datatable
data.table
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| datatable | data.table | |
|---|---|---|
| 9 | 16 | |
| 1,790 | 3,478 | |
| 0.8% | 0.8% | |
| 6.1 | 9.6 | |
| 5 months ago | 3 days ago | |
| C++ | R | |
| Mozilla Public License 2.0 | 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.
datatable
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Cheat Sheets for data.table to Python's pandas syntax?
Aside from that, there is a Python translation of data.table (see documentation here), which might be worth looking into. However, it hasn't had any major updates in a while: the last release 2 years ago ...
- Any advice on using Pandas as a data analyst?
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Alternative to Pandas
There's datatable. I haven't used it much, but the R version (data.table) is phenomenal.
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Need advice on whether to store data set for regression model in SQL database or by using Python modules like Pickle or Parquet
just use HDF5 or Parquet, or CSV + https://github.com/h2oai/datatable to speed up the file reading.
- Massive R analysis of Data Science Language and Job Trends 2022
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Scikit-Learn Version 1.0
> 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
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Hey Reddit, here's my comprehensive course on Python Pandas, for free.
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.)
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Ditching Excel for Python in a Legacy Industry (Reinsurance)
h2o's data.table clone is fine
https://github.com/h2oai/datatable
data.table
- Data.table: R's data.table package extends data.frame
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Discovering Copy-on-Write in R
The data.table package may also make a huge difference in performance, and often simplifies the code as well https://github.com/Rdatatable/data.table
- new governance being proposed for data.table
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Local development environment for the data.table R project
After the partial success with the development environment for R-yaml we tried another R package called data.table as part of the Open Source Development Course. Eventually we managed to run the tests of this too.
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Alternative to Pandas
There's datatable. I haven't used it much, but the R version (data.table) is phenomenal.
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Do python packages have long form documentation? If so can someone provide me a sample?
data.table README.md
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How to move “time” to a new column
That's an old bug in data.table v1.12.2. It's been fixed for a while now. If you update your data.table version (e.g., install.packages("data.table") ) and retry then it should work fine.
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Hiring an R coder to improve efficiency of code?
Some suggestions: (1) https://github.com/Rdatatable/data.table Code based on the data.table will probably be fastest. There are a number of reasons for this. More here: https://cran.r-project.org/web/packages/data.table/vignettes/ and here: https://rdatatable.gitlab.io/data.table/library/data.table/html/datatable-optimize.html The GForce set of optimizations is well explained here: https://www.brodieg.com/2019/02/24/a-strategy-for-faster-group-statisitics/ (2) setDTthreads() is your friend in data.table (3) I have found (on Windows at least) Microsoft Open R use of parallel MKL faster than CRAN's latest release. See https://mran.microsoft.com/documents/rro/multithread Microsoft recommends using setMKLthreads() if it will help. (4) I think rfast ( https://github.com/RfastOfficial/Rfast ) is a library worth considering although I don't know if it will help you with brms and stan operations.
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Piping in R is like baking!
Take a look at the 22nd new feature of v1.14.3 on development here.
- memory leak after data.table::fread()?
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
DataFrame - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
rust-dataframe - A Rust DataFrame implementation, built on Apache Arrow
db-benchmark - reproducible benchmark of database-like ops
siuba - Python library for using dplyr like syntax with pandas and SQL
scientific-visualization-book - An open access book on scientific visualization using python and matplotlib
TypedTables.jl - Simple, fast, column-based storage for data analysis in Julia
sktime - A unified framework for machine learning with time series
gsir-te - Getting Started in R -- Tinyverse Edition
vinum - Vinum is a SQL processor for Python, designed for data analysis workflows and in-memory analytics.
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.