Summing across rows of Pandas Dataframe

Question

I have a DataFrame of records that looks something like this:

stocks = pd.Series([\'A\', \'A\', \'B\', \'C\', \'C\'], name = \'stock\')
positions = pd.Series([ 1         


        

Answer 1

Step 1. Use [['positions']] instead of ['positions']:

In [30]: df2 = df.groupby(['stock','same1','same2'])[['positions']].sum()

In [31]: df2 
Out[31]: 

                   positions
stock same1 same2               
A     AA    AAA          300 
B     BB    BBB          300 
C     CC    CCC          900 

Step 2. And then use reset_index to move the index back to the column

In [34]: df2.reset_index()
Out[34]: 
  stock same1 same2  positions
0     A    AA   AAA        300 
1     B    BB   BBB        300 
2     C    CC   CCC        900

EDIT

Seems my method is not so good.

Thanks to @Andy and @unutbu , you can achieve your goal by more elegant ways:

method 1:

df.groupby(['stock', 'same1', 'same2'])['positions'].sum().reset_index()

method 2:

df.groupby(['stock', 'same1', 'same2'], as_index=False)['positions'].sum()