mumps-examples
db-benchmark
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mumps-examples | db-benchmark | |
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3 | 91 | |
34 | 320 | |
- | 1.3% | |
10.0 | 0.0 | |
over 4 years ago | 10 months ago | |
M | R | |
- | Mozilla Public License 2.0 |
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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
db-benchmark
- Database-Like Ops Benchmark
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Polars
Real-world performance is complicated since data science covers a lot of use cases.
If you're just reading a small CSV to do analysis on it, then there will be no human-perceptible difference between Polars and Pandas. If you're reading a larger CSV with 100k rows, there still won't be much of a perceptible difference.
Per this (old) benchmark, there are differences once you get into 500MB+ territory: https://h2oai.github.io/db-benchmark/
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DuckDB performance improvements with the latest release
I do think it was important for duckdb to put out a new version of the results as the earlier version of that benchmark [1] went dormant with a very old version of duckdb with very bad performance, especially against polars.
[1] https://h2oai.github.io/db-benchmark/
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Show HN: SimSIMD vs. SciPy: How AVX-512 and SVE make SIMD cleaner and ML faster
https://news.ycombinator.com/item?id=33270638 :
> Apache Ballista and Polars do Apache Arrow and SIMD.
> The Polars homepage links to the "Database-like ops benchmark" of {Polars, data.table, DataFrames.jl, ClickHouse, cuDF, spark, (py)datatable, dplyr, pandas, dask, Arrow, DuckDB, Modin,} but not yet PostgresML? https://h2oai.github.io/db-benchmark/ *
LLM -> Vector database: https://en.wikipedia.org/wiki/Vector_database
/? inurl:awesome site:github.com "vector database"
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Pandas vs. Julia โ cheat sheet and comparison
I agree with your conclusion but want to add that switching from Julia may not make sense either.
According to these benchmarks: https://h2oai.github.io/db-benchmark/, DF.jl is the fastest library for some things, data.table for others, polars for others. Which is fastest depends on the query and whether it takes advantage of the features/properties of each.
For what it's worth, data.table is my favourite to use and I believe it has the nicest ergonomics of the three I spoke about.
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Any faster Python alternatives?
Same. Numba does wonders for me in most scenarios. Yesterday I've discovered pola-rs and looks like I will add it to the stack. It's API is similar to pandas. Have a look at the benchmarks of cuDF, spark, dask, pandas compared to it: Benchmarks
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Pandas 2.0 (with pyarrow) vs Pandas 1.3 - Performance comparison
The syntax has similarities with dplyr in terms of the way you chain operations, and itโs around an order of magnitude faster than pandas and dplyr (thereโs a nice benchmark here). Itโs also more memory-efficient and can handle larger-than-memory datasets via streaming if needed.
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Pandas v2.0 Released
If interested in benchmarks comparing different dataframe implementations, here is one:
https://h2oai.github.io/db-benchmark/
- Database-like ops benchmark
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Python "programmers" when I show them how much faster their naive code runs when translated to C++ (this is a joke, I love python)
Bad examples. Both numpy and pandas are notoriously un-optimized packages, losing handily to pretty much all their competitors (R, Julia, kdb+, vaex, polars). See https://h2oai.github.io/db-benchmark/ for a partial comparison.
What are some alternatives?
price-transparency-guide - The technical implementation guide for the tri-departmental price transparency rule.
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
bfc - An industrial-grade brainfuck compiler
datafusion - Apache DataFusion SQL Query Engine
php-tensorflow - PHP TensorFlow Binding
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
databend - ๐๐ฎ๐๐ฎ, ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ & ๐๐. Modern alternative to Snowflake. Cost-effective and simple for massive-scale analytics. https://databend.com
DataFramesMeta.jl - Metaprogramming tools for DataFrames
sktime - A unified framework for machine learning with time series
arrow2 - Transmute-free Rust library to work with the Arrow format
disk.frame - Fast Disk-Based Parallelized Data Manipulation Framework for Larger-than-RAM Data