polars-ruby
polars
| polars-ruby | polars | |
|---|---|---|
| 10 | 144 | |
| 755 | 26,514 | |
| - | 3.9% | |
| 9.7 | 10.0 | |
| 2 days ago | 6 days ago | |
| Ruby | Rust | |
| MIT License | GNU General Public License v3.0 or later |
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.
polars-ruby
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Rails version of Python Dataframes
You might give this https://github.com/ankane/polars-ruby a look.
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Magnus 0.5 released (Library for writing Ruby gems in Rust)
polars-df
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Bundler: Bundler v2.4: new resolver, gems with Rust extensions, and more
https://github.com/ankane/polars-ruby is an example that was just posted here recently
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Tried polars in Ruby
Polars is a data frame in the Rust language using Apache Arrow Columnar Format. polars-ruby is the Ruby binding for Polars created by Andrew Kane.
- GitHub - ankane/polars-ruby: Blazingly fast DataFrames for Ruby
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Fast DataFrames for Ruby
I think its really interesting such gems offering a layer ruby on top of rust libs. One issue that I have with that is - and maybe it's my ignorance - but is that necessary to bundle the original lib as https://github.com/ankane/polars-ruby/tree/master/ext/polars ? I can imagine that makes easier to avoid breakage but couldn't we at least lock its version in Cargo.toml?
- Blazingly fast DataFrames for Ruby, powered by Polars
polars
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Why Python's Integer Division Floors (2010)
This is because 0.1 is in actuality the floating point value value 0.1000000000000000055511151231257827021181583404541015625, and thus 1 divided by it is ever so slightly smaller than 10. Nevertheless, fpround(1 / fpround(1 / 10)) = 10 exactly.
I found out about this recently because in Polars I defined a // b for floats to be (a / b).floor(), which does return 10 for this computation. Since Python's correctly-rounded division is rather expensive, I chose to stick to this (more context: https://github.com/pola-rs/polars/issues/14596#issuecomment-...).
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Polars
https://github.com/pola-rs/polars/releases/tag/py-0.19.0
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Stuff I Learned during Hanukkah of Data 2023
That turned out to be related to pola-rs/polars#11912, and this linked comment provided a deceptively simple solution - use PARSE_DECLTYPES when creating the connection:
- Polars 0.20 Released
- Segunda linguagem
- Polars: Dataframes powered by a multithreaded query engine, written in Rust
- Summing columns in remote Parquet files using DuckDB
- Polars 0.34 is released. (A query engine focussing on DataFrame front ends)
What are some alternatives?
rucaptcha - Captcha Gem for Rails, which generates captcha image by Rust.
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
ruby-spark - Ruby wrapper for Apache Spark
modin - Modin: Scale your Pandas workflows by changing a single line of code
bundix - Generates a Nix expression for your Bundler-managed application. [maintainer=@manveru]
datafusion - Apache DataFusion SQL Query Engine
halton-rb - A Ruby library, written in Rust, for generating Halton sequences
DataFrames.jl - In-memory tabular data in Julia
yrb - Ruby bindings for yrs.
datatable - A Python package for manipulating 2-dimensional tabular data structures
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing