Merge DataFrames with Matching Values From Two Different Columns - Pandas

Question

I have two different DataFrames that I want to merge with date and hours columns. I saw some threads that are there, but I could not find the solution for my issue. I also read this document and tried different combinations, however, did not work well.

Example of my two different DataFrames,

DF1

        date    hours        var1            var2 
0   2013-07-10  00:00:00    150.322617  52.225920   
1   2013-07-10  01:00:00    155.250917  53.365296   
2   2013-07-10  02:00:00    124.918667  51.158249   
3   2013-07-10  03:00:00    143.839217  53.138251
 .....  
9   2013-09-10  09:00:00    148.135818  86.676341
10  2013-09-10  10:00:00    147.833517  53.658016   
11  2013-09-10  12:00:00    149.580233  69.745368   
12  2013-09-10  13:00:00    163.715317  14.524894   
13  2013-09-10  14:00:00    168.856650  10.762779   

DF2

       date      hours      myvar1        myvar2 
0   2013-07-10  09:00:00    1.617         98.56 
1   2013-07-10  10:00:00    2.917         23.60 
2   2013-07-10  12:00:00    19.667        36.15 
3   2013-07-10  13:00:00    14.217        45.16
 .....  
20 2013-09-10   20:00:00    1.517         53.56 
21 2013-09-10   21:00:00    5.233         69.47
22 2013-09-10   22:00:00    13.717        14.25
23 2013-09-10   23:00:00    18.850        10.69 

As you can see in both DataFrames, DF2 starts with 09:00:00 and I want to join with DF1 09:00:00, which is basically the matchind dates and times. So far, I tried many different combination using previous threads and the documentation mentioned above. An example,

merged_df = DF2.merge(DF1, how = 'left', on = ['date', 'hours'])

This was introduces NAN values for right right DataFrame. I know, I do not have to use both date and hours columns, however, still getting the same result. I tried R quick like this, which works perfectly fine.

merged_df  <- left_join(DF1, DF2, by = 'date')

Is there anyway in pandas to merge DatFrames just with matching values without getting NaN values?

Solution

Use how='inner' in pd.merge:

merged_df = DF2.merge(DF1, how = 'inner', on = ['date', 'hours'])

This will perform and "inner-join" thereby omitting rows in each dataframe that do not match. Hence, no NaN in either the right or left part of merged dataframe.