polars
raku-most-wanted
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| polars | raku-most-wanted | |
|---|---|---|
| 144 | 5 | |
| 26,043 | 66 | |
| 6.1% | - | |
| 10.0 | 2.2 | |
| 6 days ago | about 1 year ago | |
| Rust | Perl | |
| MIT License | - |
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
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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)
raku-most-wanted
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Dockerfile Examples for Inline::Python
As part of an discussion on this thread, someone pointed out some difficulty getting Inline::Python to respond:
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Raku for the the scientist/PROGRAMMER that is hitting the limits of Python
module which would be a prerequisite. I would support this going on the raku-most-wanted list as it would be another brick in the python2raku wall...
- Most wanted libraries?
What are some alternatives?
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 🚀
oapi-codegen - Generate Go client and server boilerplate from OpenAPI 3 specifications
modin - Modin: Scale your Pandas workflows by changing a single line of code
raku-jupyter-kernel - Raku Kernel for Jupyter notebooks
datafusion - Apache DataFusion SQL Query Engine
ecosystem - Raku ecosystem – modules and more
DataFrames.jl - In-memory tabular data in Julia
Inline-Python - Inline::Python for Perl 6
datatable - A Python package for manipulating 2-dimensional tabular data structures
mu - Universal Raku repository (formerly called "pugs repository")
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
star - Rakudo Star (Raku distribution)