Python 熊猫聚集-如何根据事件日期查找最近的事件
我有一个很长的事件列表，这些事件都在熊猫数据框中，每个事件都有开始和结束日期。如何使用Python 熊猫聚集-如何根据事件日期查找最近的事件,python,pandas,dataframe,datetime,date-range,Python,Pandas,Dataframe,Datetime,Date Range,我有一个很长的事件列表，这些事件都在熊猫数据框中，每个事件都有开始和结束日期。如何使用pandas.groupby（）在每个城市和场馆组合中找到“最近”的活动 最近的事件可能是过去的事件，如果没有新的即将发生的事件。i、 e本例中数据帧中的最新事件，它恰好发生在过去。 如果有多个即将发生的事件，则未来最接近的事件将被视为最近的事件 我尝试了下面的groupby.agg（“max”），但这将始终提供未来最远的事件： dfp.groupby(['CITY', 'VENUE'], as_index=F
pandas.groupby（）groupby.agg（“max”）dfp.groupby(['CITY', 'VENUE'], as_index=False).agg({"EVENT_START" : "max", "EVENT_END": "max"})
EVENT_START,EVENT_END,Event Description,City,Venue
2/5/2016,3/12/2016,event 1,Chicago,Art Institute of Chicago
11/2/2014,12/2/2014,event 2,Los Angelos,Party Haus
1/25/2018,1/31/2018,event 3,Long Beach,Precious Lamb
8/26/2018,8/31/2018,event 4,West Columbia,New Brookland Tavern
11/20/2017,12/17/2017,event 5,Paris,Orsay Museum
6/26/2018,7/9/2018,event 6,Lahaina,Bamboo Fresh
4/3/2010,5/2/2010,event 7,Mitchell,The Corn Palace
9/21/2015,10/18/2015,event 8,San Diego,San Diego Zoo
1/4/2014,1/15/2014,event 9,Portland,Doug Fir Lounge
9/21/2019,9/26/2019,event 10,St. Louis,Krispy Kreme
3/15/2015,2/13/2018,event 11,Corvallis,The Beanery
9/23/2005,10/2/2005,event 12,San Jose,Winchester Mystery House
12/11/2019,12/14/2019,event 13,Chicago,Art Institute of Chicago
6/1/2013,6/26/2013,event 14,Los Angelos,Party Haus
7/10/2020,9/4/2020,event 15,Long Beach,Precious Lamb
10/18/2020,11/26/2020,event 16,West Columbia,New Brookland Tavern
5/14/2004,5/16/2004,event 17,Paris,Orsay Museum
11/16/2020,11/20/2020,event 18,Lahaina,Bamboo Fresh
7/19/2020,10/22/2020,event 19,Mitchell,The Corn Palace
11/1/2017,11/30/2017,event 20,San Diego,San Diego Zoo
7/31/2015,8/1/2015,event 21,Portland,Doug Fir Lounge
10/12/2012,10/20/2012,event 22,St. Louis,Krispy Kreme
2/28/2003,3/13/2003,event 23,Corvallis,The Beanery
9/16/2019,9/20/2019,event 24,San Jose,Winchester Mystery House
3/1/2022,4/1/2022,event 25,Chicago,Art Institute of Chicago
2/19/2009,2/25/2009,event 26,Los Angelos,Party Haus
4/16/2015,5/8/2015,event 27,Long Beach,Precious Lamb
9/7/2016,9/11/2016,event 28,West Columbia,New Brookland Tavern
8/4/2001,8/26/2001,event 29,Paris,Orsay Museum
4/27/2017,6/11/2017,event 30,Lahaina,Bamboo Fresh
5/21/2011,6/19/2011,event 31,Mitchell,The Corn Palace
6/3/2020,8/10/2020,event 32,San Diego,San Diego Zoo
10/29/2012,11/15/2012,event 33,Portland,Doug Fir Lounge
9/1/2027,10/15/2027,event 34,St. Louis,Krispy Kreme
6/23/2017,6/25/2017,event 35,Corvallis,The Beanery
4/25/2007,5/26/2007,event 36,San Jose,Winchester Mystery House
5/30/2003,7/1/2003,event 37,Chicago,Art Institute of Chicago
3/14/2008,4/12/2008,event 38,Los Angelos,Party Haus
5/29/2017,7/27/2017,event 39,Long Beach,Precious Lamb
1/31/2015,3/7/2015,event 40,West Columbia,New Brookland Tavern
4/1/2017,4/21/2017,event 41,Paris,Orsay Museum
12/29/2003,1/31/2004,event 42,Lahaina,Bamboo Fresh
7/3/2021,7/17/2021,event 43,Mitchell,The Corn Palace
9/1/2004,4/30/2005,event 44,San Diego,San Diego Zoo
10/14/2006,10/27/2006,event 45,Portland,Doug Fir Lounge
