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 🚀 (by vaexio)
data.table
R's data.table package extends data.frame: (by Rdatatable)
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| vaex | data.table | |
|---|---|---|
| 7 | 16 | |
| 8,171 | 3,476 | |
| 0.4% | 0.7% | |
| 6.0 | 9.4 | |
| 11 days ago | 3 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.
vaex
Posts with mentions or reviews of vaex.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-03.
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preprocessing millions of records - how to speed up the processing
Try vaex, vaex, using lazy evaluation and parallel calculations, you should be fine.
- Polars: Lightning-fast DataFrame library for Rust and Python
- I wrote one of the fastest DataFrame libraries
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.
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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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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.
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I wrote one of the fastest DataFrame libraries
data.table is basically a highly optimized C library
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How many of you who are employed as data Analysts/ Scientists use R vs Python vs other related software such as Power BI, Simio or Minitab?
Imo, the R syntax is way more readable and intuitive. Also R's ggplot is way more intuitive and easier to learn than remembering all the properties/methods with confusing names from matplotlib. Also dplyr works with data.table (the faster data processing library available for R, which is also incredibly faster than pandas).
What are some alternatives?
When comparing vaex and data.table you can also consider the following projects:
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
minimal-pandas-api-for-polars - pip install minimal-pandas-api-for-polars
rust-dataframe - A Rust DataFrame implementation, built on Apache Arrow
visidata - A terminal spreadsheet multitool for discovering and arranging data
umap - Uniform Manifold Approximation and Projection
db-benchmark - reproducible benchmark of database-like ops
TypedTables.jl - Simple, fast, column-based storage for data analysis in Julia
siuba - Python library for using dplyr like syntax with pandas and SQL
dtplyr - Data table backend for dplyr
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
minimal-pandas-api-for-pola