I'd like to find out what approaches the community have used to achieve similar results using either:
- Data Access Module or;
- NumPy
The following table (stats_table1) is my starting point:
I'd like to populate a new table (stats_table2) based on the following structure:
for each row in the first table:
- stats_table2 [SETTLEMENTNAME] = stats_table1 [SETTLEMENTNAME]
- stats_table2 [SOCIAL_FACILITY] = stats_table1 [NAME]
- stats_table2 [TIME0_15MIN] = stats_table1 (((TIME5 + TIME10 + TIME15)) / TOTALBUILD)*100)
- stats_table2 [TIME15 _30MIN] = stats_table1 (((TIME20 + TIME25 + TIME30)) / TOTALBUILD)*100)
- stats_table2 [TIME30 _60MIN] = stats_table1 ((TIME60 / TOTALBUILD)*100)
- stats_table2 [TIME60_PLUS] = stats_table1 ((TIME60P / TOTALBUILD)*100)
Final Results:
i.e.
Data Access Module:
- Would you use a Search Cursor to loop through stats_table1, perform the following calculations and write the results to a python dictionary, then use a Update Cursor to populate stats_table2
NumPy:
- Would you convert the stats_table1 to a NumPy array, perform the following calculations and write the results into a temporary array and and back to a table to be appended to stats_table2
Any sample code or references will be appreciated as I originally was looking at nesting a Search Cursor with a Update Cursor, then realised it was a bad idea.
