Python 按相同列值删除和操作熊猫中的日期

对于以下类型的表：

Name       Date        Score
James      02/2011      70
James      03/2011      72   
James      10/2011      60
James      12/2011      50
James      01/2012      40
James      02/2012      60
James      03/2012      75
James      11/2012      70
James      12/2012      70
James      01/2013      30
James      02/2013      20
James      04/2013      60
James      06/2013      80
Jacob      01/2011      70
Jacob      02/2011      70
Jacob      03/2011      60
Jacob      04/2011      80
Jacob      05/2011      70
Jacob      06/2011      70
Jacob      08/2011      70
Jacob      10/2011      60
Jacob      11/2011      60
Jacob      12/2011      70
Jacob      02/2012      80

我想按名称应用以下规则：

1） 如果连续日期之间的差异超过6个月，请删除与前一行关联的所有行

因此，对于James，在前3行：

James      02/2011      70
James      03/2011      72 (difference between 03/2011, 10/2011 is 7 months)
James      10/2011      60     

我们在这里删除前两行，得到的行是

James      10/2011      60
James      12/2011      50
James      01/2012      40
James      02/2012      60
James      03/2012      75
James      11/2012      70
James      12/2012      70
James      01/2013      30
James      02/2013      20
James      04/2013      60
James      06/2013      80

但是在这里

James      10/2011      60
James      12/2011      50
James      01/2012      40
James      02/2012      60
James      03/2012      75 (difference between 03/2012, 11/2012 is 8 months)
James      11/2012      70
James      12/2012      70
James      01/2013      30
James      02/2013      20
James      04/2013      60
James      06/2013      80

所以我们摆脱了

James      10/2011      60
James      12/2011      50
James      01/2012      40
James      02/2012      60
James      03/2012      75

而且只得到

James      11/2012      70
James      12/2012      70
James      01/2013      30
James      02/2013      20
James      04/2013      60
James      06/2013      80 

2） 对所有名称应用1）后，更改日期，使最后一个日期保持不变，连续日期之间的差值始终为1个月

因此，对于雅各布来说，我们最初有

Jacob      01/2011      70
Jacob      02/2011      70
Jacob      03/2011      60
Jacob      04/2011      80
Jacob      05/2011      70
Jacob      06/2011      70
Jacob      08/2011      70
Jacob      10/2011      60
Jacob      11/2011      60
Jacob      12/2011      70
Jacob      02/2012      80

但由此产生的行将是

Jacob      04/2011      70
Jacob      05/2011      70
Jacob      06/2011      60
Jacob      07/2011      80
Jacob      08/2011      70
Jacob      09/2011      70
Jacob      10/2011      70
Jacob      11/2011      60
Jacob      12/2011      60
Jacob      01/2012      70
Jacob      02/2012      80

因此，我想要的结果表是：

Name       Date        Score
James      01/2013      70
James      02/2013      70
James      03/2013      30
James      04/2013      20
James      05/2013      60
James      06/2013      80
Jacob      04/2011      70
Jacob      05/2011      70
Jacob      06/2011      60
Jacob      07/2011      80
Jacob      08/2011      70
Jacob      09/2011      70
Jacob      10/2011      70
Jacob      11/2011      60
Jacob      12/2011      60
Jacob      01/2012      70
Jacob      02/2012      80

任何帮助都将不胜感激。

我把台阶拆下来。 #转换为日期时间 df.Date=pd.to_datetime（df.Date，格式=“%m/%Y”）

---

只是玩弄了一些数字，但对一些人来说不起作用dates@user98235嗯，那么你的样本数据就不能重现你的问题了。它对我的样本数据有效，但对于新数据，存在一些问题。我想你的代码给了我足够的提示。我会尽我最大的努力来解决这个问题。谢谢

# create new key for groupby , then we juts need the last group
df['Newkey']=df.groupby('Name').apply(lambda x : (x.Date.diff()/np.timedelta64(6, 'M')).gt(1).cumsum()).sort_index(level=1).values

# filter out the only keep the last subgroup base one each name 
df1=df.loc[df.Newkey==df.groupby('Name').Newkey.transform('max')]

# create the new date by using the len of the group and the max date value from that group
df1['NewDate']=np.concatenate(df1.groupby('Name',sort=False).apply(lambda x : pd.date_range(end=x['Date'].max(),periods=len(x['Date']),freq='MS').strftime('%m/%Y')).values)
df1
Out[281]: 
     Name       Date  Score  Newkey  NewDate
7   James 2012-11-01     70       2  01/2013
8   James 2012-12-01     70       2  02/2013
9   James 2013-01-01     30       2  03/2013
10  James 2013-02-01     20       2  04/2013
11  James 2013-04-01     60       2  05/2013
12  James 2013-06-01     80       2  06/2013
13  Jacob 2011-01-01     70       0  04/2011
14  Jacob 2011-02-01     70       0  05/2011
15  Jacob 2011-03-01     60       0  06/2011
16  Jacob 2011-04-01     80       0  07/2011
17  Jacob 2011-05-01     70       0  08/2011
18  Jacob 2011-06-01     70       0  09/2011
19  Jacob 2011-08-01     70       0  10/2011
20  Jacob 2011-10-01     60       0  11/2011
21  Jacob 2011-11-01     60       0  12/2011
22  Jacob 2011-12-01     70       0  01/2012
23  Jacob 2012-02-01     80       0  02/2012