4 ms·
For the next step, and easier name matching... why not export a CSV of your online banking and use names and totals to match? Or are these cash receipts?
by pbnjay 11y ago
For the next step, and easier name matching... why not export a CSV of your online banking and use names and totals to match? Or are these cash receipts?
- omn1 11y agoGood idea! Most of the time I pay in cash when I'm at a supermarket or at a gas station. So I don't really have the data in online banking. It's kind of common in Germany.
- deleted 11y ago[deleted]
- amboar 11y agoYeah, this is what I ended up doing. I maintain a little project[1] that parses the CSV files and graphs your spending habits as well. I also wanted fuzzy string matching, but I ended up writing my own clusterer in C to get the speed I wanted, and then wrapped it up as a python module. Must admit I hadn't considered difflib. The c code now lives in ccan[2]. I've got a patch to add the string vector cosine measurement as a filter which gives quite a big performance boost. As a rough indicator, on my laptop it clusters 3500 transaction descriptions in about 600ms. [1] https://github.com/amboar/fpos/ https://github.com/amboar/fpos/ [2] http://ccodearchive.net/info/strgrp.html http://ccodearchive.net/info/strgrp.html