siuba
dtplyr
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siuba | dtplyr | |
---|---|---|
25 | 24 | |
1,100 | 654 | |
- | -0.2% | |
7.5 | 7.5 | |
7 months ago | 2 months ago | |
Python | R | |
MIT License | GNU General Public License v3.0 or later |
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siuba
- The Design Philosophy of Great Tables (Software Package)
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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?
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Happy Halloween, Pandas! 🎃🤓
You mean siuba?
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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?
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R / Tidyverse User -> Python | How to Make it Hurt Less
Check out siuba
dtplyr
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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.
What are some alternatives?
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
tidytable - Tidy interface to 'data.table'
dtale - Visualizer for pandas data structures
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
Altair - Declarative statistical visualization library for Python
tidypolars - Tidy interface to polars
q - q - Run SQL directly on delimited files and multi-file sqlite databases
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 🚀
vinum - Vinum is a SQL processor for Python, designed for data analysis workflows and in-memory analytics.
Datamancer - A dataframe library with a dplyr like API
DataFramesMeta.jl - Metaprogramming tools for DataFrames
explorer - Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir