Merge two rows in the same Dataframe if their index is the same?

Question

I have created a large Dataframe by pulling data from an Azure database. The construction of the dataframe wasn't simple as I had to do it in parts, using the concat function to add new columns to the data set as they were pulled from the database.

This worked fine, however I am indexing by entry date and when concatenating I sometimes get two data rows with the same index. Is it possible for me to merge lines with the same index? I have searched online for solutions but I always come across examples trying to merge two separate dataframes instead of merging rows within the same dataframe.

In summary:

This

                      Col1  Col2
2015-10-27 22:22:31   1400  
2015-10-27 22:22:31         50.5

To this

                      Col1  Col2
2015-10-27 22:22:31   1400  50.5

I have tried using the groupby function on index but that just messed up. Most of the data columns disappeared and a few very large numbers were spat out.

Note:

The data is in this sort of format, except with many more columns and is generally quite sparse!

                        Col1    Col2    ...    Col_n-1 Col_n    
2015-10-27 21:15:60+0   1220        
2015-10-27 21:25:4+0    1420        
2015-10-27 21:28:8+0    1410        
2015-10-27 21:37:10+0           51.5    
2015-10-27 21:37:11+0   1500        
2015-10-27 21:46:14+0           51  
2015-10-27 21:46:15+0   1390        
2015-10-27 21:55:19+0   1370        
2015-10-27 22:04:24+0   1450        
2015-10-27 22:13:28+0   1350        
2015-10-27 22:22:31+0   1400        
2015-10-27 22:22:31+0           50.5
2015-10-27 22:25:33+0   1300        
2015-10-27 22:29:42+0                   ...    1900 
2015-10-27 22:29:42+0                                  63       
2015-10-27 22:34:36+0   1280        

Answer 1

Building up on @EdChum 's answer, it is also possible to use the min_count parameter of groupBy.sum to manage NaN values in different ways. Let's say we have an additional row to the example:

                      Col1  Col2
2015-10-27 22:22:31   1400   NaN
2015-10-27 22:22:31    NaN  50.5
2022-08-02 16:00:00   1600   NaN

then,

In [184]:
df.groupby('index').sum(min_count=1)

Out[184]:
                     Col1  Col2
index                          
2015-10-27 22:22:31  1400  50.5
2022-08-02 16:00:00  1600   NaN

Using min_count=0 will output 0 instead of NaN values.

Answer 2

You can groupby on your index and call sum:

In [184]:
df.groupby(level=0).sum()

Out[184]:
                     Col1  Col2
index                          
2015-10-27 22:22:31  1400  50.5