mumps-examples
polars
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| mumps-examples | polars | |
|---|---|---|
| 3 | 144 | |
| 34 | 26,218 | |
| - | 6.7% | |
| 10.0 | 10.0 | |
| over 4 years ago | 2 days ago | |
| M | Rust | |
| - | 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.
mumps-examples
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I analyzed 1835 hospital price lists so you didn't have to
From the context I'm guessing MUMPS, and it kinda seems to resemble it, if it had more line breaks: https://github.com/programarivm/mumps-examples/blob/f160bfb6...
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Resources for learning Cache?
I would recommend searching for resources for GT.M, not for Cache. From a Google Search for GT.M resources, I found this reference manual which seems OK as well as this tutorial and these example programs on github. The example programs include an explanation on how to install GT.M and run it on Debian.
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We gotta get rid of ego in the programming community
This gives some better technical examples https://github.com/programarivm/mumps-examples
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?
price-transparency-guide - The technical implementation guide for the tri-departmental price transparency rule.
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 🚀
bfc - An industrial-grade brainfuck compiler
modin - Modin: Scale your Pandas workflows by changing a single line of code
php-tensorflow - PHP TensorFlow Binding
datafusion - Apache DataFusion SQL Query Engine
db-benchmark - reproducible benchmark of database-like ops
DataFrames.jl - In-memory tabular data in Julia
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
rust-numpy - PyO3-based Rust bindings of the NumPy C-API