Sum values for rows with multiple matching columns

Question

I have a dataset with multiple entries for the same year, metric, and country. I want to create a dataframe where rows with matching values for year, metric, and country are summed into a single row.

There are potentially hundreds of instances of matching values across the first three columns so the solution must scale well.

Initial data:

Year    Metric  Country         Q1      Q2      Q3      Q4
2016    2.1.1   Australia   23  166 146 17
2016    2.1.1   Australia   0   24  26  0
2014    3.1.1   Haiti           0   0   0   0
2015    2.1.1   Mexico          442 37  16  58
2013    3.1.4   Jamaica             
2015    2.1.1   Mexico          165 140 209 309

I have tried several iterations of groupby and boolean indexing from here:

How do I sum values in a column that match a given condition using pandas?

Desired output:

Year    Metric  Country         Q1      Q2      Q3      Q4
2016    2.1.1   Australia   23  190 172 17
2014    3.1.1   Haiti           0   0   0   0
2015    2.1.1   Mexico          606 177 225 367
2013    3.1.4   Jamaica

`df.groupby(['Year', 'Metric','Country'],sort=False).sum()` – anky Jul 31 '19 at 15:31 — anky, Jul 31 '19 at 15:31
Where are your attempts ? – Alexandre B. Jul 31 '19 at 15:31 — Alexandre B., Jul 31 '19 at 15:31

Sum values for rows with multiple matching columns

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