awesome-pandas-alternatives
Awesome list of alternative dataframe libraries in Python. (by baggiponte)
data.table
R's data.table package extends data.frame: (by Rdatatable)
| awesome-pandas-alternatives | data.table | |
|---|---|---|
| 1 | 16 | |
| 29 | 3,489 | |
| - | 0.6% | |
| 10.0 | 9.6 | |
| over 1 year ago | about 13 hours ago | |
| R | ||
| MIT License | Mozilla Public License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
awesome-pandas-alternatives
Posts with mentions or reviews of awesome-pandas-alternatives.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-12-21.
-
Alternative to Pandas
I am maintaining a small list of [pandas alternatives](https://github.com/baggiponte/awesome-pandas-alternatives) on github, but I guess that for your usecase pandas would be the perfect match.
data.table
Posts with mentions or reviews of data.table.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-12-21.
- Data.table: R's data.table package extends data.frame
-
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.
-
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?
When comparing awesome-pandas-alternatives and data.table you can also consider the following projects:
awesome-polars - A curated list of Polars talks, tools, examples & articles. Contributions welcome !
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 🚀
fastexcel - A Python wrapper around calamine
rust-dataframe - A Rust DataFrame implementation, built on Apache Arrow
datatable - A Python package for manipulating 2-dimensional tabular data structures
siuba - Python library for using dplyr like syntax with pandas and SQL
TypedTables.jl - Simple, fast, column-based storage for data analysis in Julia
gsir-te - Getting Started in R -- Tinyverse Edition
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.
db-benchmark - reproducible benchmark of database-like ops
awesome-pandas-alternatives vs awesome-polars
data.table vs vaex
awesome-pandas-alternatives vs fastexcel
data.table vs rust-dataframe
awesome-pandas-alternatives vs datatable
data.table vs siuba
data.table vs TypedTables.jl
data.table vs gsir-te
data.table vs ballista
data.table vs db-benchmark
data.table vs datatable