Pandas: Creating aggregated column in DataFrame

Question

With the DataFrame below as an example,

In [83]: df = pd.DataFrame({'A':[1,1,2,2],'B':[1,2,1,2],'values':np.arange(10,30,5)}) df Out[83]:    A  B  values 0  1  1      10 1  1  2      15 2  2  1      20 3  2  2      25 

What would be a simple way to generate a new column containing some aggregation of the data over one of the columns?

For example, if I sum values over items in A

In [84]: df.groupby('A').sum()['values'] Out[84]: A 1    25 2    45 Name: values 

How can I get

   A  B  values  sum_values_A 0  1  1      10            25 1  1  2      15            25 2  2  1      20            45 3  2  2      25            45 

Answer 1

In [20]: df = pd.DataFrame({'A':[1,1,2,2],'B':[1,2,1,2],'values':np.arange(10,30,5)})  In [21]: df Out[21]:    A  B  values 0  1  1      10 1  1  2      15 2  2  1      20 3  2  2      25  In [22]: df['sum_values_A'] = df.groupby('A')['values'].transform(np.sum)  In [23]: df Out[23]:    A  B  values  sum_values_A 0  1  1      10            25 1  1  2      15            25 2  2  1      20            45 3  2  2      25            45 

Answer 2

I found a way using join:

In [101]: aggregated = df.groupby('A').sum()['values'] aggregated.name = 'sum_values_A' df.join(aggregated,on='A')  Out[101]:    A  B  values  sum_values_A 0  1  1      10            25 1  1  2      15            25 2  2  1      20            45 3  2  2      25            45 

Anyone has a simpler way to do it?