Pandas: get the first occurrence grouping by keys

Question

If I have following dataframe

| id | timestamp           | code | id2
| 10 | 2017-07-12 13:37:00 | 206  | a1
| 10 | 2017-07-12 13:40:00 | 206  | a1
| 10 | 2017-07-12 13:55:00 | 206  | a1
| 10 | 2017-07-12 19:00:00 | 206  | a2
| 11 | 2017-07-12 13:37:00 | 206  | a1
...

I need to group by id, id2 columns and get the first occurrence of timestamp value, e.g. for id=10, id2=a1, timestamp=2017-07-12 13:37:00.

I googled it and found some possible solutions, but cant figure out how to realize them properly. This probably should be something like:

df.groupby(["id", "id2"])["timestamp"].apply(lambda x: ....)

Answer 1

I think you need GroupBy.first:

df.groupby(["id", "id2"])["timestamp"].first()

Or drop_duplicates:

df.drop_duplicates(subset=['id','id2'])

For same output:

df1 = df.groupby(["id", "id2"], as_index=False)["timestamp"].first()
print (df1)
   id id2            timestamp
0  10  a1  2017-07-12 13:37:00
1  10  a2  2017-07-12 19:00:00
2  11  a1  2017-07-12 13:37:00

df1 = df.drop_duplicates(subset=['id','id2'])[['id','id2','timestamp']]
print (df1)
   id id2            timestamp
0  10  a1  2017-07-12 13:37:00
1  10  a2  2017-07-12 19:00:00
2  11  a1  2017-07-12 13:37:00