Livebook is a web application for writing interactive and collaborative code notebooks for #elixir, built with Phoenix LiveView. Inspired by Jupyter and Deepnote.
It features:
- Code notebooks with Markdown support and Elixir cells where code is evaluated on demand.
- Shareable: notebooks are stored in the .livemd format, which is a subset of Markdown with annotations and KaTex for mathematical formulas. This means your notebooks can be saved, easily shared, and play well with version control.
- Interactive widgets via Kino: manipulate Vega-Lite charts, tables, and more.
- Rich code editor through Monaco: with support for autocompletion, inline documentation, code formatting, etc.
- Reproducible: Livebook ensures your code runs in a predictable order, all the way down to package management. It also tracks your notebook state, annotating which parts are stale.
- Custom runtimes: when executing Elixir code, you can either start a fresh Elixir instance, connect to an existing node, or run it inside an existing Elixir project, with access to all of its modules and dependencies. This means Livebook can be a great tool to provide live documentation for existing projects.
- Persistence: persist your notebooks to disk or any S3-compatible cloud storage.
- Collaboration: multiple users can work on the same notebook at once. It works out-of-the-box either in single-node or multi-node deployments - without a need for additional tooling.
https://github.com/livebook-dev/livebook
It features:
- Code notebooks with Markdown support and Elixir cells where code is evaluated on demand.
- Shareable: notebooks are stored in the .livemd format, which is a subset of Markdown with annotations and KaTex for mathematical formulas. This means your notebooks can be saved, easily shared, and play well with version control.
- Interactive widgets via Kino: manipulate Vega-Lite charts, tables, and more.
- Rich code editor through Monaco: with support for autocompletion, inline documentation, code formatting, etc.
- Reproducible: Livebook ensures your code runs in a predictable order, all the way down to package management. It also tracks your notebook state, annotating which parts are stale.
- Custom runtimes: when executing Elixir code, you can either start a fresh Elixir instance, connect to an existing node, or run it inside an existing Elixir project, with access to all of its modules and dependencies. This means Livebook can be a great tool to provide live documentation for existing projects.
- Persistence: persist your notebooks to disk or any S3-compatible cloud storage.
- Collaboration: multiple users can work on the same notebook at once. It works out-of-the-box either in single-node or multi-node deployments - without a need for additional tooling.
https://github.com/livebook-dev/livebook
Dataframes for #elixir
Explorer is a dataframe library for Elixir. First and foremost, Explorer is an API for data manipulation. Its high-level features are:
- Simply typed series:
- A powerful but constrained and opinionated API, so you spend less time looking for the right function and more time doing data manipulation.
- Pluggable backends, providing a uniform API whether you're working in-memory or (forthcoming) on remote databases or even Spark dataframes.
- The first (and default) backend is based on NIF bindings to the blazing-fast polars library.
The API is influenced heavily by Tidy Data and borrows much of its design from dplyr.
The aim here isn't to have the fastest dataframe library around (though it certainly helps that we're building on one of the fastest). Instead, we're aiming to bridge the best of many worlds:
- the elegance of dplyr
- the speed of polars
- the joy of Elixir
That means you can expect the guiding principles to be 'Elixir-ish'. For example, you won't see the underlying data mutated, even if that's the most efficient implementation. Explorer functions will always return a new dataframe or series.
https://github.com/elixir-nx/explorer
Explorer is a dataframe library for Elixir. First and foremost, Explorer is an API for data manipulation. Its high-level features are:
- Simply typed series:
:float, :integer, :boolean, :string, :date, and :datetime.- A powerful but constrained and opinionated API, so you spend less time looking for the right function and more time doing data manipulation.
- Pluggable backends, providing a uniform API whether you're working in-memory or (forthcoming) on remote databases or even Spark dataframes.
- The first (and default) backend is based on NIF bindings to the blazing-fast polars library.
The API is influenced heavily by Tidy Data and borrows much of its design from dplyr.
The aim here isn't to have the fastest dataframe library around (though it certainly helps that we're building on one of the fastest). Instead, we're aiming to bridge the best of many worlds:
- the elegance of dplyr
- the speed of polars
- the joy of Elixir
That means you can expect the guiding principles to be 'Elixir-ish'. For example, you won't see the underlying data mutated, even if that's the most efficient implementation. Explorer functions will always return a new dataframe or series.
https://github.com/elixir-nx/explorer
KDL is a document language with xml-like semantics that looks like you're invoking a bunch of CLI commands! It's meant to be used both as a serialization format and a configuration language, much like JSON, YAML, or XML.
Features:
- Node-based, you won't have to mess with identation to make it work
- Type annotations
- Implementations in different languages: #python, #elixir, #rust, #java, #js, #ruby, #php, etc
https://kdl.dev/
Features:
- Node-based, you won't have to mess with identation to make it work
- Type annotations
- Implementations in different languages: #python, #elixir, #rust, #java, #js, #ruby, #php, etc
https://kdl.dev/