db-benchmark
plotnine
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db-benchmark | plotnine | |
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91 | 36 | |
319 | 3,823 | |
0.9% | - | |
0.0 | 9.6 | |
10 months ago | about 11 hours ago | |
R | Python | |
Mozilla Public License 2.0 | MIT License |
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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.
[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.
plotnine
- FLaNK AI Weekly 18 March 2024
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A look at the Mojo language for bioinformatics
To your last point, have you tried plotnine? It's meant to be ggplot2 for python.
https://github.com/has2k1/plotnine
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
plotnine - A grammar of graphics for Python based on ggplot2.
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/has2k1/plotnine
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Lets-Plot: An open-source plotting library by JetBrains
This seems quite similar to plotnine [0], which also provides a grammar of graphics interface for Python. That said, I love ggplot and I can't wait to use this in my research! I hope we can port/re-implement ggthemes, scientificplots [1], and other ggplot libraries for lets-plot.
0: https://plotnine.readthedocs.io/en/stable/
1: https://github.com/garrettj403/SciencePlots
- When would you use R instead of Python?
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[P] Easily make complex plots using ChatGPT [open source]
There is [plotnine](https://plotnine.readthedocs.io/en/stable/) which tries to implement ggplot in Python.
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Is R or Python an EASIER option for non-CS/SE grads?
You could use plotnine if you like the grammar of graphics concept: https://plotnine.readthedocs.io/en/stable/
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Every modeler is supposed to be a great Python programmer
> Python doesn’t yet have anything remotely close to ggplot for rapidly making exploratory graphics, for example.
Plug for plotnine (https://plotnine.readthedocs.io/en/stable/). I don't know R but use ggplot indirectly through this library for exploratory data analysis, and comparing the experience to any other python plotting library, I understand why R folks are usually so sad to be using Python.
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Why has nobody ported ggplot to Python?
They have, https://plotnine.readthedocs.io/en/stable/
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
seaborn - Statistical data visualization in Python
arrow-datafusion - Apache DataFusion SQL Query Engine
matplotlib - matplotlib: plotting with Python
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
Altair - Declarative statistical visualization library for Python
databend - 𝗗𝗮𝘁𝗮, 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜. Modern alternative to Snowflake. Cost-effective and simple for massive-scale analytics. https://databend.com
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
DataFramesMeta.jl - Metaprogramming tools for DataFrames
ggplot - ggplot port for python
sktime - A unified framework for machine learning with time series
bokeh - Interactive Data Visualization in the browser, from Python