I'm building a report builder for my Django app and could use a little advice.
My reports are fairly simple where I accumulate scores of data (easy enough) but then I want to alter the report totals by varying dimensions (date ranges / split dates/weeks/months / owners / other metadata etc.). Since I am working with Django Querysets, I have some options as to how I can query the data into one query set with joins where I can traverse the joins for my accumulating data. Or I can take multiple querysets and join them in my app manually which simplifies the queries somewhat (this optimization might come later when I load test the app). My data might look something like this: Parent (with useful dimensional metadata) -> Child (with useful dimensional metadata) ->Child of child (accumulating data source, i.e. Counts to aggregate) I see some stuff about Pandas, also Anaconda. I took a brief look at both and they definitely both sound more hardcore than I need, but then I don't feel like rolling my own axis/dimensional modelling logic if I can perhaps build a dataset and have the app do it for me. Which package is recommended for babby's first stat package that can meet my requirements? Ideally one that uses less resources as I plan to scale this app up quite a bit in production. Also for whichever package recommended, where would I find some good basic tutorials on how to build my dataset and alter it for reporting purposes? I plan to build charts on the client-side front-end with HTML5/js/css. -- https://mail.python.org/mailman/listinfo/python-list
