Parsing Data From Pandas Dataframe-CodePudding

I have a dataframe with random repeating sequences. I need to set a flag of sorts so that every time I come across the term maintenance, I store the rows between each instance. " store everything between the two maintenance instances." The input is:

NAME     PROCESS     STATUS
name 0   process 0  
name 1   maintenance start
name 2   process 2
name 3   process 3
name 4   process 4
name 5   process 5
name 6   maintenance stop
name 7   process 7
name 8   process 8
name 9   process 9 
name 10  maintenance start
name 11  process 11
name 12  process 12
name 13  process 13
name 14  process 14
name 15  maintenance stop

I was thinking about doing a set of logical conditions:

for i in np.arange(len(df)-1):
  if df['process'][i] = 'maintenance':
     df['new'] = df[i]

but, I am hoping there is a way that pandas can handle this (that I cant find) as I cant seem to figure out the stopping condition.

desired output:

name 2   process 2
name 3   process 3
name 4   process 4
name 5   process 5
name 11  process 11
name 12  process 12
name 13  process 13
name 14  process 14

CodePudding user response：

You can iterate over the rows in data frame and start appending rows to new frame when process="maintenance" and status="start" and stop adding them when status="stop".

import pandas as pd
from io import StringIO

data = """name,process,status
name 0,process 0,
name 1,maintenance,start
name 2,process 2,
name 3,process 3,
name 4,process 4
name 5,process 5
name 6,maintenance,stop
name 7,process 7
name 8,process 8
name 9,process 9 
name 10,maintenance,start
name 11,process 11
name 12,process 12
name 13,process 13
name 14,process 14
name 15,maintenance,stop
"""

df = pd.read_csv(StringIO(data)).fillna('')

row_list = []
collect = False
for row in df.itertuples(index=False):
    if row.process == "maintenance":
        collect = row.status == "start"
    elif collect:
        row_list.append(row)

df2 = pd.DataFrame.from_records(row_list, columns=df.columns)
print(df2)

Output:

      name     process status
0   name 2   process 2
1   name 3   process 3
2   name 4   process 4
3   name 5   process 5
4  name 11  process 11
5  name 12  process 12
6  name 13  process 13
7  name 14  process 14

If want to drop the "status" column then call this:

df2 = df2.drop('status', axis=1)