---
layout: post
title: Automating a Parking Expenses Report
date: 2025-03-02 14:38:00 Europe/Amsterdam
categories: paperless paperless-ngx nix python python3
---

For the past year, I was consulting at a place where I could request
reimbursement of parking expenses. This had to be claimed using a standard Excel
template.

Doing this manually every month seemed boring, so I automated a significant part
of that:

- Fetch parking costs for a certain month from my Paperless-ngx instance
- Calculate costs after taxes and a grand total
- Fill in an Excel sheet with the details
- Convert the Excel sheet to PDF and append photos of parking costs

Note: I don't expect anybody to have the same requirements as me here, but
hopefully pieces can be useful to some. The open-sourced project can be found
[here](https://git.kun.is/pim/parking-expenses).

# Organizing the Parking Costs

Each time I would park, I would accumulate a parking ticket. This ticket
contains the following information:

- Date and time
- Car license plate
- Price
- VAT

In order to automate the report, I had to have this information in a structured
way. Therefore, I turned to [Paperless-ngx](https://docs.paperless-ngx.com/).
Paperless-ngx is an self-hostable open-source service that helps digitalizing
paper documents. Third-party mobile apps help quickly scanning documents and
uploading them to the server. This is exactly what I did with the parking
tickets as well: each time I received a ticket, I would scan it with the
Paperless app.

Paperless-ngx uses Optical Character Recognition (OCR) to cleverly extract the
date of the ticket automatically. I also created a custom label to indicate a
parking ticket, and I added custom fields to indicate a ticket's cost and VAT
amount. You can see this information in the screenshot below.

![foo](ticket.png) _Translated from Dutch: aanmaakdatum means creation date,
parkeerkaart means parking ticket, BTW means VAT and bedrag means cost_

Paperless-ngx will also automatically learn to label the tickets with the
"parking ticket" label, based on the document's content. That just leaves the
VAT and price data points. Unfortunately I found the OCR to be too unreliable to
extract that text from the documents. Therefore, I had to manually set those two
data points for each ticket.

Neat, we now have all parking tickets organized on Paperless-ngx! In order to
create a report, we can just query the Paperless-ngx API!

# Using the Paperless-ngx API

The remaining part of this post will be code explanations. You can find the full
open-sourced Python code
[here](https://git.kun.is/pim/parking-expenses/src/commit/645fd8b4e4e46bc8f3d8ba831ca28261cd3edb39/main.py).

Paperless-ngx has [an API](https://docs.paperless-ngx.com/api/) we can now use
to query our parking tickets. I found the filtering logic pretty hard to
understand. Therefore I simply looked at what API calls the web UI makes with
the query I needed. This turned out to be something like this:

```python
response = requests.get(
    f"{paperless_ngx_url}/api/documents/?page_size=50&query=created:[{start_date} TO {end_date}]&tags__id__all={FILTER_TAG_ID}&correspondent__id__in={FILTER_CORRESPONDENT_ID}",
    headers={"Authorization": f"Token {token}"},
)
```

In the above code snippet, `FILTER_TAG_ID` is the ID of the "Parking ticket"
label and `FILTER_CORRESPONDENT_ID` is the ID of the correspondent (I included
this to filter out potential unrelated parking tickets).

Perfect, this returns a JSON list of parking tickets we can use to create the
report!

# Creating the Report

All that's left is to fill in an Excel template now. This actually proved to be
the most annoying part but also the most boring part. I used the
[OpenPyXL](https://openpyxl.readthedocs.io/en/stable/) Python library to
manipulate the Excel sheets and basically just add a row for each parking
ticket. For code, check
[here](https://git.kun.is/pim/parking-expenses/src/commit/645fd8b4e4e46bc8f3d8ba831ca28261cd3edb39/main.py#L104).

To convert the Excel sheet to a PDF, I used the (apparently now deprecated)
[unoconv](https://github.com/unoconv/unoconv) utility. It uses LibreOffice under
the hood for the conversion, which adds a 2GiB dependency for the project 🫠.

Finally, to merge the report PDF with photos of the parking tickets, I used the
[PyPDF2](https://pypdf2.readthedocs.io/en/3.x/) Python library.

I would now show some example output, but it contains quite some sensitive
information so unfortunately I can't... But I never had any complaints from the
finance department, so it worked great!