Keep row and next one based on conditions - Pandas
I need to clean a dataset that is similar to the following:
https://i.stack.imgur.com/yMjuy.png
The expected result looks like this:
https://i.stack.imgur.com/QFJHS.png
In other words, I need to keep all rows that (1) have 'action' in the column 'ACTION' and the immediate next one - (2) if the next one has 'result' in that column.
I've tried several combination of .shift(), but it did not work.
Thanks in advance.
pandas
asked Nov 13 '18 at 17:32
E. FasloE. Faslo
113
add a comment |
I need to clean a dataset that is similar to the following:
https://i.stack.imgur.com/yMjuy.png
The expected result looks like this:
https://i.stack.imgur.com/QFJHS.png
In other words, I need to keep all rows that (1) have 'action' in the column 'ACTION' and the immediate next one - (2) if the next one has 'result' in that column.
I've tried several combination of .shift(), but it did not work.
Thanks in advance.
pandas
asked Nov 13 '18 at 17:32
E. FasloE. Faslo
113
add a comment |
I need to clean a dataset that is similar to the following:
https://i.stack.imgur.com/yMjuy.png
The expected result looks like this:
https://i.stack.imgur.com/QFJHS.png
In other words, I need to keep all rows that (1) have 'action' in the column 'ACTION' and the immediate next one - (2) if the next one has 'result' in that column.
I've tried several combination of .shift(), but it did not work.
Thanks in advance.
pandas
asked Nov 13 '18 at 17:32
E. FasloE. Faslo
113
I need to clean a dataset that is similar to the following:
https://i.stack.imgur.com/yMjuy.png
The expected result looks like this:
https://i.stack.imgur.com/QFJHS.png
In other words, I need to keep all rows that (1) have 'action' in the column 'ACTION' and the immediate next one - (2) if the next one has 'result' in that column.
I've tried several combination of .shift(), but it did not work.
Thanks in advance.
pandas
pandas
asked Nov 13 '18 at 17:32
E. FasloE. Faslo
113
asked Nov 13 '18 at 17:32
E. FasloE. Faslo
113
edited Nov 14 '18 at 9:19
E. Faslo
asked Nov 13 '18 at 17:32
E. FasloE. Faslo
113
asked Nov 13 '18 at 17:32
E. FasloE. Faslo
113
asked Nov 13 '18 at 17:32
E. FasloE. Faslo
113
113
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
You could try something like this:
df.assign(step=df['Action'].eq('action').cumsum())
.drop_duplicates(subset=['Action','step'], keep='first')
.groupby('step')
.filter(lambda x: x.step.count()==2)
Output:
SESSION Action TIME step
0 1 action 0.1 1
1 1 result 0.2 1
2 2 action 0.1 2
3 2 result 0.2 2
4 3 action 0.3 3
5 3 result 0.4 3
7 1 action 0.3 4
8 1 result 0.4 4
12 3 action 0.6 6
13 3 result 0.7 6
14 4 action 0.8 7
15 4 result 0.9 7
answered Nov 13 '18 at 17:53
Scott BostonScott Boston
52.9k72955
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
You could try something like this:
df.assign(step=df['Action'].eq('action').cumsum())
.drop_duplicates(subset=['Action','step'], keep='first')
.groupby('step')
.filter(lambda x: x.step.count()==2)
Output:
SESSION Action TIME step
0 1 action 0.1 1
1 1 result 0.2 1
2 2 action 0.1 2
3 2 result 0.2 2
4 3 action 0.3 3
5 3 result 0.4 3
7 1 action 0.3 4
8 1 result 0.4 4
12 3 action 0.6 6
13 3 result 0.7 6
14 4 action 0.8 7
15 4 result 0.9 7
answered Nov 13 '18 at 17:53
Scott BostonScott Boston
52.9k72955
add a comment |
You could try something like this:
df.assign(step=df['Action'].eq('action').cumsum())
.drop_duplicates(subset=['Action','step'], keep='first')
.groupby('step')
.filter(lambda x: x.step.count()==2)
Output:
SESSION Action TIME step
0 1 action 0.1 1
1 1 result 0.2 1
2 2 action 0.1 2
3 2 result 0.2 2
4 3 action 0.3 3
5 3 result 0.4 3
7 1 action 0.3 4
8 1 result 0.4 4
12 3 action 0.6 6
13 3 result 0.7 6
14 4 action 0.8 7
15 4 result 0.9 7
answered Nov 13 '18 at 17:53
Scott BostonScott Boston
52.9k72955
add a comment |
You could try something like this:
df.assign(step=df['Action'].eq('action').cumsum())
.drop_duplicates(subset=['Action','step'], keep='first')
.groupby('step')
.filter(lambda x: x.step.count()==2)
Output:
SESSION Action TIME step
0 1 action 0.1 1
1 1 result 0.2 1
2 2 action 0.1 2
3 2 result 0.2 2
4 3 action 0.3 3
5 3 result 0.4 3
7 1 action 0.3 4
8 1 result 0.4 4
12 3 action 0.6 6
13 3 result 0.7 6
14 4 action 0.8 7
15 4 result 0.9 7
answered Nov 13 '18 at 17:53
Scott BostonScott Boston
52.9k72955
You could try something like this:
df.assign(step=df['Action'].eq('action').cumsum())
.drop_duplicates(subset=['Action','step'], keep='first')
.groupby('step')
.filter(lambda x: x.step.count()==2)
Output:
SESSION Action TIME step
0 1 action 0.1 1
1 1 result 0.2 1
2 2 action 0.1 2
3 2 result 0.2 2
4 3 action 0.3 3
5 3 result 0.4 3
7 1 action 0.3 4
8 1 result 0.4 4
12 3 action 0.6 6
13 3 result 0.7 6
14 4 action 0.8 7
15 4 result 0.9 7
answered Nov 13 '18 at 17:53
Scott BostonScott Boston
52.9k72955
answered Nov 13 '18 at 17:53
Scott BostonScott Boston
52.9k72955
answered Nov 13 '18 at 17:53
Scott BostonScott Boston
52.9k72955
answered Nov 13 '18 at 17:53
Scott BostonScott Boston
52.9k72955
52.9k72955
add a comment |
add a comment |
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