连接具有相同列的两个熊猫数据框并合并具有相同索引的行

Question

I have two dataframes df1 and df2 each with the same column names using timestamps as indicies. I want to concatenate the two dataframes whilst merging rows with the same index choosing the values stored in df2 as preference. This is poorly worded but see below. Eg

>>> df1= TimeStamp A_Output B_Output C_Output
          00:00:00  20       15       5
          00:00:06  20       NaN      3
          00:00:15  15       6      NaN
          00:00:20  20       NaN      5
          00:00:30  25       14      10


 >>> df2= TimeStamp A_Output B_Output C_Output
          00:00:00  15       5        8
          00:00:04  16       NaN      NaN
          00:00:06  17       NaN      NaN
          00:00:15  NaN      NaN      2
          00:00:18  19       NaN      NaN
          00:00:21  14       NaN      NaN
          00:00:26  32       NaN      5
          

 >>> df3= TimeStamp A_Output B_Output C_Output
          00:00:00  15       5        8
          00:00:04  16       NaN      NaN
          00:00:06  17       NaN      3
          00:00:15  15       6        2
          00:00:18  19       NaN      NaN
          00:00:21  14       NaN      NaN
          00:00:26  32       NaN      5
          00:00:30  25       14      10

df3 is what I would like to achieve. Here there is a timestamp for every index in df1 and df2 . For each common index, where db2 is not NaN, we take the values, otherwise we preserve those stored in df1 .

df1 >>> 00:00:15  15        6     NaN
df2 >>> 00:00:15  NaN      NaN     2
df3 >>> 00:00:15  15        6      2

df1 >>> 00:00:00  20        15     5
df2 >>> 00:00:00  15         5     8
df3 >>> 00:00:00  15         5     8

For clarification see the above examples. I really can't find a way to do this -- for reference each dataframe has around 90 columns and 100k+ rows.

Answer 1

Try combine first:

df3 = df2.combine_first(df1)

print(df3)

           A_Output  B_Output  C_Output
TimeStamp                              
00:00:00       15.0       5.0       8.0
00:00:04       16.0       NaN       NaN
00:00:06       17.0       NaN       3.0
00:00:15       15.0       6.0       2.0
00:00:18       19.0       NaN       NaN
00:00:20       20.0       NaN       5.0
00:00:21       14.0       NaN       NaN
00:00:26       32.0       NaN       5.0
00:00:30       25.0      14.0      10.0