7/18/2017,7/19/2017,event 46,St. Louis,Krispy Kreme
6/1/2006,6/1/2006,event 47,Corvallis,The Beanery
10/1/2012,11/4/2012,event 48,San Jose,Winchester Mystery House
9/5/2011,9/19/2011,event 49,Chicago,Art Institute of Chicago
5/28/2020,6/2/2020,event 50,Los Angelos,Party Haus
3/1/2023,4/1/2023,event 51,Chicago,Art Institute of Chicago
3/1/2022 4/1/2022 event 25 Chicago Art Institute of Chicago
5/28/2020 6/2/2020 event 50 Los Angelos Party Haus
7/10/2020 9/4/2020 event 15 Long Beach Precious Lamb
10/18/2020 11/26/2020 event 16 West Columbia New Brookland Tavern
11/20/2017 12/17/2017 event 5 Paris Orsay Museum
11/16/2020 11/20/2020 event 18 Lahaina Bamboo Fresh
7/3/2021 7/17/2021 event 43 Mitchell The Corn Palace
6/3/2020 8/10/2020 event 32 San Diego San Diego Zoo
7/31/2015 8/1/2015 event 21 Portland Doug Fir Lounge
9/1/2027 10/15/2027 event 34 St. Louis Krispy Kreme
6/23/2017 6/25/2017 event 35 Corvallis The Beanery
9/16/2019 9/20/2019 event 24 San Jose Winchester Mystery House
EVENT_START EVENT_END Event Description City Venue
24 2022-03-01 2022-04-01 event 25 Chicago Art Institute of Chicago
34 2017-06-23 2017-06-25 event 35 Corvallis The Beanery
17 2020-11-16 2020-11-20 event 18 Lahaina Bamboo Fresh
14 2020-07-10 2020-09-04 event 15 Long Beach Precious Lamb
49 2020-05-28 2020-06-02 event 50 Los Angelos Party Haus
42 2021-07-03 2021-07-17 event 43 Mitchell The Corn Palace
4 2017-11-20 2017-12-17 event 5 Paris Orsay Museum
20 2015-07-31 2015-08-01 event 21 Portland Doug Fir Lounge
31 2020-06-03 2020-08-10 event 32 San Diego San Diego Zoo
23 2019-09-16 2019-09-20 event 24 San Jose Winchester Mystery House
33 2027-09-01 2027-10-15 event 34 St. Louis Krispy Kreme
15 2020-10-18 2020-11-26 event 16 West Columbia New Brookland Tavern
EVENT_START EVENT_END Event Description City Venue diff
33 2027-09-01 2027-10-15 event 34 St. Louis Krispy Kreme 2422 days 02:40:47.022263
50 2023-03-01 2023-04-01 event 51 Chicago Art Institute of Chicago 777 days 02:40:47.022263
42 2021-07-03 2021-07-17 event 43 Mitchell The Corn Palace 171 days 02:40:47.022263
15 2020-10-18 2020-11-26 event 16 West Columbia New Brookland Tavern -48 days +02:40:47.022263
17 2020-11-16 2020-11-20 event 18 Lahaina Bamboo Fresh -54 days +02:40:47.022263
14 2020-07-10 2020-09-04 event 15 Long Beach Precious Lamb -131 days +02:40:47.022263
31 2020-06-03 2020-08-10 event 32 San Diego San Diego Zoo -156 days +02:40:47.022263
49 2020-05-28 2020-06-02 event 50 Los Angelos Party Haus -225 days +02:40:47.022263
23 2019-09-16 2019-09-20 event 24 San Jose Winchester Mystery House -481 days +02:40:47.022263
10 2015-03-15 2018-02-13 event 11 Corvallis The Beanery -1065 days +02:40:47.022263
4 2017-11-20 2017-12-17 event 5 Paris Orsay Museum -1123 days +02:40:47.022263
20 2015-07-31 2015-08-01 event 21 Portland Doug Fir Lounge -1992 days +02:40:47.022263
df['diff'] = np.where(df['EVENT_START'] - pd.Timestamp('now') > pd.Timedelta(days=0),
df['EVENT_START'] - pd.Timestamp('now'),
df['EVENT_END'] - pd.Timestamp('now'))
df.loc[df.groupby(['City', 'Venue'])['diff'].idxmax()]
