It is in an SQL database, but it is a GIS implementation. It is cross-platform, and aggregate functions within the GIS framework are severely limited--which keeps the geographic component tied to the database component. Otherwise, I would have a more elegant SQL statement, and not this problem.

Thank you,

Thank you Kudos--I can make sense of your post, and I think I can make it work. It's much more elegant than what...

Alrighty, my data coming in is a table with four fields, OBJECTID, STREETNAME, LOW, and HIGH. Streets are broken up into blocks, so that main street will have a record for the 100 block, one for the 200 block and so on. The incoming table will look like this

OBJECTID | STREETNAME | LOW | HIGH
123456 | MAIN ST | 200 | 299
123457 | MAIN ST | 100 | 199
123458 | MAIN ST | 300 | 399

I felt it appropriate...

Hey all,

I am very new to python, and not usually a programmer. Have mercy, please :) I am comparing road address ranges to find inconsistencies . Some roads are much longer than others and are represented by many database records. Sucking the data into python, I have arranged it like this:

{CASTLERIDGE: [3281, 1000, 1099, 21553, 900, 999]}
{BROOKRIDGE: [3265, 1000, 1099, 3276, 1100, 1199, 16398, 1200, 1299]}...