Pandas sum by groupby, but exclude certain columns

Question

What is the best way to do a groupby on a Pandas dataframe, but exclude some columns from that groupby? e.g. I have the following dataframe:

Code   Country      Item_Code   Item    Ele_Code    Unit    Y1961    Y1962   Y1963
2      Afghanistan  15          Wheat   5312        Ha      10       20      30
2      Afghanistan  25          Maize   5312        Ha      10       20      30
4      Angola       15          Wheat   7312        Ha      30       40      50
4      Angola       25          Maize   7312        Ha      30       40      50

I want to groupby the column Country and Item_Code and only compute the sum of the rows falling under the columns Y1961, Y1962 and Y1963. The resulting dataframe should look like this:

Code   Country      Item_Code   Item    Ele_Code    Unit    Y1961    Y1962   Y1963
2      Afghanistan  15          C3      5312        Ha      20       40       60
4      Angola       25          C4      7312        Ha      60       80      100

Right now I am doing this:

df.groupby('Country').sum()

However this adds up the values in the Item_Code column as well. Is there any way I can specify which columns to include in the sum() operation and which ones to exclude?

Answer 1

You can select the columns of a groupby:

In [11]: df.groupby(['Country', 'Item_Code'])[["Y1961", "Y1962", "Y1963"]].sum()
Out[11]:
                       Y1961  Y1962  Y1963
Country     Item_Code
Afghanistan 15            10     20     30
            25            10     20     30
Angola      15            30     40     50
            25            30     40     50

Note that the list passed must be a subset of the columns otherwise you'll see a KeyError.