Hello everyone,
For the past two months I've been working on belugas:
https://github.com/OutSquareCapital/belugas
It's a python dataframe library aiming to provide a polars-like API to build and execute queries on a duckdb backend.
Compared to Ibis or narwhals, the goal is not to be a general multi-backend tool, but rather a specialist one, covering as much as possible of polars and duckdb functionnalities (currently +700 expressions functions, selectors, unpivot, join_asof, geometry datatypes, and more).
Disclaimer:
I'm just an uni student, I have no affiliation at all with DuckDB, or polars.
It's not an "official" project showcase, because even if it work and has already can do a lot, it's still WIP.
There's no website doc yet, nor Pypi package, nor proper docstrings in all expressions, nor full test coverage (+1000 currently but it should be way more), etc....
I will do one at some point in the future however!
Hence, the motivation behind my post is to ask the following question:
If you were to use this library, would you rather have duckdb aligned results after computation, or polars ones?
They can diverge quite a lot.
For example, the `millisecond` function doesn't give the same results at all, the `len` function doesn't have the same meaning, column names can be different after a join, etc...
I see pros and cons for both.
## DuckDB aligned results
- Best performance wise if there's a divergence that need a coalesce, null handling, or custom column name resolution implementation
- Aligned with duckdb documentation and backend expectations
- Are simpler to implement internally
## Polars aligned results
- Way easier to test (simply check equality with polars computations)
- I expect that polars users would be more interested by belugas than duckdb users, hence they would be the most expected results
- The possibility to simply switch from `bl.col()` to `pl.col` seamessly in a data pipeline when one tool is preferrable to the other (which is a huge point IMO)