton,3.5,C,03:13:00,98.0
id2,2017-04-27 03:39:00,cotton,3.5,C,00:02:00,99.0
id2,2017-04-27 03:39:30,cotton,3.5,C,00:01:00,100.0
id2,2017-04-27 03:40:00,cotton,3.5,Finish,00:01:00,100.0
id1,2017-05-27 01:35:30,cotton,3.5,A,03:15:00,23.0
id1,2017-05-27 01:37:30,cotton,3.5,B,03:13:00,24.0
id1,2017-05-27 01:38:00,cotton,3.5,B,03:13:00,24.0
id1,2017-05-27 01:38:30,cotton,3.5,C,03:13:00,24.0
id1,2017-05-27 01:39:00,cotton,3.5,C,00:02:00,99.0
id1,2017-05-27 01:39:30,cotton,3.5,C,00:01:00,100.0
id1,2017-05-27 01:40:00,cotton,3.5,Finish,00:01:00,100.0
id1,2017-05-27 02:35:30,cotton,3.5,A,01:15:00,1.0
id1,2017-05-27 02:36:00,cotton,3.5,A,01:14:00,2.0
id1,2017-05-27 02:36:30,cotton,3.5,A,01:13:00,2.0
id1,2017-05-27 02:37:00,cotton,3.5,B,01:12:00,3.0
id1,2017-05-27 02:37:30,cotton,3.5,B,01:11:00,4.0
id1,2017-05-27 02:38:00,cotton,3.5,B,01:10:00,5.0
id1,2017-05-27 02:38:30,cotton,3.5,C,01:09:00,98.0
id1,2017-05-27 02:39:00,cotton,3.5,C,00:08:00,99.0
输出：
id_B, ts_B,course,weight,Phase,remainingTime,progressPercentage
id1,2017-04-27 01:35:30,cotton,3.5,A,01:15:00,23.0
id1,2017-04-27 01:37:30,cotton,3.5,B,01:13:00,24.0
id1,2017-04-27 01:38:00,cotton,3.5,B,01:13:00,24.0
id1,2017-04-27 01:38:30,cotton,3.5,C,01:13:00,24.0
id1,2017-04-27 01:39:00,cotton,3.5,C,00:02:00,99.0
id1,2017-04-27 01:39:30,cotton,3.5,C,00:01:00,100.0
id1,2017-04-27 01:40:00,cotton,3.5,Finish,00:01:00,100.0
id_B, ts_B,course,weight,Phase,remainingTime,progressPercentage
id1,2017-04-27 02:35:30,cotton,3.5,A,03:15:00,1.0
id1,2017-04-27 02:36:00,cotton,3.5,A,03:14:00,2.0
id1,2017-04-27 02:36:30,cotton,3.5,A,03:14:00,2.0
id1,2017-04-27 02:37:00,cotton,3.5,B,03:13:00,3.0
id1,2017-04-27 02:37:30,cotton,3.5,B,03:13:00,4.0
id1,2017-04-27 02:38:00,cotton,3.5,B,03:13:00,5.0
id1,2017-04-27 02:38:30,cotton,3.5,C,03:13:00,98.0
id1,2017-04-27 02:39:00,cotton,3.5,C,00:02:00,99.0
id1,2017-04-27 02:39:30,cotton,3.5,C,00:01:00,100.0
id1,2017-04-27 02:40:00,cotton,3.5,Finish,00:01:00,100.0
id_B, ts_B,course,weight,Phase,remainingTime,progressPercentage
id2,2017-04-27 03:36:00,cotton,3.5,A,03:15:00,1.0
id2,2017-04-27 03:36:30,cotton,3.5,A,03:14:00,1.0
id2,2017-04-27 03:37:00,cotton,3.5,B,03:13:00,2.0
id2,2017-04-27 03:37:30,cotton,3.5,B,03:13:00,2.0
id2,2017-04-27 03:38:00,cotton,3.5,B,03:13:00,3.0
id2,2017-04-27 03:38:30,cotton,3.5,C,03:13:00,98.0
id2,2017-04-27 03:39:00,cotton,3.5,C,00:02:00,99.0
id2,2017-04-27 03:39:30,cotton,3.5,C,00:01:00,100.0
id2,2017-04-27 03:40:00,cotton,3.5,Finish,00:01:00,100.0
id_B, ts_B,course,weight,Phase,remainingTime,progressPercentage
id1,2017-05-27 01:35:30,cotton,3.5,A,03:15:00,1.0
id1,2017-05-27 01:37:30,cotton,3.5,B,03:13:00,2.0
id1,2017-05-27 01:38:00,cotton,3.5,B,03:13:00,3.0
id1,2017-05-27 01:38:30,cotton,3.5,C,03:13:00,4.0
id1,2017-05-27 01:39:00,cotton,3.5,C,00:02:00,99.0
id1,2017-05-27 01:39:30,cotton,3.5,C,00:01:00,100.0
id1,2017-05-27 01:40:00,cotton,3.5,Finish,00:01:00,100.0
id_B, ts_B,course,weight,Phase,remainingTime,progressPercentage
id1,2017-05-27 02:35:30,cotton,3.5,A,01:15:00,1.0
id1,2017-05-27 02:36:00,cotton,3.5,A,01:14:00,2.0
