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  val         


        

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

This is not so direct but I found it very intuitive (the use of map to create new columns from another column) and can be applied to many other cases:

gb = df.groupby('A').sum()['values']

def getvalue(x):
    return gb[x]

df['sum'] = df['A'].map(getvalue)
df

Answer 3

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?

Answer 4

In [15]: def sum_col(df, col, new_col):
   ....:     df[new_col] = df[col].sum()
   ....:     return df

In [16]: df.groupby("A").apply(sum_col, 'values', 'sum_values_A')
Out[16]: 
   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