this post was submitted on 09 Mar 2024
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I think it's a good thing polars developers are heading toward interoperability. The Dataframe Interchange Protocol the article mentions sounds interesting.
I know this seems to be an important topic in the community. But honestly, I rarely use all the plotting backends at all. They are nice for quick visualizations, but most of the time I prefer to throw my data into matplotlib on my own, just for the sake of customization.
I don't want to complain, it is definitely a good thing polars developers address this. pandas is the standard and as long as full interoperability between polars and the pandas ecosystem is lacking, this "hack" is needed. However, data transformation can be an incredibly sensitive topic. I do not even trust pandas or tensorflow in always doing the right thing when converting data - processing data in polars, converting it to pandas and then process it further - I am sceptical. And I am not even talking about performance here.
This is important. Geopandas is one of the most import libraries derived from pandas and widely used in the geoscience community. The idea of an equivalent like "geopolars" is insane in my eyes. I am biased as a data scientist mostly working on spatial data, but this is the main reason that I watch the development of polars only from the sidelines. Even if I wouldn't work with geographic data, GeoAI is such an important topic you can't just ignore it. And that's only the perspective from my field, who knows what other important communities are out there that rely on pandas.