db-benchmark
DataFramesMeta.jl
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db-benchmark | DataFramesMeta.jl | |
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91 | 4 | |
319 | 470 | |
0.9% | 2.8% | |
0.0 | 6.9 | |
10 months ago | 21 days ago | |
R | Julia | |
Mozilla Public License 2.0 | GNU General Public License v3.0 or later |
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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.
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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:
- 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.
DataFramesMeta.jl
- Pandas vs. Julia – cheat sheet and comparison
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Why not Julia?
A package: https://github.com/JuliaData/DataFramesMeta.jl
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Is there tidyverse/dplyr for Julia?
I'd also heartily recommend DataFramesMeta which provides really nice macros for manipulating dataframes.
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[S] Among R, Python, SQL, and SAS, which language(s) do you prefer to perform data manipulation and merge datasets?
I do get the feeling though that Python people are considered “more sophisticated” as programmers than R. But I think Julia is gaining traction now and it can handle general programming tasks better than R can, while still remaining pretty similar so its worth learning too. It has DataFramesMeta.jl: https://github.com/JuliaData/DataFramesMeta.jl. Works like dplyr.
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
DataFrames.jl - In-memory tabular data in Julia
arrow-datafusion - Apache DataFusion SQL Query Engine
siuba - Python library for using dplyr like syntax with pandas and SQL
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
TwoBasedIndexing.jl - Two-based indexing
databend - 𝗗𝗮𝘁𝗮, 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜. Modern alternative to Snowflake. Cost-effective and simple for massive-scale analytics. https://databend.com
DaemonMode.jl - Client-Daemon workflow to run faster scripts in Julia
sktime - A unified framework for machine learning with time series
FromFile.jl - Julia enhancement proposal (Julep) for implicit per file module in Julia
arrow2 - Transmute-free Rust library to work with the Arrow format
HTTP.jl - HTTP for Julia