id1,2017-05-27 02:36:30,cotton,3.5,A,01:13:00,2.0
id1,2017-05-27 02:37:00,cotton,3.5,B,01:12:00,3.0
id1,2017-05-27 02:37:30,cotton,3.5,B,01:11:00,4.0
id1,2017-05-27 02:38:00,cotton,3.5,B,01:10:00,5.0
id1,2017-05-27 02:38:30,cotton,3.5,C,01:09:00,98.0
id1,2017-05-27 02:39:00,cotton,3.5,C,00:08:00,99.0
id1,2017-05-27 02:39:00,cotton,3.5,C,00:01:00,100.0
我一直在使用.shift（）和groupby，如下所示：
a = dfb['Operation.progressPercentage'].shift().eq(100)
grouping = dfb.groupby([dfb.wm_id,a])
但它没有提供预期的结果。 请问有关如何更改代码以完成任务的任何帮助？
非常感谢提前。 最好的祝福， 卡罗
答案 0 :(得分：2)
您可以将progressPercentage等于100除以数据帧。如果它们是连续的，则删除先前的索引。然后将数据帧切片并追加到数组中。希望这有帮助
import numpy as np
df = pd.read_csv('input.csv',delimiter=',') # The input csv provided
df1 = df[(df["progressPercentage "]==100)]
x = (np.array(df1.index) + 1).tolist()
x.insert(0,0)
#Remove the consecutive elements so that they can be treated under one dataframe.
x = [ begin for begin, end in zip(x, x[1:]) if (begin != end-1)]
x.insert(len(x),df.shape[0])
frames = [df.iloc[begin:end] for begin, end in zip(x, x[1:])]
您可以使用for循环打印数据帧，即
for df in frames:
print(df)
数据帧的输出：
id_B ts_B course weight Phase remainingTime \
0 id1 2017-04-27 01:35:30 cotton 3.5 A 01:15:00
1 id1 2017-04-27 01:37:30 cotton 3.5 B 01:13:00
2 id1 2017-04-27 01:38:00 cotton 3.5 B 01:13:00
3 id1 2017-04-27 01:38:30 cotton 3.5 C 01:13:00
4 id1 2017-04-27 01:39:00 cotton 3.5 C 00:02:00
5 id1 2017-04-27 01:39:30 cotton 3.5 C 00:01:00
6 id1 2017-04-27 01:40:00 cotton 3.5 Finish 00:01:00
progressPercentage
0 23.0
1 24.0
2 24.0
3 24.0
4 99.0
5 100.0
6 100.0
id_B ts_B course weight Phase remainingTime \
7 id1 2017-04-27 02:35:30 cotton 3.5 A 03:15:00
8 id1 2017-04-27 02:36:00 cotton 3.5 A 03:14:00
9 id1 2017-04-27 02:36:30 cotton 3.5 A 03:14:00
10 id1 2017-04-27 02:37:00 cotton 3.5 B 03:13:00
11 id1 2017-04-27 02:37:30 cotton 3.5 B 03:13:00
12 id1 2017-04-27 02:38:00 cotton 3.5 B 03:13:00
13 id1 2017-04-27 02:38:30 cotton 3.5 C 03:13:00
14 id1 2017-04-27 02:39:00 cotton 3.5 C 00:02:00
15 id1 2017-04-27 02:39:30 cotton 3.5 C 00:01:00
16 id1 2017-04-27 02:40:00 cotton 3.5 Finish 00:01:00
progressPercentage
7 1.0
8 2.0
9 2.0
10 3.0
11 4.0
12 5.0
13 98.0
14 99.0
15 100.0
16 100.0
id_B ts_B course weight Phase remainingTime \
17 id2 2017-04-27 03:36:00 cotton 3.5 A 03:15:00
18 id2 2017-04-27 03:36:30 cotton 3.5 A 03:14:00
19 id2 2017-04-27 03:37:00 cotton 3.5 B 03:13:00
20 id2 2017-04-27 03:37:30 cotton 3.5 B 03:13:00
21 id2 2017-04-27 03:38:00 cotton 3.5 B 03:13:00
22 id2 2017-04-27 03:38:30 cotton 3.5 C 03:13:00
23 id2 2017-04-27 03:39:00 cotton 3.5 C 00:02:00
24 id2 2017-04-27 03:39:30 cotton 3.5 C 00:01:00
25 id2 2017-04-27 03:40:00 cotton 3.5 Finish 00:01:00
progressPercentage
17 1.0
18 1.0
19 2.0
20 2.0
21 3.0
22 98.0
23 99.0
24 100.0
25 100.0
id_B ts_B course weight Phase remainingTime \
26 id1 2017-05-27 01:35:30 cotton 3.5 A 03:15:00
27 id1 2017-05-27 01:37:30 cotton 3.5 B 03:13:00
28 id1 2017-05-27 01:38:00 cotton 3.5 B 03:13:00
29 id1 2017-05-27 01:38:30 cotton 3.5 C 03:13:00
30 id1 2017-05-27 01:39:00 cotton 3.5 C 00:02:00
