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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.
Rust and what it needs to gain space in computation-oriented applications
7 projects | reddit.com/r/rust | 24 Nov 2021
You should check out polars, datafusion, influxdb iox and databend, all written in native Rust and powered by the Apache Arrow format. Polars in particular is pretty dam fast and has bindings for Python.
Database-Like Ops Benchmark
1 project | news.ycombinator.com | 20 Nov 2021
A better dtypes for pandas dataframes pulled from Postgres
1 project | reddit.com/r/datascience | 14 Nov 2021
Here is a good comparison: https://h2oai.github.io/db-benchmark/
Introducing tidypolars - a Python data frame package with syntax familiar to R tidyverse users
4 projects | reddit.com/r/datascience | 10 Nov 2021
The biggest difference with this one is that it's built on top of the polars package, which is probably the fastest data frame manipulation library out there. All of the other dplyr-to-python packages are build on top of pandas (which is very slow in comparison).
Introducing tidypolars - a Python data frame package for R tidyverse users
9 projects | reddit.com/r/rstats | 10 Nov 2021
I think having a basic understanding of pandas, given how broadly it's used, is beneficial. That being said, polars seems to be matching or beating data.table in performance, so I think it'd be very worth it to take it up. Wes McKinney, creator of pandas, has been quite vocal about architecture flaws of pandas -- which is why he's been working on the Arrow project. polars is based on Arrow, so in principle it's kinda like pandas 2.0 (adopting the changes that Wes proposed).9 projects | reddit.com/r/rstats | 10 Nov 2021
tidypolars uses the polars package as a backend, which might be the fastest data frame manipulation library out there. (Faster even than R's data.table, which has been the king of speed for many years.)
Your perfect program/language for experience studies?
1 project | reddit.com/r/actuary | 4 Nov 2021
Julia has ExperienceStudies.jl to help with exposure calculations and MortalityTables.jl for mortality rate data. It also performs very well in data science benchmarks: https://h2oai.github.io/db-benchmark/
Comparing SQLite, DuckDB and Arrow
5 projects | news.ycombinator.com | 27 Oct 2021
this benchmark is more comprehensive for this type of analytical work:
1 project | reddit.com/r/datascience | 23 Oct 2021
Data too big to work with memory you can do in R too, using SparkR. I agree the documentation to something like PySpark is better though. For data within memory, data.table in R beats pandas. Loses to Polars (implemented in Rust that has bindings in Python) but that is not in use much as its new: https://github.com/h2oai/db-benchmark.
Turning database into a searchable dashboard?
3 projects | reddit.com/r/datascience | 21 Oct 2021
Scikit-Learn Version 1.0
11 projects | news.ycombinator.com | 14 Sep 2021
> For me I had with pandas the most issues using it's multiindex.
Yessss. I loathe indices, and have never been in a situation where I was better off with them than without them.
> Regarding fast you have something like Vaex on python sid
I've never used Vaex, but I've used datatable (https://github.com/h2oai/datatable) and polars (https://github.com/pola-rs/polars). Polars is my favorite API, but datatable was faster at reading data (Polars was faster in execution). I'll have to give Vaex a try at some point.
Show HN: Sheet2dict – simple Python XLSX/CSV reader/to dictionary converter
5 projects | news.ycombinator.com | 21 Apr 2021
Hey Reddit, here's my comprehensive course on Python Pandas, for free.
1 project | reddit.com/r/Python | 1 Feb 2021
Yep. I think this is the downside to a package being entirely maintained by volunteers. In any case, Pandas is still the leading data wrangling package for Python. (I'm excited to see how datatable evolves.)
Ditching Excel for Python in a Legacy Industry (Reinsurance)
3 projects | news.ycombinator.com | 30 Dec 2020
h2o's data.table clone is fine
What are some alternatives?
arrow-datafusion - Apache Arrow DataFusion and Ballista query engines
polars - Fast multi-threaded DataFrame library in Rust and Python
DataFramesMeta.jl - Metaprogramming tools for DataFrames
DataFrame - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types, continuous memory storage, and no pointers are involved
sktime - A unified framework for machine learning with time series
Preql - An interpreted relational query language that compiles to SQL.
csvs-to-sqlite - Convert CSV files into a SQLite database
pyodide - Python with the scientific stack, compiled to WebAssembly.
vinum - Vinum is a SQL processor for Python, designed for data analysis workflows and in-memory analytics.
tidytable - Tidy interface to 'data.table'
PackageCompiler.jl - Compile your Julia Package