price-transparency-guide
db-benchmark
price-transparency-guide | db-benchmark | |
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14 | 91 | |
341 | 320 | |
1.5% | 0.0% | |
5.9 | 0.0 | |
20 days ago | 11 months ago | |
Ruby | R | |
- | Mozilla Public License 2.0 |
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price-transparency-guide
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Are there any websites making the "Transparency in coverage" data available?
The problem is that its all released in files in JSON code. The file from the above link has links to 2374 other JSON files that are compressed into multipart JSON.GZ files and some of these are 25 gigabytes and larger. Literally terabytes of data.
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Analyzing multi-gigabyte JSON files locally
Neither of these are implemented via HL7 or FHIR. CMS has defined a new "machine readable format" to implement the regulation: https://github.com/CMSgov/price-transparency-guide
- Dataset needed!!!!! Well I've worked on some datasets that I took from Kaggle. No satisfaction. I request you to provide an excellent Dataset to perform Exploratory Data Analysis.
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Help hosting trillions of rows of new health insurance public price data
CMS was very particular in the format they required payers to use. You can even check out the spec yourself on GitHub. Unfortunately their requirements don’t make a lot of sense from a data engineering standpoint.
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Curious if this community saw this - [OC] The ridiculously absurd amount of pricing data that insurance companies just publicly dumped
You can review the provided example schemas here: https://github.com/CMSgov/price-transparency-guide/tree/master/schemas
- [OC] The ridiculously absurd amount of pricing data that insurance companies just publicly dumped
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I analyzed 1835 hospital price lists so you didn't have to
The health insurance companies are now required to publish this and in fact the rule went into effect July 1 2022.
Look up price transparency by CMS (the data will be published in this format: https://github.com/CMSgov/price-transparency-guide)
Note: the data being published by payors in machine readable format (MRF) is MASSIVE - terrabytes of data. Example: https://transparency-in-coverage.uhc.com/
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How Much Health Insurers Pay for Almost Everything Is About to Go Public
This may be useful in figuring out how to parse the contents.
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Hospitals and Insurers Didn’t Want You to See These Prices. Here’s Why
FYI, there is a similar effort going on that requires all health plans to host machine readable files containing negotiated rates by provider and procedure code by 2022-01-01: https://github.com/CMSgov/price-transparency-guide
- Instructions around the usage of meta robot tags and robots.txt files
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 – 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 it’s around an order of magnitude faster than pandas and dplyr (there’s a nice benchmark here). It’s 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.