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Given to Dataframes df_1 

Code | Jan | Feb | Mar a | 1 | 2 | 1 b | 3 | 4 | 3

and df_2

Code | Jan | Feb | Mar a | 1 | 1 | 2 c | 7 | 0 | 0

I would like to sum these to tables based on the row and colum. So my result dataframe shoul look like this:

Code | Jan | Feb | Mar a | 2 | 3 | 3 b | 3 | 4 | 3 c | 7 | 0 | 0

Is there an easy way to do this? I can to this using a lot of for loops and if statements but this is very slow for large datasets.

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Use concat and aggregate sum:

df = pd.concat([df_1, df_2]).groupby('Code', as_index=False).sum() print (df) Code Jan Feb Mar 0 a 2 3 3 1 b 3 4 3 2 c 7 0 0
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