排序\u值删除重复项df['diff'] = np.where(df['EVENT_START'] - pd.Timestamp('now') > pd.Timedelta(days=0),
df['EVENT_START'] - pd.Timestamp('now'),
df['EVENT_END'] - pd.Timestamp('now'))
df.sort_values('diff', ascending=False).drop_duplicates(['City', 'Venue'])
EVENT_START EVENT_END Event Description City Venue
24 2022-03-01 2022-04-01 event 25 Chicago Art Institute of Chicago
34 2017-06-23 2017-06-25 event 35 Corvallis The Beanery
17 2020-11-16 2020-11-20 event 18 Lahaina Bamboo Fresh
14 2020-07-10 2020-09-04 event 15 Long Beach Precious Lamb
49 2020-05-28 2020-06-02 event 50 Los Angelos Party Haus
42 2021-07-03 2021-07-17 event 43 Mitchell The Corn Palace
4 2017-11-20 2017-12-17 event 5 Paris Orsay Museum
20 2015-07-31 2015-08-01 event 21 Portland Doug Fir Lounge
31 2020-06-03 2020-08-10 event 32 San Diego San Diego Zoo
23 2019-09-16 2019-09-20 event 24 San Jose Winchester Mystery House
33 2027-09-01 2027-10-15 event 34 St. Louis Krispy Kreme
15 2020-10-18 2020-11-26 event 16 West Columbia New Brookland Tavern
EVENT_START EVENT_END Event Description City Venue diff
33 2027-09-01 2027-10-15 event 34 St. Louis Krispy Kreme 2422 days 02:40:47.022263
50 2023-03-01 2023-04-01 event 51 Chicago Art Institute of Chicago 777 days 02:40:47.022263
42 2021-07-03 2021-07-17 event 43 Mitchell The Corn Palace 171 days 02:40:47.022263
15 2020-10-18 2020-11-26 event 16 West Columbia New Brookland Tavern -48 days +02:40:47.022263
17 2020-11-16 2020-11-20 event 18 Lahaina Bamboo Fresh -54 days +02:40:47.022263
14 2020-07-10 2020-09-04 event 15 Long Beach Precious Lamb -131 days +02:40:47.022263
31 2020-06-03 2020-08-10 event 32 San Diego San Diego Zoo -156 days +02:40:47.022263
49 2020-05-28 2020-06-02 event 50 Los Angelos Party Haus -225 days +02:40:47.022263
23 2019-09-16 2019-09-20 event 24 San Jose Winchester Mystery House -481 days +02:40:47.022263
10 2015-03-15 2018-02-13 event 11 Corvallis The Beanery -1065 days +02:40:47.022263
4 2017-11-20 2017-12-17 event 5 Paris Orsay Museum -1123 days +02:40:47.022263
20 2015-07-31 2015-08-01 event 21 Portland Doug Fir Lounge -1992 days +02:40:47.022263
EVENT_START EVENT_END Event Description City Venue
24 2022-03-01 2022-04-01 event 25 Chicago Art Institute of Chicago
34 2017-06-23 2017-06-25 event 35 Corvallis The Beanery
17 2020-11-16 2020-11-20 event 18 Lahaina Bamboo Fresh
14 2020-07-10 2020-09-04 event 15 Long Beach Precious Lamb
49 2020-05-28 2020-06-02 event 50 Los Angelos Party Haus
42 2021-07-03 2021-07-17 event 43 Mitchell The Corn Palace
4 2017-11-20 2017-12-17 event 5 Paris Orsay Museum
20 2015-07-31 2015-08-01 event 21 Portland Doug Fir Lounge
31 2020-06-03 2020-08-10 event 32 San Diego San Diego Zoo
23 2019-09-16 2019-09-20 event 24 San Jose Winchester Mystery House
33 2027-09-01 2027-10-15 event 34 St. Louis Krispy Kreme
15 2020-10-18 2020-11-26 event 16 West Columbia New Brookland Tavern
EVENT_START EVENT_END Event Description City Venue diff
33 2027-09-01 2027-10-15 event 34 St. Louis Krispy Kreme 2422 days 02:40:47.022263
