explorer
datasette
explorer | datasette | |
---|---|---|
20 | 187 | |
977 | 8,955 | |
1.2% | - | |
9.4 | 9.3 | |
6 days ago | 7 days ago | |
Elixir | Python | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
explorer
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Polars
The Explorer library [0] in Elixir uses Polars underneath it.
[0] https://github.com/elixir-explorer/explorer
- Unpacking Elixir: Concurrency
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Elixir Livebook is a secret weapon for documentation
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.
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Does anyone else hate Pandas?
Already exists. Check out https://github.com/elixir-nx/explorer which provides a tidyverse-like API in Elixir using polars as the back end.
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Data wrangling in Elixir with Explorer, the power of Rust, the elegance of R
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!
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Would you still choose Elixir/Phoenix/LiveView if scaling and performance weren’t an issue to solve for?
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.
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Updated Erlport alternative ?
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.
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ElixirConf 2022 - That's a wrap!
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.
- Dataframes but for Elixir
- Quick candlestick summaries with Elixir's Explorer
datasette
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Ask HN: High quality Python scripts or small libraries to learn from
Simon Willison's github would be a great place to get started imo -
https://github.com/simonw/datasette
- Show HN: TextQuery – Query and Visualize Your CSV Data in Minutes
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Little Data: How do we query personal data? (2013)
I'm a fan on simonw's datasette/dogsheep ecosystem https://datasette.io/
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LaTeX and Neovim for technical note-taking
I use Anki the exact same way. After a lifetime of learning I have accepted that I will never read over anything I write for myself voluntarily - so my two options are:
1. Write an article so good I can publish it and look it over myself later on. I did this last year with https://andrew-quinn.me/fzf/, for example.
2. Create Anki cards out of the material. Use the builtin Card Browser or even https://datasette.io/ on the underlying SQLite database in a pinch to search for my notes any time I have to.
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Daily Price Tracking for Trader Joes
Were you aware of, or tempted by https://datasette.io/ for creating your solution?
- SQLite-Web: Web-based SQLite database browser written in Python
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Ask HN: What two software products should have a kid?
Browsing HN, GitHub and the like we get to see a huge variety of software products and code bases.
I often see products and think - if this product X, got together with Y, it would be pretty cool - kind of like if they had a kid together.
Not too literally, but more on the conceptual level - my level of programming is low.
E.g. Just some....
- pocketable.io & datasette (+with some more charting) [https://pocketbase.io, https://datasette.io]
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Ask HN: Looking for a project to volunteer on? (February 2024)
You might like the Datasette project: https://datasette.io/
I don't think they are desperate for contributions but it's a welcoming environment and a fun project to hack on. You'll learn a lot just from reading the source and the incredibly informative PRs. The creator is a really talented developer with a great blog which shows up on the HN front page often.
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Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
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What We Watched: A Netflix Engagement Report – About Netflix
> uploads of boring raw excel data and receive a nice UI
https://datasette.io/
What are some alternatives?
dplyr - dplyr: A grammar of data manipulation
nocodb - 🔥 🔥 🔥 Open Source Airtable Alternative
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
duckdb - DuckDB is an in-process SQL OLAP Database Management System
axon - Nx-powered Neural Networks
sql.js-httpvfs - Hosting read-only SQLite databases on static file hosters like Github Pages
db-benchmark - reproducible benchmark of database-like ops
litestream - Streaming replication for SQLite.
arrow2 - Transmute-free Rust library to work with the Arrow format
Sequel-Ace - MySQL/MariaDB database management for macOS
wasmex - Execute WebAssembly from Elixir
beekeeper-studio - Modern and easy to use SQL client for MySQL, Postgres, SQLite, SQL Server, and more. Linux, MacOS, and Windows.