分割 pandas 数据框 [英] splitting a pandas Dataframe
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问题描述
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：
I would like to filter and split my original dataframe into a number of dataframes using the condition that progressPercentage goes from 1.0 to 100 as in the following example:
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
I一直按以下方式使用.shift（）和groupby：
I have been using the .shift() and groupby as in the following:
a = dfb['Operation.progressPercentage'].shift().eq(100)
grouping = dfb.groupby([dfb.wm_id,a])
，但未提供预期结果。
请帮助我修改代码以完成工作？
but it did not provide the expected results. Please, any help on how I should change the code to get it done?
非常感谢。
最好的问候，
Carlo
Many Thanks in advance. Best Regards, Carlo
推荐答案
如果完成值有时会丢失，仅需要使用 progressPercentage 列使用：
If Finish value sometimes missing and need use only progressPercentage column use:
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)
...
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