50 2023-03-01 2023-04-01 event 51 Chicago Art Institute of Chicago 777 days 02:40:47.022263
42 2021-07-03 2021-07-17 event 43 Mitchell The Corn Palace 171 days 02:40:47.022263
15 2020-10-18 2020-11-26 event 16 West Columbia New Brookland Tavern -48 days +02:40:47.022263
17 2020-11-16 2020-11-20 event 18 Lahaina Bamboo Fresh -54 days +02:40:47.022263
14 2020-07-10 2020-09-04 event 15 Long Beach Precious Lamb -131 days +02:40:47.022263
31 2020-06-03 2020-08-10 event 32 San Diego San Diego Zoo -156 days +02:40:47.022263
49 2020-05-28 2020-06-02 event 50 Los Angelos Party Haus -225 days +02:40:47.022263
23 2019-09-16 2019-09-20 event 24 San Jose Winchester Mystery House -481 days +02:40:47.022263
10 2015-03-15 2018-02-13 event 11 Corvallis The Beanery -1065 days +02:40:47.022263
4 2017-11-20 2017-12-17 event 5 Paris Orsay Museum -1123 days +02:40:47.022263
20 2015-07-31 2015-08-01 event 21 Portland Doug Fir Lounge -1992 days +02:40:47.022263
df['diff'] = np.where(df['EVENT_START'] - pd.Timestamp('now') > pd.Timedelta(days=0),
df['EVENT_START'] - pd.Timestamp('now'),
df['EVENT_END'] - pd.Timestamp('now'))
df.loc[df.groupby(['City', 'Venue'])['diff'].idxmax()]
排序\u值删除重复项df['diff'] = np.where(df['EVENT_START'] - pd.Timestamp('now') > pd.Timedelta(days=0),
df['EVENT_START'] - pd.Timestamp('now'),
df['EVENT_END'] - pd.Timestamp('now'))
df.sort_values('diff', ascending=False).drop_duplicates(['City', 'Venue'])
EVENT_START EVENT_END Event Description City Venue
24 2022-03-01 2022-04-01 event 25 Chicago Art Institute of Chicago
34 2017-06-23 2017-06-25 event 35 Corvallis The Beanery
17 2020-11-16 2020-11-20 event 18 Lahaina Bamboo Fresh
14 2020-07-10 2020-09-04 event 15 Long Beach Precious Lamb
49 2020-05-28 2020-06-02 event 50 Los Angelos Party Haus
42 2021-07-03 2021-07-17 event 43 Mitchell The Corn Palace
4 2017-11-20 2017-12-17 event 5 Paris Orsay Museum
20 2015-07-31 2015-08-01 event 21 Portland Doug Fir Lounge
31 2020-06-03 2020-08-10 event 32 San Diego San Diego Zoo
23 2019-09-16 2019-09-20 event 24 San Jose Winchester Mystery House
33 2027-09-01 2027-10-15 event 34 St. Louis Krispy Kreme
15 2020-10-18 2020-11-26 event 16 West Columbia New Brookland Tavern
EVENT_START EVENT_END Event Description City Venue diff
33 2027-09-01 2027-10-15 event 34 St. Louis Krispy Kreme 2422 days 02:40:47.022263
50 2023-03-01 2023-04-01 event 51 Chicago Art Institute of Chicago 777 days 02:40:47.022263
42 2021-07-03 2021-07-17 event 43 Mitchell The Corn Palace 171 days 02:40:47.022263
15 2020-10-18 2020-11-26 event 16 West Columbia New Brookland Tavern -48 days +02:40:47.022263
17 2020-11-16 2020-11-20 event 18 Lahaina Bamboo Fresh -54 days +02:40:47.022263
14 2020-07-10 2020-09-04 event 15 Long Beach Precious Lamb -131 days +02:40:47.022263
31 2020-06-03 2020-08-10 event 32 San Diego San Diego Zoo -156 days +02:40:47.022263
49 2020-05-28 2020-06-02 event 50 Los Angelos Party Haus -225 days +02:40:47.022263
23 2019-09-16 2019-09-20 event 24 San Jose Winchester Mystery House -481 days +02:40:47.022263
10 2015-03-15 2018-02-13 event 11 Corvallis The Beanery -1065 days +02:40:47.022263
