如何在不重復行的情況下合並/連接兩個數據幀，並將具有相同參數的數據幀保留在熊貓的同一行中？

Question

我會加入/合並兩個數據框，如下所示：

ID1 = [2002070, 2002070, 2002740,2002740,2003010]
ID2 = [2002070, 200800, 200800,2002740,2002740]
Value1 = [4.5, 4.2, 3.7, 4.8, 4.4]
Value2 = [7.2, 6.4, 10, 2.3, 1.5]
date1 = ['2008-05-14', '2005-12-07','2008-10-27', '2009-04-20', '2012-03-01']
date2 = ['2005-12-07','2003-10-10', '2004-05-14', '2011-06-03', '2015-07-05']
date1=pd.to_datetime(date1)
date2=pd.to_datetime(date2)
df1=pd.DataFrame({'ID': ID1, 'Value1': Value1, 'Date1':date1}).sort_values('Date1')
df2=pd.DataFrame({'ID': ID2, 'Value2': Value2, 'Date2':date2}).sort_values('Date2')
ok = df1.merge(df2,on='ID', how='outer', sort=True)

我獲得了這個新的 df，其中一個 df 中缺少數據時會復制數據

          ID  Value1    Date1   Value2  Date2
    0   200800  NaN     NaT     6.4 2003-10-10
    1   200800  NaN     Na      10  2004-05-14
    2   2002070 4.2 2005-12-07  7.2 2005-12-07
    3   2002070 4.5 2008-05-14  7.2 2005-12-07
    4   2002740 3.7 2008-10-27  2.3 2011-06-03
    5   2002740 3.7 2008-10-27  1.5 2015-07-05
    6   2002740 4.8 2009-04-20  2.3 2011-06-03
    7   2002740 4.8 2009-04-20  1.5 2015-07-05
    8   2003010 4.4 2012-03-01  NaN    NaT

但我想獲得這個，只有具有相同 ID 和 Date 的數據位於同一行，其他數據替換為 NaN 或 NaT 並且不重復

         ID  Value Date1       Value2   Date2
    0   200800  NaN   NaT       6.4 2003-10-10
    1   200800  NaN   NaT       10  2004-05-14
    2   2002070 4.2 2005-12-07  7.2 2005-12-07   <---on the same row
    3   2002070 4.5 2008-05-14  NaN    NaT       <---not duplicated
    4   2002740 3.7 2008-10-27  NaN    NaT       
    5   2002740 NaN    NaT      2.3 2011-06-03
    6   2002740 NaN    NaT      1.5 2015-07-05
    7   2002740 4.8 2009-04-20  NaN    NaT
    8   2003010 4.4 2012-03-01  NaN    NaT

你知道怎么做嗎？

Answer 1

我相信你需要DataFrame.merge左右兩列：

ok = df1.merge(df2, left_on=['ID','Date1'],right_on=['ID','Date2'], how='outer', sort=True)
print (ok)
        ID  Value1      Date1  Value2      Date2
0   200800     NaN        NaT     6.4 2003-10-10
1   200800     NaN        NaT    10.0 2004-05-14
2  2002070     4.2 2005-12-07     7.2 2005-12-07
3  2002070     4.5 2008-05-14     NaN        NaT
4  2002740     3.7 2008-10-27     NaN        NaT
5  2002740     4.8 2009-04-20     NaN        NaT
6  2002740     NaN        NaT     2.3 2011-06-03
7  2002740     NaN        NaT     1.5 2015-07-05
8  2003010     4.4 2012-03-01     NaN        NaT