arrow2 VS datapane

One of the interesting components of Polars that I've been watching is the use of the Apache Arrow memory format, which is a standard layout for data in memory that enables processing (querying, iterating, calculating, etc) in a language agnostic way, in particular without having to copy/convert it into the local object format first. This enables cross-language data access by mmaping or transferring a single buffer, with zero [de]serialization overhead.

For some history, there's has been a bit of contention between the official arrow-rs implementation and the arrow2 implementation created by the polars team which includes some extra features that they find important. I think the current status is that everyone agrees that having two crates that implement the same standard is not ideal, and they are working to port any necessary features to the arrow-rs crate and plan on eventually switching to it and deprecating arrow2. But that's not easy.

https://github.com/apache/arrow-rs/issues/1176

https://github.com/jorgecarleitao/arrow2/pull/1476

https://github.com/jorgecarleitao/arrow2 https://github.com/apache/arrow-datafusion https://github.com/apache/arrow-ballista https://github.com/pola-rs/polars https://github.com/duckdb/duckdb

Currently datafusion and polars aren't directly operable iirc because they use different underlying arrows implementations, but there seems to be work being done on that here https://github.com/jorgecarleitao/arrow2/issues/1429

[arrow2](https://github.com/jorgecarleitao/arrow2) and [parquet2](https://github.com/jorgecarleitao/parquet2) are great foundational libraries for and DataFrame libs in Rust.

[1] https://github.com/jorgecarleitao/arrow2

In lockstep with arrow2's 0.13 release, we have published polars 0.23.0.

This is so freaking life-saving! - we have been using StreamingIterator and FallibleStreamingIterator in libraries (arrow2 and parquet2) and the existing landscape is quite confusing for new users!

arrow2 has support for mssql via ODBC (which microsoft has first class support to). Here are the integration tests we have (both read and write) against mssql specifically.

If you're looking for an easy way to build an HTML report using Python, you might find Datapane (https://github.com/datapane/datapane) helpful. I'm one of the people building it! We don't support polars (yet, on the roadmap) but we do support pandas so you can convert to a pandas DataFrame and include your data and any plots, etc.

If you're interested in an easier way to create reports using Python and Plotly/Pandas, you should check out our open-source library, Datapane: https://github.com/datapane/datapane - you can create a standalone, redistributable HTML file in a few lines of Python.

You might be interested in what we're hacking on at Datapane (I'm one of the founders): https://github.com/datapane/datapane.

You can create standalone HTML data reports from Python/Jupyter in ~3 lines of code: https://docs.datapane.com/reports/overview/

You can build web apps from Jupyter using Datapane [0]. I'm one of the founders, so let me know if I can help at all.

You can either export a static site [1] (and host on GH pages or S3), or, if you need backend logic, you can add Python functions [2] and serve on your favourite host (we use Fly).

We have specific Jupyter integration to automatically convert your notebook into an app [3].

[0] https://github.com/datapane/datapane

[1] https://docs.datapane.com/reference/reports/#datapane.proces...

[2] https://docs.datapane.com/apps/overview/

[3] https://docs.datapane.com/reports/jupyter-integration/#conve...

Our GitHub is https://github.com/datapane/datapane and you can get started here: https://docs.datapane.com/quickstart/

Thanks a lot! Yes, absolutely, a few people have brought this up and working working on removing the header right now. If I can help at all, feel free to reach us on GH Discussions: https://github.com/datapane/datapane/discussions