dtplyr
Data table backend for dplyr (by tidyverse)
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)
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| dtplyr | vaex | |
|---|---|---|
| 24 | 7 | |
| 654 | 8,171 | |
| -0.2% | 0.4% | |
| 7.5 | 5.4 | |
| 2 months ago | 24 days ago | |
| R | Python | |
| GNU General Public License v3.0 or later | MIT License |
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.
dtplyr
Posts with mentions or reviews of dtplyr.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-09.
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Tidyverse 2.0.0
Can’t say I’ve used it, but isn’t that what dtplyr is supposed to provide?
https://dtplyr.tidyverse.org/
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Error when trying to use dtplyr::lazy_dt, "invalid argument to unary operator"
# I am trying to follow the example at https://dtplyr.tidyverse.org/
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Millions of rows
FYI the developer of tidytable has been developing dtplyr for the Tidyverse. You might like that too!
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fuzzyjoin - "Error in which(m) : argument to 'which' is not logical"
If you need speed, you should consider using dtplyr (or tidytable), or even dbplyr with duckdb.
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Best alternative to Pandas 2023?
https://dtplyr.tidyverse.org/ ?
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R Dialects Broke Me
If you want data.table speed, but using dplyr/tidy then dtplyr is a good package to have handy. Personally I love R, and choose R + NodeJS as my gotos for everything I do, and use Python only when I have to.
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Merging csv from environment.
Also, that dataset is quite big, and the "base" Tidyverse will be excessively slow. You should supplement the "base" Tidyverse packages (i.e. dplyr and tidyr) with either dtplyr or dbplyr (+ duckDB). I'd suggest starting with dtplyr, which should handle 10M+ rows fine.
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mutate ( ) function is only working in code chunk I run it in. It does not change the column in my data frame other than in that one code chunk.
If you want, there's a "substitute" for dplyr called dtplyr (also part of the Tidyverse), which "translates" your dplyr/tidyr code into data.table behind the scenes, and allows you to make your modifications apply directly to the original dataset by default:
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R process taking over 2 hours to run suddenly
Install the dtplyr package and change your code to:
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DS student here: why use R over Python?
Get the best of both worlds (tidyverse + data.tables) with dtplyr, a data.table backend for dplyr.
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.
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High performance (for the consumer) time series storage?
I'd recommend QuestDB. Worked with it multiple times for different algorithmic trading needs and it didn't disappoint. If you want to load data fast, I'd recommend this Python library.
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Python Pandas vs Dask for csv file reading
How about vaex?
- Polars: Lightning-fast DataFrame library for Rust and Python
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For stocks, what historical data do you store and how do you store it?
You might find vaex (https://github.com/vaexio/vaex) interesting if you work with HDF5.
- I wrote one of the fastest DataFrame libraries
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A Hybrid Apache Arrow/Numpy DataFrame with Vaex Version 4.0
My guess is that should be possible, feel free to hop onto https://github.com/vaexio/vaex/discussions !
What are some alternatives?
When comparing dtplyr and vaex you can also consider the following projects:
tidytable - Tidy interface to 'data.table'
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
data.table - R's data.table package extends data.frame:
tidypolars - Tidy interface to polars
minimal-pandas-api-for-polars - pip install minimal-pandas-api-for-polars
Datamancer - A dataframe library with a dplyr like API
rust-dataframe - A Rust DataFrame implementation, built on Apache Arrow
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
visidata - A terminal spreadsheet multitool for discovering and arranging data
dataiter - Python classes for data manipulation
umap - Uniform Manifold Approximation and Projection