My problems is the same as, How to group by continuous records in SQL, only I need a solution in Pandas.
Given a df like
ID Colour
------------
1 Red
2 Red
3 Red
4 Red
5 Red
6 Green
7 Green
8 Green
9 Green
10 Red
11 Red
12 Red
13 Red
14 Green
15 Green
16 Green
17 Blue
18 Blue
19 Red
20 Blue
I want it grouped into
color minId
------------
Red 1
Green 6
Red 10
Green 14
Blue 17
Red 19
Blue 20
It is okay to change the name of the colors (e.g., Green1)
The solution should generalize into other aggregations other than just min
>Solution :
You can grouping by consecutive values by helper Series created by compared shifted values and cumsum and then aggregate first and min:
g = df['Colour'].ne(df['Colour'].shift()).cumsum()
df = df.groupby(g).agg(color=('Colour','first'), minId=('ID','min')).reset_index(drop=True)
print (df)
color minId
0 Red 1
1 Green 6
2 Red 10
3 Green 14
4 Blue 17
5 Red 19
6 Blue 20