How to use duplicated rows in pandas DF to efficiently create new columns?

I am trying to convert the output of the value_counts() pd method into a new df structure.

pd.DataFrame({'Value': {0: 1, 1: 1, 2: 1, 3: 2, 4: 2, 5: 2, 6: 3},
 'Class': {0: 14, 1: 7, 2: 15, 3: 1, 4: 12, 5: 2, 6: 15},
 'counts': {0: 2065, 1: 1268, 2: 78, 3: 54, 4: 40, 5: 33, 6: 6}})

    Value   Class   counts
0   1       14       2065
1   1       7        1268
2   1       15       78
3   2       1        54
4   2       12       40
5   2       2        33
6   3       15       6

I am trying to consolidate to the unique ids from the Value field (1, 2, 3), and then have the unique numbers from Class field for the new fields. The the class values will be the counts.

I am trying to get something like:

     Value    1    2    7    12    14    15 
0    1        0    0    1268 0     2065  78
1    2        54   33   0    40    0     0 
2    3        0    0    0    0     0     6

I know I can brute-force it, but I am working with 10+ million unique "Value" ids, so 50+ million count rows. I am trying to figure out a fast and effective way to do this.

>Solution :

This can be done with a pivot table:

df.pivot(index='Value', columns='Class', values='counts').fillna(0)