When running the program on the current Pennsylvania pick 3 evening data, here is the output of the current script (with the launch settings as ProfileHotCold("rawP3E.csv",60, 10, 12, 8) ...

Distribution for column A over 60 draws:
1 8 H
4 8 H
0 7 N
2 7 N
6 7 N
8 7 N
3 5 N
5 4 C
9 4 C
7 3 C

Distribution for column B over 60 draws:
9 9 H
0 8 H
4 8 H
2 6 N
5 6 N
7 6 N
1 5 N
8 5 N
6 4 C
3 3 C

Distribution for column C over 60 draws:
1 8 H
3 8 H
2 7 N
5 7 N
8 7 N
9 7 N
6 5 N
0 4 C
4 4 C
7 3 C

Final classifier count summary:
A: 2 H - 5 N - 3 C
B: 3 H - 5 N - 2 C
C: 2 H - 5 N - 3 C

Classifications for the last 10 rows (side by side):
5 C    5 N    8 N
6 N    5 N    6 N
3 N    7 N    6 N
6 N    6 C    2 N
2 N    1 N    6 N
9 C    4 H    0 C
5 C    2 N    3 H
3 N    3 C    6 N
2 N    8 N    5 N
1 H    5 N    7 C

a few things of note... this was not a script for general use, hence the lack of headers in the distribution counts, but here is how that works... 

example: 1 8 H 

1 is the digit, 8 is the number of times it came out (frequency, the distribution is sorted by frequency, descending), H is the classifier because it was >= 12% of the 60 draws in X (13.33% actually)

The classifier count is per column, with A being the first column of results. it is a quick visual summary of the 3 distribution columns above it.

Also notice the frequency of patterns in the last 10 draws... N being the most common overall, with 4 draws with an  N N N profile and 2 with the pattern N C N... it's as if the hot ones are less of a factor.

I wrote it that if I enter a 0 for Y, it skips classifying any draws because it is the goal to have current data classified to make a pick.

Not sure what good it will do, but it was another idea turned into code, so on that front, it was already a win.