删除重复项，保留最新日期，Pandas 数据框
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Drop duplicates, keep most recent date, Pandas dataframe
提问by PJW
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:
我有一个包含两列的 Pandas 数据框：一个日期时间列和一个代表站 ID 的整数列。我需要一个具有以下修改的新数据框：
For each set of duplicate STATION_IDvalues, keep the row with the most recent entry for DATE_CHANGED. If the duplicate entries for the STATION_IDall contain the same DATE_CHANGEDthen drop the duplicates and retain a single row for the STATION_ID. If there are no duplicates for the STATION_IDvalue, simply retain the row.
对于每组重复STATION_ID值，保留具有最新条目的行DATE_CHANGED。如果所有的重复条目STATION_ID包含相同的条目，DATE_CHANGED则删除重复项并为STATION_ID. 如果该STATION_ID值没有重复项，只需保留该行。
Dataframe (sorted by STATION_ID):
数据框（按 排序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_CHANGEDis dtype: datetime64[ns]
DATE_CHANGED是 dtype: datetime64[ns]
STATION_IDis dtype: int64
STATION_ID是 dtype: int64
pandas==0.23.4
Pandas==0.23.4
python==2.7.15
蟒蛇==2.7.15
回答by sacuL
Try:
尝试：
df.sort_values('DATE_CHANGED').drop_duplicates('STATION_ID',keep='last')
