|7 months ago||about 2 months ago|
|MIT License||Mozilla Public License 2.0|
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Fast Lane to Learning R
4 projects | news.ycombinator.com | 14 May 2022
I strongly recommend data.table R. Tidyverse is an improvement on base R, no question. Data.table has less intuitive syntax and can be harder to learn, but is lightning fast and memory efficient. If you're working with more than 1M rows, you should be using data.table.
Here are some benchmarks: https://h2oai.github.io/db-benchmark/
Friendlier SQL with DuckDB
8 projects | news.ycombinator.com | 12 May 2022
Hi, good to hear that you guys care about testing. One thing apart from the Github issues that led me to believe it might not be super stable yet was the benchmark results on https://h2oai.github.io/db-benchmark/ which make it look like it couldn't handle the 50GB case due to a out of memory error. I see that the benchmark and the used versions are about a year old so maybe things changed a lot since then. Can you chime in regarding the current story of running bigger DBs like 1TB on a machine with just 32GB or so RAM? Especially regardung data mutations and DDL queries. Thanks!
I used a new dataframe library (polars) to wrangle 300M prices and discover some of the most expensive hospitals in America. Code/notebook in article
2 projects | reddit.com/r/Python | 9 May 2022
Per these benchmarks it appears Polars is an order of magnitude more performant and it's lazy and Rust is just kinda sexy.
Benchmarking for loops vs apply and others
2 projects | reddit.com/r/rstats | 1 May 2022
This is a much more comprehensive set of benchmarks: https://h2oai.github.io/db-benchmark/
Why is R viewed badly over here? Also, as a college student, should I prioritize Python instead?
1 project | reddit.com/r/datascience | 29 Apr 2022
Its not like pandas is faster than tidyverse either on all the bechmarks, and data.table is faster than both. https://h2oai.github.io/db-benchmark/
Resources for data cleaning
2 projects | reddit.com/r/rstats | 13 Apr 2022
Language isn't really important here; what's important is tooling, and R definitely has the tooling. I would look at this benchmark reference for database-like operations, and you'll see that data.table (a very fast and memory-efficient R package) consistently ranks as one of the fastest tools out there that can also support a wide range of memory loads.
The fastest tool for querying large JSON files is written in Python (benchmark)
16 projects | news.ycombinator.com | 12 Apr 2022
Polars 0.20.0 release
2 projects | reddit.com/r/rust | 14 Mar 2022
How Easy It Is to Re-use Old Pandas Code in Spark 3.2
1 project | reddit.com/r/programming | 4 Feb 2022
It seems to me that the Spark model is much more sensible in terms of performance. In Spark, individual tasks are finally compiled into optimized Java code. As I understand Dusk works, a separate Python process is run for each data subset. So because of this architecture, Dusk is unlikely to ever get Spark performance. By the way, both systems build and optimize the operation graph. This is confirmed by benchmarks: https://h2oai.github.io/db-benchmark/
Count frequency of breed by animal ID (details in comments)
1 project | reddit.com/r/rstats | 3 Feb 2022
You can try to compare dplyr and data.table in this benchmark of different data sizes and operations: https://h2oai.github.io/db-benchmark/
What are some alternatives?
arrow-datafusion - Apache Arrow DataFusion and Ballista query engines
polars - Fast multi-threaded DataFrame library in Rust | Python | Node.js
databend - A modern Elasticity and Performance cloud data warehouse, activate your object storage for real-time analytics.
sktime - A unified framework for machine learning with time series
disk.frame - Fast Disk-Based Parallelized Data Manipulation Framework for Larger-than-RAM Data
DataFramesMeta.jl - Metaprogramming tools for DataFrames
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
arrow2 - Unofficial transmute-free Rust library to work with the Arrow format
DataFrame - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
julia - The Julia Programming Language
csvs-to-sqlite - Convert CSV files into a SQLite database
Preql - An interpreted relational query language that compiles to SQL.