31 id1 2017-05-27 01:39:30 cotton 3.5 C 00:01:00
32 id1 2017-05-27 01:40:00 cotton 3.5 Finish 00:01:00
progressPercentage
26 23.0
27 24.0
28 24.0
29 24.0
30 99.0
31 100.0
32 100.0
id_B ts_B course weight Phase remainingTime \
33 id1 2017-05-27 02:35:30 cotton 3.5 A 01:15:00
34 id1 2017-05-27 02:36:00 cotton 3.5 A 01:14:00
35 id1 2017-05-27 02:36:30 cotton 3.5 A 01:13:00
36 id1 2017-05-27 02:37:00 cotton 3.5 B 01:12:00
37 id1 2017-05-27 02:37:30 cotton 3.5 B 01:11:00
38 id1 2017-05-27 02:38:00 cotton 3.5 B 01:10:00
39 id1 2017-05-27 02:38:30 cotton 3.5 C 01:09:00
40 id1 2017-05-27 02:39:00 cotton 3.5 C 00:08:00
41 id1 2017-05-27 02:39:00 cotton 3.5 C 00:08:00
progressPercentage
33 1.0
34 2.0
35 2.0
36 3.0
37 4.0
38 5.0
39 98.0
40 99.0
41 100.0
id_B ts_B course weight Phase remainingTime \
42 id2 2017-04-27 03:36:00 cotton 3.5 A 03:15:00
43 id2 2017-04-27 03:36:30 cotton 3.5 A 03:14:00
44 id2 2017-04-27 03:37:00 cotton 3.5 B 03:13:00
45 id2 2017-04-27 03:37:30 cotton 3.5 B 03:13:00
46 id2 2017-04-27 03:38:00 cotton 3.5 B 03:13:00
47 id2 2017-04-27 03:38:30 cotton 3.5 C 03:13:00
48 id2 2017-04-27 03:39:00 cotton 3.5 C 00:02:00
49 id2 2017-04-27 03:39:30 cotton 3.5 C 00:01:00
50 id2 2017-04-27 03:40:00 cotton 3.5 Finish 00:01:00
progressPercentage
42 1.0
43 1.0
44 2.0
45 2.0
46 3.0
47 98.0
48 99.0
49 100.0
50 100.0
答案 1 :(得分：2)
如果Finish值有时会丢失，只需使用progressPercentage列：
shifted = df['progressPercentage'].shift()
#compare difference for second 100 if together 100 (e.g. 15, 16 row)
m = shifted.diff(-1).ne(0) & shifted.eq(100)
a = m.cumsum()
aa = df.groupby([df.id_B,a])
for k, gp in aa:
print('key=' + str(k))
print(gp)
print('A NEW ONE...')
key=('id1', 0)
id_B ts_B course weight Phase remainingTime \
0 id1 2017-04-27 01:35:30 cotton 3.5 A 01:15:00
1 id1 2017-04-27 01:37:30 cotton 3.5 B 01:13:00
2 id1 2017-04-27 01:38:00 cotton 3.5 B 01:13:00
3 id1 2017-04-27 01:38:30 cotton 3.5 C 01:13:00
4 id1 2017-04-27 01:39:00 cotton 3.5 C 00:02:00
5 id1 2017-04-27 01:39:30 cotton 3.5 C 00:01:00
6 id1 2017-04-27 01:40:00 cotton 3.5 Finish 00:01:00
progressPercentage
0 23.0
1 24.0
2 24.0
3 24.0
4 99.0
5 100.0
6 100.0
A NEW ONE...
key=('id1', 1)
id_B ts_B course weight Phase remainingTime \
7 id1 2017-04-27 02:35:30 cotton 3.5 A 03:15:00
8 id1 2017-04-27 02:36:00 cotton 3.5 A 03:14:00
9 id1 2017-04-27 02:36:30 cotton 3.5 A 03:14:00
10 id1 2017-04-27 02:37:00 cotton 3.5 B 03:13:00
11 id1 2017-04-27 02:37:30 cotton 3.5 B 03:13:00
12 id1 2017-04-27 02:38:00 cotton 3.5 B 03:13:00
13 id1 2017-04-27 02:38:30 cotton 3.5 C 03:13:00
14 id1 2017-04-27 02:39:00 cotton 3.5 C 00:02:00
15 id1 2017-04-27 02:39:30 cotton 3.5 C 00:01:00
16 id1 2017-04-27 02:40:00 cotton 3.5 Finish 00:01:00
progressPercentage
7 1.0
8 2.0
9 2.0
10 3.0
11 4.0
12 5.0
13 98.0
14 99.0
15 100.0
16 100.0
A NEW ONE...
key=('id2', 2)
...
答案 2 :(得分：0)
我发现的最佳方式如下：
a = dfb['progressPercentage'].shift().eq(100).cumsum()
df_output = dfb.groupby([dfb.id_B,a])
for k, gp in aa:
print('key=' + str(k))
print(gp.sort_values(['eventTime', 'wm_id'], ascending=[1, 0]).to_string())
print('A NEW ONE...')