I have an existing python script that runs through a nested series of for loops. The script works as expected, however, I would like to optimize the script with total time to completion being the key performance indicator to measure against.
Take an existing python script with nested for loops and convert the loops into parallel processes.
Once all processes are complete the final dataset should be merged. The final dataset should match the [url removed, login to view] file provided.
I would like to run the loops in parallel and async if possible.
In order to implement async this Python2 script may require conversion to Python3
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I am fast with parallel programming and experienced with Python and JSON, most probably I can do this efficiently, I would like to check your script!
this is what i am doing my whole life, all you need is some changes in python code, how to add parallelize: depends on code, but i guarantee i can do it in 4-5 hours max :)