How to massage / normalise data read from DB

[madduck]

First Check

• I added a very descriptive title here.
• I used the GitHub search to find a similar question and didn't find it.
• I searched the SQLModel documentation, with the integrated search.
• I already searched in Google "How to X in SQLModel" and didn't find any information.
• I already searched the Web "How to X in SQLModel" and didn't find any information.
• I already read and followed all the tutorial in the docs and didn't find an answer.
• I already checked if it is not related to SQLModel but to Pydantic.
• I already checked if it is not related to SQLModel but to SQLAlchemy.

Commit to Help

• I commit to help with one of those options 👆

Example Code

# not related to specific code

Description

I have to deal with a database that regularly changes, but which imposes no constraints on the data in columns. The software is so badly designed, but there is nothing I can do about it. It's a Microsoft Access nightmare.

There are columns that are supposed to be integers, but the cells contain 0 and null interchangeably. Also, there are date columns that have both null and 1899-12-30 12:34:56 to indicate that there is no valid date stored.

When slurping up those data into SQLModels, I'd love to normalise the data, i.e. turn 0 and the weird date into None.

But how?

Validation does not work when data come from the database because it is assumed that data in the database are proper. Well, not in this case.

With SQLAlchemy's voodoo magic replacing __init__ on models, I could find no way to invoke code to run after a new instance was created and populated with data, but before it's returned to the user.

Please don't make me have to provide proxy properties, or hack __getattr__. Why can't we just have a __post_init__?

Operating System

Linux

Operating System Details

No response

SQLModel Version

0.0.24

Python Version

3.13.3

Additional Context

No response

[madduck]

I just had to discover that even overriding __getattr__ won't let me do anything to the data. :(