4 2017-11-20 2017-12-17 event 5 Paris Orsay Museum -1123 days +02:40:47.022263
20 2015-07-31 2015-08-01 event 21 Portland Doug Fir Lounge -1992 days +02:40:47.022263
#拆分过去事件和未来事件
cond=df['EVENT_START']>datetime.now（）
df_future=df[cond]
df_pass=df[~cond]
#保持最近的距离
dfn_furture=df_furture.sort_值（['City'，'vention'，'EVENT_START']））\
.drop_duplicates（['City'，'vention']，keep='first'）
#为每个城市和过去的事件合并一个最近的未来事件
dfn=pd.concat（[dfn未来，df过去]）
df_result=dfn.sort_值（['City'，'vention'，'EVENT_START']））\
.drop_duplicates（['City'，'vention']，keep='last'）。sort_index（）
EVENT_START EVENT_END Event Description City \
4 2017-11-20 2017-12-17 event 5 Paris
14 2020-07-10 2020-09-04 event 15 Long Beach
15 2020-10-18 2020-11-26 event 16 West Columbia
17 2020-11-16 2020-11-20 event 18 Lahaina
20 2015-07-31 2015-08-01 event 21 Portland
23 2019-09-16 2019-09-20 event 24 San Jose
24 2022-03-01 2022-04-01 event 25 Chicago
31 2020-06-03 2020-08-10 event 32 San Diego
33 2027-09-01 2027-10-15 event 34 St. Louis
34 2017-06-23 2017-06-25 event 35 Corvallis
42 2021-07-03 2021-07-17 event 43 Mitchell
49 2020-05-28 2020-06-02 event 50 Los Angelos
Venue
4 Orsay Museum
14 Precious Lamb
15 New Brookland Tavern
17 Bamboo Fresh
20 Doug Fir Lounge
23 Winchester Mystery House
24 Art Institute of Chicago
31 San Diego Zoo
33 Krispy Kreme
34 The Beanery
42 The Corn Palace
49 Party Haus
#拆分过去事件和未来事件
cond=df['EVENT_START']>datetime.now（）
df_future=df[cond]
df_pass=df[~cond]
#保持最近的距离
dfn_furture=df_furture.sort_值（['City'，'vention'，'EVENT_START']））\
.drop_duplicates（['City'，'vention']，keep='first'）
#为每个城市和过去的事件合并一个最近的未来事件
dfn=pd.concat（[dfn未来，df过去]）
df_result=dfn.sort_值（['City'，'vention'，'EVENT_START']））\
.drop_duplicates（['City'，'vention']，keep='last'）。sort_index（）
EVENT_START EVENT_END Event Description City \
4 2017-11-20 2017-12-17 event 5 Paris
14 2020-07-10 2020-09-04 event 15 Long Beach
15 2020-10-18 2020-11-26 event 16 West Columbia
17 2020-11-16 2020-11-20 event 18 Lahaina
20 2015-07-31 2015-08-01 event 21 Portland
23 2019-09-16 2019-09-20 event 24 San Jose
24 2022-03-01 2022-04-01 event 25 Chicago
31 2020-06-03 2020-08-10 event 32 San Diego
33 2027-09-01 2027-10-15 event 34 St. Louis
34 2017-06-23 2017-06-25 event 35 Corvallis
42 2021-07-03 2021-07-17 event 43 Mitchell
49 2020-05-28 2020-06-02 event 50 Los Angelos
Venue
4 Orsay Museum
14 Precious Lamb
15 New Brookland Tavern
17 Bamboo Fresh
20 Doug Fir Lounge
23 Winchester Mystery House
24 Art Institute of Chicago
31 San Diego Zoo
33 Krispy Kreme
34 The Beanery
42 The Corn Palace
49 Party Haus
你能发布完整的数据集，而不仅仅是前两列吗？数据如下：对不起，看不到任何数据。请用数据编辑您的原始帖子同样，添加所需的输出将更有帮助。添加更多事件扫描您发布的是完整的数据集，而不仅仅是前两列？数据如下：抱歉，没有看到任何数据。请用数据编辑您的原始帖子同样，添加所需的输出将更有帮助。添加更多事件添加一个条目，如下，到数据集：3/1/2023,4/1/2023，事件51，芝加哥艺术学院Chicago@Ganga第二个语句使用新的更新输出。谢谢您的回答！如果我只想参加最远的活动，是的，但我要找的是“最近的”，例如芝加哥艺术学院的多个活动Chicago@Ganga啊。。。见。在数据集中添加如下条目：3/1/2023,4/1/2023，事件51，芝加哥艺术学院Chicago@Ganga第二个语句使用新的更新输出。谢谢您的回答！如果我只想参加最远的活动，是的，但我要找的是“最近的”，例如芝加哥艺术学院的多个活动Chicago@Ganga啊。。。我明白了。非常感谢您的回复，问题很快就解决了！令人惊叹的！！非常感谢您的回复，问题很快就解决了！令人惊叹的！！