Add a `.to_polars_df()` method (very similar to `.to_dataframe()`, which implicitly uses pandas)

[DeflateAwning]

Is your feature request related to a problem?

Pandas is much less performant, and is decreasingly used in new projects. It would be awesome to be able to move data out of xarray and into Polars directly, without jumping through Pandas.

Describe the solution you'd like

Add a .to_polars_df() method (very similar to .to_dataframe(), which implicitly uses pandas)

Describe alternatives you've considered

You currently have to do:

import polars as pl

pl.from_pandas(da.to_dataframe())`

This is slower than it could be if there was a directly-to-polars method.

Additional context

I'd even consider renaming the .to_dataframe() method to .to_pandas_df(). Suggesting that the main/default dataframe is Pandas seems a little strange in the 2025 data analysis ecosystem.

[welcome]

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[max-sixty]

What would the method do internally which would be faster than going through pandas? No objection per se, but there would need to be some benefit from adding the method...

[DeflateAwning]

I'm not too certain exactly what the current mechanisms look like, but I do know there is an opportunity for improvement as converting to Python Pandas is not a zero-cost operation.

Other cross-compatible libraries (e.g., DuckDB) have separate methods for to-pandas vs to-polars, suggesting that there are benefits (_i.e., performance benefits).

I believe there is a sort of dataframe-library-agnostic dataframe specification (something about __dataframe__ maybe) which may be effective for this.

[max-sixty]

OK, feel free to post more details when you / someone else has them. We can leave this open for a while; eventually would suggest closing until we have some legible benefit

[DocOtak]

@DeflateAwning Here is the dataframe interchange protocol spec: https://data-apis.org/dataframe-protocol/latest/index.html

I'm also interested in polars dataframe support in xarray.

[DeflateAwning]

OK, feel free to post more details when you / someone else has them. We can leave this open for a while; eventually would suggest closing until we have some legible benefit

Oh, sorry that wasn't clear. The obvious benefit is performance. The secondary benefit is avoiding Pandas; it is rightfully deamed legacy tech in all organizations I work with.

Thanks @DocOtak - that's exactly what I was talking about.

[max-sixty]

The obvious benefit is performance.

OK, I'm not saying this isn't valid, but I am asking how it would be meaningfully more performant. An example showing the improvement would be great...

The spec looks interesting, thanks for posting. I don't see it covering creating a dataframe though...

[TomNicholas]

I have to agree with @max-sixty - the question is not whether polars is faster / better than pandas in general (I believe you), but whether an xarray.DataArray().to_polars_df() method can ever be faster than pl.from_pandas(da.to_dataframe()), and if it actually is faster today.

[max-sixty]

please feel free to reopen with some empirics on performance improvements (on this specific method; we def believe polars is generally faster than pandas...)

[DeflateAwning]

Sorry, you want me to implement this and then do a performance test? Then, you'll decide if it's worth implementing?

[max-sixty]

anything that gives us some empirical data that this is worth a new method. that could be a full implementation, it could be something as simple as a comparison of creating a dataframe from an numpy array

is that reasonable?