extendr
db-benchmark
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extendr | db-benchmark | |
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2 | 91 | |
388 | 320 | |
2.8% | 2.2% | |
8.2 | 0.0 | |
3 days ago | 9 months ago | |
Rust | R | |
MIT License | Mozilla Public License 2.0 |
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extendr
db-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.
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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 v2.0 Released
If interested in benchmarks comparing different dataframe implementations, here is one:
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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.
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Best alternative to Pandas 2023?
And what's your rating scale? Objectively, pandas loses in performance against everything relevant. It has a wonky syntax that requires using lambda all over the place or to retype your df name at least twice for many operations.
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Tutorial on Intro to Rust Programming
There has been an upward trend in opensource tools written in Rust with interfaces to python eg: pydantic (moved to Rust in the recent release), polars which is very fast as indicated in the H2Oai benchmarks.
- How do I work with GIGANTIC csv files (20-100 gigabytes)?
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
arrow-datafusion - Apache Arrow DataFusion SQL Query Engine
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
databend - 𝗗𝗮𝘁𝗮, 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜. 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
datatable - A Python package for manipulating 2-dimensional tabular data structures
DataFrame - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
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 🚀
PackageCompiler.jl - Compile your Julia Package