I applied sum() on a groupby and I want to sort the values of the last column

Question

Given the following DataFrame

user_ID  product_id  amount
   1       456          1
   1        87          1
   1       788          3
   1       456                


        

Answer 1

Suppose df is:

     user_ID  product_id  amount
0        1         456       1
1        1          87       1
2        1         788       3
3        1         456       5
4        1          87       2
5        2         456       1
6        2         788       3
7        2         456       5

Then you can use, groupby and sum as before, in addition you can sort values by two columns [user_ID, amount] and ascending=[True,False] refers ascending order of user and for each user descending order of amount:

new_df = df.groupby(['user_ID','product_id'], sort=True).sum().reset_index()
new_df = new_df.sort_values(by = ['user_ID', 'amount'], ascending=[True,False])
print(new_df)

Output:

     user_ID   product_id  amount
1        1         456       6
0        1          87       3
2        1         788       3
3        2         456       6
4        2         788       3

Answer 2

You could also use aggregate():

# Make up some example data
df = data.frame (user_ID = as.factor(rep(1:5, each = 5)), 
                 product_id = as.factor(sample(seq(1:10),size = 25, replace = TRUE)),
                 amount = sample(1:5, size = 25, replace = TRUE))

# Use aggregate with function sum to calculate the amount of products bought by product and customer
aggregate(amount ~  product_id * user_ID , data = df, FUN = sum)

Output:

   product_id user_ID amount
1           2       1      3
2           4       1      2
3           6       1      1
4           9       1      5
5           1       2      5
6           3       2      9
7           8       2      1
8          10       2      5
9           2       3      5
10          3       3      5
11          4       3      5
12          5       3      3
13          8       3      5
14          3       4      3
15          4       4      9
16          5       4      2
17         10       4      1
18          2       5      1
19          4       5      4
20          5       5      2
21         10       5      2