One bank I use allows me to customize a set of Categories.  The categories can 
have hierarchy denoted with “/“.  There is a facility for static mapping of 
text found in the description and the categories.

I would like to create categories at the bank which will match the accounts in 
GNUCash.  In this way, I can do a simple search and replace in a spreadsheet 
program to convert “/“ to “:”, and also to prepend accounts with, for example, 
“Liabilities:” or “Expenses:”.  The whole operation from export, to search and 
replace, to exporting back to csv from a spreadsheet would take less than a 
minute.

What does the “Import Transactions from CSV” wizard expect of the contents of 
fields to which one maps “Account”?  Does it expect the full literal text 
representing the account as I have implied in my search and replace?   For 
example, if Date, Description and Deposit fields are defined, and the Account 
information in the csv is “Expenses:Coffee:Equipment and Maintenance”, will 
this be the account specified in the imported transaction?

Secondly, if the “Import Transactions from CSV” wizard does expect the account 
in this way, will the Bayesian learning be applied to the transaction so that 
it can benefit from the mapping and learn from the transactions?  My guess is 
that it would not, because I would expect the transactions to behave as if they 
were manually specified.  However, I think it would be useful to allow it to 
learn from this import, because I will not always import transactions in this 
way.  In other words, I will likely import a few transactions every day so that 
I can more easily keep up with bookkeeping.  In this case, the algorithm can 
benefit from a massive import when, say, I catch up my books *before* the 
opening date to extend them further into the past and do all the reconciliation 
needed to make that happen.

Thanks,

Justin



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