使用padas dataframe中的for，If语句计算持续时间

Question

I am a newbie to pandas and python - Your answers are highly appreciated I have three columns in a data frame where it has values as follows: print(df)

    |name        |date&time           |Id
    1 Start      2021-01-1 17:15:56    Bike1
    
    2 Pause      2021-01-1 17:17:57    Bike1

    3 Resume     2021-01-1 17:18:50    Bike1

    3 Progress   2021-01-1 17:19:58    Bike1

    5 Stop       2021-01-1 17:20:00    Bike1

    6 Start      2021-01-1 17:25:56    Bike2
    
    7 Pause      2021-01-1 17:27:57    Bike2

    8 Resume     2021-01-1 17:28:50    Bike2

    9 Progress   2021-01-1 17:29:58    Bike2

   10 Stop       2021-01-1 17:30:00    Bike2

I am trying to get the duration of the total time spent here by excluding the time between pause and resume.

 If(name=='Start')
a=date&time of that name(start) (storing it in a temp variable)
 If(name=='Pause')
b=date&time of that name(Pause) (storing it in a temp variable)
c=a+b;
 If(name=='resume')
d=date&time of that name(resume) (storing it in a temp variable)
 If(name=='stop')
e=date&time of that name(Stop) (storing it in a temp variable)
f=d+e;
For(time=0)
time=time+(c+f)

thought of this pseudo code - Can anyone help me with this?? Thanks in advance.

Answer 1

The beauty of pandas dataframes is that you can use operations by columns and rows without the need to iterate the rows. You can solve this problem in very few lines of code:

make sure date&time is type datetime:

df['date&time']=pd.to_datetime(df['date&time'])

then pivot so the times are columns for each bike:

df1=df.pivot(index='Id', columns='name' ,values='date&time')

finally, simple do the math:

df1['totaltime']=df1['Stop']-df1['Start']-(df1['Resume']-df1['Pause'])

output:

name    Pause                   Progress                 Resume                  Start                   Stop                  totaltime
Id                      
Bike1   2021-01-01 17:17:57     2021-01-01 17:19:58     2021-01-01 17:18:50     2021-01-01 17:15:56     2021-01-01 17:20:00     0 days 00:03:11
Bike2   2021-01-01 17:27:57     2021-01-01 17:29:58     2021-01-01 17:28:50     2021-01-01 17:25:56     2021-01-01 17:30:00     0 days 00:03:11