Summing values with similar row values

Question

I have a pandas data set that looks like this

city    difference 
NY       6
SF       8
LA       8
NY       9
SF       10

I want to sum up

Answer 1

I think you need transform:

df['total difference'] = df.groupby('city')['difference'].transform(sum) 
print (df)
  city  difference  total difference
0   NY           6                15
1   SF           8                18
2   LA           8                 8
3   NY           9                15
4   SF          10                18

And if need sort column also:

df['total difference'] = df.groupby('city')['difference'].transform('sum') 
df = df.sort_values('city')
print (df)
  city  difference  total difference
2   LA           8                 8
0   NY           6                15
3   NY           9                15
1   SF           8                18
4   SF          10                18

I was interested about differences in functions and timings are very similar:

#[10000000 rows x 2 columns]
np.random.seed(100)
df = pd.DataFrame(np.random.randint(1000, size=(10000000,2)), columns=['city','difference'])
#print (df)

In [293]: %timeit (df.groupby('city')['difference'].transform('sum'))
1 loop, best of 3: 570 ms per loop

In [294]: %timeit (df.groupby('city')['difference'].transform(sum))
1 loop, best of 3: 567 ms per loop

In [295]: %timeit (df.groupby('city')['difference'].transform(np.sum))
1 loop, best of 3: 561 ms per loop