Cell by cell addition of floats in two pandas dataframes by matching the row values of the first col-CodePudding

I have two Pandas dataframes containing floats with row values of the first column which differ slightly. For example,

df_1 = timestamps column_1 column_2 ... column_n
          row_1         
          row_2
          row_3
          row_4 
          row_5
          row_6
             .
             .
             .
             n

df_2 = timestamps column_1 column_2 ... column_n
          row_1         
          row_2
          row_3
          row_4 
          row_5
          row_7
             .
             .
             .
             n

As you can see, row_6 in df_1 is swapped with row_7 in df_2. Could someone please help me in adding the two dataframes cell by cell after aligned by identical row values of the first column. I want to do something like df_2 = df_1 df_2 inside a for loop with a counter n. Finally, I want to do df_2 = df_2/n to give some more context.

CodePudding user response：

if I understand correctly,

df_1
###
  timestamp  column_1  column_2
0        t1         1         6
1        t2         2         7
2        t3         3         8
3        t4         4         9
4        t5         5        10

df_2
###
  timestamp  column_1  column_2
0        t1        11        16
1        t2        12        17
2        t5        13        18
3        t6        14        19
4        t7        15        20

Use iloc to deal with calculation,

df_2.iloc[:, 1:] = df_1.iloc[:, 1:]   df_2.iloc[:, 1:]
df_2
###
  timestamp  column_1  column_2
0        t1        12        22
1        t2        14        24
2        t5        16        26
3        t6        18        28
4        t7        20        30