dataiter VS explorer

Here's another alternative. I wrote Dataiter specifically as I too was frustrated with Pandas. In my experience if you design a new API from scratch (and don't try to reimplement the Pandas API as many projects have done!) and have some vision and consistent principles, it's well possible to get a good intuitive API as a result. Two relevant issues remain: You're limited by NumPy's datatypes and their problems, such as memory-hogging strings and a lack of a proper missing value (NA), and secondly, limited by the Python language, so compared to e.g. dplyr's non-standard evaluation, you'll need to use lambda functions, which are unfortunately clumsy and verbose.

https://github.com/otsaloma/dataiter

Here's a comparison of dplyr vs. Dataiter vs. Pandas, which should give quick overview of the similarieties and differences.

https://dataiter.readthedocs.io/en/latest/_static/comparison...

Agreed, dplyr is great.

I built my own data frame implementation on top of NumPy specifically trying to accomplish a better API, similar to dplyr. It's not exactly the same naming or operations, but should feel familiar and much simpler and consistent than Pandas. And no indexes or axes.

Having done this, a couple notes on what will unavoidably differ in Python

* It probably makes more sense in Python to use classes, so method chaining instead of function piping. I wish one could syntactically skip enclosing parantheses in Python though, method chains look a bit verbose.

* Python doesn't have R's "non-standard evaluation", so you end up needing lambda functions for arguments in method chains and group-wise aggregation etc. I'd be interested if someone has a better solution.

* NumPy (and Pandas) is still missing a proper missing value (NA). It's a big pain to try to work around that.

https://github.com/otsaloma/dataiter

The Explorer library [0] in Elixir uses Polars underneath it.

[0] https://github.com/elixir-explorer/explorer

To ensure you do not miss this: LiveBook comes with a Vega Lite integration (https://livebook.dev/integrations -> https://livebook.dev/integrations/vega-lite/), which means you get access to a lot of visualisations out of the box, should you need that (https://vega.github.io/vega-lite/).

In the same "standing on giant's shoulders" stance, you can use Explorer (see example LiveBook at https://github.com/elixir-explorer/explorer/blob/main/notebo...), which leverages Polars (https://www.pola.rs), a very fast DataFrame library and now a company (https://www.pola.rs/posts/company-announcement/) with 4M$ seed.

Already exists. Check out https://github.com/elixir-nx/explorer which provides a tidyverse-like API in Elixir using polars as the back end.

José from the Livebook team. I don't think I can make a pitch because I have limited Python/R experience to use as reference.

My suggestion is for you to give it a try for a day or two and see what you think. I am pretty sure you will find weak spots and I would be very happy to hear any feedback you may have. You can find my email on my GitHub profile (same username).

In general we have grown a lot since the Numerical Elixir effort started two years ago. Here are the main building blocks:

* Nx (https://github.com/elixir-nx/nx/tree/main/nx#readme): equivalent to Numpy, deeply inspired by JAX. Runs on both CPU and GPU via Google XLA (also used by JAX/Tensorflow) and supports tensor serving out of the box

* Axon (https://github.com/elixir-nx/axon): Nx-powered neural networks

* Bumblebee (https://github.com/elixir-nx/bumblebee): Equivalent to HuggingFace Transformers. We have implemented several models and that's what powers the Machine Learning integration in Livebook (see the announcement for more info: https://news.livebook.dev/announcing-bumblebee-gpt2-stable-d...)

* Explorer (https://github.com/elixir-nx/explorer): Series and DataFrames, as per this thread.

* Scholar (https://github.com/elixir-nx/scholar): Nx-based traditional Machine Learning. This one is the most recent effort of them all. We are treading the same path as scikit-learn but quite early on. However, because we are built on Nx, everything is derivable, GPU-ready, distributable, etc.

Regarding visualization, we have "smart cells" for VegaLite and MapLibre, similar to how we did "Data Transformations" in the video above. They help you get started with your visualizations and you can jump deep into the code if necessary.

I hope this helps!

There's a package in the Nx ecosystem called Explorer (https://github.com/elixir-nx/explorer). It uses bindings for the rust library, polars, which is much more betterer than Pandas.

FWIW around April this year I started using erlport with python polars in a production ETL app because explorer didn't have the features I needed at the time.

Machine learning is rapidly expanding within the Elixir ecosystem, with tools such as Nx, Axon, and Explorer being used both by individuals and companies such as Amplified, as mentioned above.