Datamancer VS db-benchmark

I have been using Nim for all of my recent data munging and analysis. There's https://github.com/Vindaar/ggplotnim for plots (among others) and everything else has just been normal code. There's also https://github.com/SciNim/Datamancer if you need something more like tidyverse.

Check out Datamancer for your Pandas equivalent. If I recall correctly it does have the ability to read/write csv. If that doesn't suite you, there is a Python/Nim bridge called Nimpy. I do a lot of machine learning projects and have to use OpenCV and some other things from python because it doesn't exist yet. It's a pretty damn cool library.

worked on the roadmap https://github.com/nim-lang/Nim/pull/19388 (enable -d:nimPreviewFloatRoundtrip and -d:nimPreviewDotLikeOps) and found that an important_packages (datamancer) failed. So I made a PR (https://github.com/SciNim/Datamancer/pull/23). It is not a bug of nimPreviewFloatRoundtrip(It seems like a precision problem to me) so alternatively datamancer can be disabled transiently.

There seems to be two major ones for Nim, NimData and Datamancer. Which one is better?

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/

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/

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"

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.

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

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.

If interested in benchmarks comparing different dataframe implementations, here is one:

https://h2oai.github.io/db-benchmark/

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.