Regular bank statements are presented in a very unhelpful way - simply sorted by date, with no analysis of the items to assist you to understand them.
View my Better Bank Statement.ipynb in nbviewer or download the .ipynb file.
The iPython Notebook uses Pandas to analyse your bank statement (downloaded as a .csv) and produces output like the following.
New items that haven't been seen before are separated out so they can be checked easily:
Items to payees that have been seen before but with amounts that are different are displayed graphically to let you see at a glance which are truly problematic and which are normal variations.
Recurring items are usually going to be fine, but you may want to check that an automatic payment hasn't been missed:
The same breakdown is done for credits.
You have to ask yourself: why can't the bank do something like this? Until then, see how you go playing with this notebook.
1 comment:
Tom,
Yes, you are quite right. In the past I've spent a very long time trying to categorise my transactions, so that I can create an effective budget.
Your initial categorisation seems to be about the medium used. I tried to create a second categorisation of the transaction by Biller, which then let me determine the type of item or service I'd bought. I also found that many recurring services had almost the same price (in these low inflation times), so that helped.
My process required repeatedly tagging, resorting, retagging, in a spreadsheet.
Hope yours is easier.
richard horobin
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