How To Solve The Following Condition?

I have the following data type: id point 1 point 2 count Time 018 Paris London 01 2016-05-20 10:50:00 015 Paris

Solution 1:

I think you need numpy.roll in custom function:

#sort values first
df = df.sort_values(['id','Time'])
#create new columns
df['Start'] = df['point 1']
df['End'] = df['point 2']

def f(x):

    #roll values of point 2 and compare with point 1 per groups
    #all function for scalar True if all values are True
    m = (np.roll(x['point 2'].values, -1) != x['point 1']).all()
    if m:
        #assign first and last values
        x['Start'] = x['point 1'].iat[0]
        x['End'] = x['point 2'].iat[-1]
    return x

#apply custom function
df = df.groupby('id').apply(f)
print (df)
    id    point 1    point 2 count                Time      Start     End
7  002     Vienna     Prague    15 2016-05-19 02:45:00     Vienna  Munich
2  002     Prague     Munich    15 2016-05-19 17:55:00     Vienna  Munich
3  003  Frankfurt     London    01 2016-05-17 21:15:00  Frankfurt  Vienna
6  003     London  Barcelona    01 2016-05-18 06:45:00  Frankfurt  Vienna
5  003  Barcelona     Vienna    15 2016-05-19 03:20:00  Frankfurt  Vienna
1  015      Paris     London    01 2016-05-19 11:50:00      Paris  London
4  015     London      Paris    08 2016-05-21 13:50:00     London   Paris
0  018      Paris     London    01 2016-05-20 10:50:00      Paris  London