siuba
Python library for using dplyr like syntax with pandas and SQL (by machow)
data.table
R's data.table package extends data.frame: (by Rdatatable)
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| siuba | data.table | |
|---|---|---|
| 25 | 16 | |
| 1,100 | 3,478 | |
| - | 0.8% | |
| 7.5 | 9.6 | |
| 7 months ago | 2 days ago | |
| Python | 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.
siuba
Posts with mentions or reviews of siuba.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-04.
- The Design Philosophy of Great Tables (Software Package)
-
Best alternative to Pandas 2023?
I don't know what's best for you, but I can recommend Siuba, a tidy interface for Python to send queries to pandas and SQL-db.
- Method Chaining in Pandas: Bad Form or a Recipe for Success?
-
Happy Halloween, Pandas! 🎃🤓
You mean siuba?
-
Explorer (Elixir and Polars)
For further inspiration, this is a pretty good-looking "dplyr for Python": https://github.com/machow/siuba
- Unpopular opinion: Matplotlib is a bad library
- A trick to have arbitrary infix operators in Python
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Going from R to Pandas: dplython vs dfply vs plydata
You should follow /u/the75th's advice. However, if you decide to buck that take, I'd look into siuba. I've never heard of those packages you've listed, and have doubts they'd be maintained.
- Tidyverse equivalent in Python?
-
R / Tidyverse User -> Python | How to Make it Hurt Less
Check out siuba
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
-
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.
-
Do python packages have long form documentation? If so can someone provide me a sample?
data.table README.md
-
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.
-
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.
-
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 siuba and data.table you can also consider the following projects:
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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 🚀
dtale - Visualizer for pandas data structures
rust-dataframe - A Rust DataFrame implementation, built on Apache Arrow
Altair - Declarative statistical visualization library for Python
TypedTables.jl - Simple, fast, column-based storage for data analysis in Julia
q - q - Run SQL directly on delimited files and multi-file sqlite databases
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.
DataFramesMeta.jl - Metaprogramming tools for DataFrames
db-benchmark - reproducible benchmark of database-like ops