Quiffen is a Python package for parsing QIF (Quicken Interchange Format) files.
The package allows users to both read QIF files and interact with the contents, and also to create a QIF structure and then output to either a QIF file, a CSV of transaction data or a pandas DataFrame.
QIF is an old file type, but has its merits because:
- It's standardised (apart from dates, but that can be dealt with)
- Unlike CSVs, QIF files all follow the same format, so they don't require special attention when they come from different sources
- It's written in plain text
- Import QIF files and manipulate data
- Create QIF structures (support for Transactions, Investments, Accounts, Categories, Classes, Splits)
- Convert Qif objects to a number of different formats and export (pandas DataFrame, CSV, QIF file)
Here's an example parsing of a QIF file:
>>> from quiffen import Qif
>>> import decimal
>>> qif = Qif.parse('test.qif')
>>> qif.accounts
{'Quiffen Default Account': Account(name='Quiffen Default Account', desc='The default account created by Quiffen when no
other accounts were present')}
>>> acc = qif.accounts['Quiffen Default Account']
>>> acc.transactions
{'Bank': TransactionList(Transaction(date=datetime.datetime(2021, 2, 14, 0 , 0), amount=decimal.Decimal(150.0), ...), ...),
'Invst': TransactionList(...)}
>>> tr = acc.transactions['Bank'][0]
>>> print(tr)
Transaction:
Date: 2020-02-14 00:00:00
Amount: 67.5
Payee: T-Mobile
Category: Cell Phone
Split Categories: ['Bills']
Splits: 2 total split(s)
>>> qif.categories
{'Bills': Category(name='Bills), expense=True, hierarchy='Bills'}
>>> bills = qif.categories['Bills']
>>> print(bills.render_tree())
Bills (root)
└─ Cell Phone
>>> df = qif.to_dataframe(data='transactions')
>>> df.head()
date amount payee ... memo cleared check_number
0 2020-02-14 67.5 T-Mobile ... NaN NaN NaN
1 2020-02-14 32.0 US Post Office ... money back for damaged parcel NaN NaN
2 2020-12-02 -10.0 Target ... two transactions, equal NaN NaN
3 2020-11-02 -25.0 Walmart ... non split transaction X 123.0
4 2020-10-02 -100.0 Amazon.com ... test order 1 * NaN
...
And here's an example of creating a QIF structure and exporting to a QIF file:
>>> import quiffen
>>> from datetime import datetime
>>> qif = quiffen.Qif()
>>> acc = quiffen.Account('Personal Bank Account', desc='My personal bank account with Barclays.')
>>> qif.add_account(acc)
>>> groceries = quiffen.Category('Groceries')
>>> essentials = quiffen.Category('Essentials')
>>> groceries.add_child(essentials)
>>> qif.add_category(groceries)
>>> tr = quiffen.Transaction(date=datetime.now(), amount=150.0)
>>> acc.add_transaction(tr, header='Bank')
>>> qif.to_qif() # If a path is provided, this will save the file too!
'!Type:Cat\nNGroceries\nETrue\nIFalse\n^\nNGroceries:Essentials\nETrue\nIFalse\n^\n!Account\nNPersonal Bank Account\nDMy
personal bank account with Barclays.\n^\n!Type:Bank\nD02/07/2021\nT150.0\n^\n'
Documentation can be found at: https://quiffen.readthedocs.io/en/latest/
Install Quiffen by running:
>>> pip install quiffen
- pandas (optional) for exporting to DataFrames
- The
to_dataframe()
method will not work without pandas installed.
- The
- Add support for the
MemorizedTransaction
object present in QIF files.
GitHub pull requests welcome, though if you want to make a major change, please open an issue first for discussion.
- Issue Tracker: https://github.com/isaacharrisholt/quiffen/issues
- Source Code: https://github.com/isaacharrisholt/quiffen
If you are having issues, please let me know.
The project is licensed under the GNU GPLv3 license.