pandas 如何获得每类A的月末数据B

如何取到下面这个dataframe中，每一类Code对应的月末数据

df = pd.DataFrame({
    "Code":['a', 'a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'd'],
    "Time":['20180101', '20180130', '20180201', '20180228', '20180302', '20180401', '20180430', '20180502', '20180530', '20180629'],
    'X':np.arange(0,20,2),
    'Y':np.arange(1,20,2)})
df

Out[6]: 
  Code      Time   X   Y
0    a  20180101   0   1
1    a  20180130   2   3
2    a  20180201   4   5
3    a  20180228   6   7
4    b  20180302   8   9
5    b  20180401  10  11
6    b  20180430  12  13
7    c  20180502  14  15
8    c  20180530  16  17
9    d  20180629  18  19

即我们想要得到下面的这种效果

  Code    Time   X   Y
0    a  201801   2   3
1    a  201802   6   7
2    b  201803   8   9
3    b  201804  12  13
4    c  201805  16  17
5    d  201806  18  19

df["Time"] = pd.to_datetime(df["Time"]).dt.strftime("%Y%m").astype(np.int64)
df.groupby(["Code", "Time"]).agg('last')

Out[9]: 
              X   Y
Code Time          
a    201801   2   3
     201802   6   7
b    201803   8   9
     201804  12  13
c    201805  16  17
d    201806  18  19

df.groupby(["Code", "Time"]).agg('last').reset_index()

Out[11]: 
  Code    Time   X   Y
0    a  201801   2   3
1    a  201802   6   7
2    b  201803   8   9
3    b  201804  12  13
4    c  201805  16  17
5    d  201806  18  19

可以用

df = pd.DataFrame({
    "Time":['20180101', '20180130', '20180201', '20180228', '20180302', '20180401', '20180430', '20180502', '20180530', '20180629'],
    'X':np.arange(0,20,2),
    'Y':np.arange(1,20,2)})

df["Time"] = pd.to_datetime(df["Time"])
df.set_index("Time")

Out[16]: 
             X   Y
Time              
2018-01-01   0   1
2018-01-30   2   3
2018-02-01   4   5
2018-02-28   6   7
2018-03-02   8   9
2018-04-01  10  11
2018-04-30  12  13
2018-05-02  14  15
2018-05-30  16  17
2018-06-29  18  19

df.set_index("Time", inplace=True)
df.loc[df.groupby(df.index.to_period('M')).apply(lambda x: x.index.max())]

Out[19]: 
             X   Y
Time              
2018-01-30   2   3
2018-02-28   6   7
2018-03-02   8   9
2018-04-30  12  13
2018-05-30  16  17
2018-06-29  18  19

参考：
《get first and last values in a groupby》
《pandas获取月底最后一个交易日对应数据》