Pandas - Fill the blank column with some value-CodePudding

I am looking to create a new column 'isRunning' in the data frame by identifying the 'Task' column status. Also, I have a 'Session' column and if a Session only has START value in the Task and no END vaule (for example, Session 3 has only START val), so 'isRunning' should have TRUE flag untill the end of that session.

	Session	Task	isRunning
0	1	START	TRUE
1	1		TRUE
2	1		TRUE
3	1		TRUE
4	1	END	TRUE
5	1		FALSE
6	2		FALSE
7	2	START	TRUE
8	2		TRUE
9	2	END	TRUE
10	2		FALSE
11	2		FALSE
12	3		FALSE
13	3	START	TRUE
14	3		TRUE
15	3		TRUE
16	4		FALSE
17	4	START	TRUE
18	4		TRUE
19	4		TRUE
20	4	END	TRUE

Not sure how to move forward with adding an extra condition and identifying if the Task is running.

import pandas as pd
d = {'Session':[1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4],
         'Task':['START', '', '', '', 'END', '', '', 'START', '', 'END', '', '', '', 'START', '', '', '', 'START', '', '', 'END']}

df = pd.DataFrame(data=d)

CodePudding user response：

Assuming one start/end per group. You can use a double mask:

m1 = df['Task'].eq('START').groupby(df['Session]').cummax()

m2 = df.loc[::-1, 'Task'].eq('END').groupby(df['Session']).cummax()

df['IsRunning'] = m1&m2

If several start/end per session are possible:

df['IsRunning'] = (df['Task'].map({'START': True, 'END': False})
                   .groupby(df['Session']).ffill()
                   .fillna(False)
                ) | df['Task'].eq('END')

Output:

    Session   Task  IsRunning
0         1  START       True
1         1              True
2         1              True
3         1              True
4         1    END       True
5         1             False
6         2             False
7         2  START       True
8         2              True
9         2    END       True
10        2             False
11        2             False
12        3             False
13        3  START       True
14        3              True
15        3              True
16        4             False
17        4  START       True
18        4              True
19        4              True
20        4    END       True