Drop duplicates, keep most recent date in a Pandas dataframe

Question

I have a Pandas dataframe containing two columns: a datetime column, and a column of integers representing station IDs. I need a new dataframe with the following modifications:

For each set of duplicate STATION_ID values, keep the row with the most recent entry for DATE_CHANGED. If the duplicate entries for the STATION_ID all contain the same DATE_CHANGED then drop the duplicates and retain a single row for the STATION_ID. If there are no duplicates for the STATION_ID value, simply retain the row.

Dataframe (sorted by STATION_ID):

              DATE_CHANGED  STATION_ID
0      2006-06-07 06:00:00           1
1      2000-09-26 06:00:00           1
2      2000-09-26 06:00:00           1
3      2000-09-26 06:00:00           1
4      2001-06-06 06:00:00           2
5      2005-07-29 06:00:00           2
6      2005-07-29 06:00:00           2
7      2001-06-06 06:00:00           2
8      2001-06-08 06:00:00           4
9      2003-11-25 07:00:00           4
10     2001-06-12 06:00:00           7
11     2001-06-04 06:00:00           8
12     2017-04-03 18:36:16           8
13     2017-04-03 18:36:16           8
14     2017-04-03 18:36:16           8
15     2001-06-04 06:00:00           8
16     2001-06-08 06:00:00          10
17     2001-06-08 06:00:00          10
18     2001-06-08 06:00:00          11
19     2001-06-08 06:00:00          11
20     2001-06-08 06:00:00          12
21     2001-06-08 06:00:00          12
22     2001-06-08 06:00:00          13
23     2001-06-08 06:00:00          13
24     2001-06-08 06:00:00          14
25     2001-06-08 06:00:00          14
26     2001-06-08 06:00:00          15
27     2017-08-07 17:48:25          15
28     2001-06-08 06:00:00          15
29     2017-08-07 17:48:25          15
...                    ...         ...
157066 2018-08-06 14:11:28       71655
157067 2018-08-06 14:11:28       71656
157068 2018-08-06 14:11:28       71656
157069 2018-09-11 21:45:05       71664
157070 2018-09-11 21:45:05       71664
157071 2018-09-11 21:45:05       71664
157072 2018-09-11 21:41:04       71664
157073 2018-08-09 15:22:07       71720
157074 2018-08-09 15:22:07       71720
157075 2018-08-09 15:22:07       71720
157076 2018-08-23 12:43:12       71899
157077 2018-08-23 12:43:12       71899
157078 2018-08-23 12:43:12       71899
157079 2018-09-08 20:21:43       71969
157080 2018-09-08 20:21:43       71969
157081 2018-09-08 20:21:43       71969
157082 2018-09-08 20:21:43       71984
157083 2018-09-08 20:21:43       71984
157084 2018-09-08 20:21:43       71984
157085 2018-09-05 18:46:18       71985
157086 2018-09-05 18:46:18       71985
157087 2018-09-05 18:46:18       71985
157088 2018-09-08 20:21:44       71990
157089 2018-09-08 20:21:44       71990
157090 2018-09-08 20:21:44       71990
157091 2018-09-08 20:21:43       72003
157092 2018-09-08 20:21:43       72003
157093 2018-09-08 20:21:43       72003
157094 2018-09-10 17:06:18       72024
157095 2018-09-10 17:15:05       72024

[157096 rows x 2 columns]

DATE_CHANGED is dtype: datetime64[ns]

STATION_ID is dtype: int64

pandas==0.23.4

python==2.7.15

Asked By: pjw

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Source

Answer 1

Try:

df.sort_values('DATE_CHANGED').drop_duplicates('STATION_ID',keep='last')

Answered By: sacuL

Answer 2

If DATE_CHANGED is dtype datetime64 (as it is in the OP), groupby.max can be used as well because the most recent date has the highest value.

df['DATE_CHANGED'] = pd.to_datetime(df['DATE_CHANGED'])
df.groupby('STATION_ID', as_index=False)['DATE_CHANGED'].max()

If the values are sorted by DATE_CHANGED, then groupby.tail may be used as well.

df = df.sort_values(by='DATE_CHANGED')
df.groupby('STATION_ID', as_index=False).tail(1)

Answered By: cottontail

Drop duplicates, keep most recent date in a Pandas dataframe

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