Julia-DataFrames-Tutorial
Tidier.jl
Julia-DataFrames-Tutorial | Tidier.jl | |
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2 | 5 | |
507 | 489 | |
- | 4.1% | |
0.0 | 8.5 | |
about 1 year ago | 7 days ago | |
Jupyter Notebook | Julia | |
MIT License | MIT License |
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Julia-DataFrames-Tutorial
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Pandas vs. Julia – cheat sheet and comparison
To be clear on this: DataFrames, like most of the Julia ecosystem, follows SemVer. DataFrames 1.0 was released over two years ago (March 2021), and the API has been stable ever since.
Furthermore, Bogumil Kaminski, one of the main developers behind DataFrames, makes sure that the DataFrames tutorials he has created here (https://github.com/bkamins/Julia-DataFrames-Tutorial) are updated on every new release.
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How do I access a specific column/row based on the column name and/or row value with an indexed table?
Take a look at the these notebooks: https://github.com/bkamins/Julia-DataFrames-Tutorial
Tidier.jl
- Tidier.jl: Meta-package for data analysis in Julia, modeled after R tidyverse
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Julia 1.10 Released
btw, there has been a pretty nice effort of reimplementing the tidyverse in julia with https://github.com/TidierOrg/Tidier.jl and it seems to be quite nice to work with, if you were missing that from R at least
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Pandas vs. Julia – cheat sheet and comparison
Indeed DataFrames.jl isn't and won't be the fastest way to do many things. It makes a lot of trade offs in performance for flexibility. The columns of the dataframe can be any indexable array, so while most examples use 64-bit floating point numbers, strings, and categorical arrays, the nice thing about DataFrames.jl is that using arbitrary precision floats, pointers to binaries, etc. are all fine inside of a DataFrame without any modification. This is compared to things like the Pandas allowed datatypes (https://pbpython.com/pandas_dtypes.html). I'm quite impressed by the DataFrames.jl developers given how they've kept it dynamic yet seem to have achieved pretty good performance. Most of it is smart use of function barriers to avoid the dynamism in the core algorithms. But from that knowledge it's very clear that systems should be able to exist that outperform it even with the same algorithms, in some cases just by tens of nanoseconds but in theory that bump is always there.
In the Julia world the one which optimizes to be fully non-dynamic is TypedTables (https://github.com/JuliaData/TypedTables.jl) where all column types are known at compile time, removing the dynamic dispatch overhead. But in Julia the minor performance gain of using TypedTables vs the major flexibility loss is the reason why you pretty much never hear about it. Probably not even worth mentioning but it's a fun tidbit.
> For what it's worth, data.table is my favourite to use and I believe it has the nicest ergonomics of the three I spoke about.
I would be interested to hear what about the ergonomics of data.table you find useful. if there are some ideas that would be helpful for DataFrames.jl to learn from data.table directly I'd be happy to share it with the devs. Generally when I hear about R people talk about tidyverse. Tidier (https://github.com/TidierOrg/Tidier.jl) is making some big strides in bringing a tidy syntax to Julia and I hear that it has had some rapid adoption and happy users, so there are some ongoing efforts to use the learnings of R API's but I'm not sure if someone is looking directly at the data.table parts.
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Tidyverse 2.0.0
“Tidier.jl is a 100% Julia implementation of the R tidyverse mini-language in Julia.”
https://github.com/TidierOrg/Tidier.jl
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What's Julia's biggest weakness?
A recent package, Tidier.jl, is coming from a R package developer: https://github.com/kdpsingh/Tidier.jl
What are some alternatives?
IndexedTables.jl - Flexible tables with ordered indices
tidytable - Tidy interface to 'data.table'
Zygote-Mutating-Arrays-WorkAround.jl - A tutorial on how to work around ‘Mutating arrays is not supported’ error while performing automatic differentiation (AD) using the Julia package Zygote.
py-shiny - Shiny for Python
ISLR.jl - JuliaLang version of "An Introduction to Statistical Learning: With Applications in R"
DataFramesMeta.jl - Metaprogramming tools for DataFrames
JuliaTutorials - Learn Julia via interactive tutorials!
julia - The Julia Programming Language
Julia-on-Colab - Notebook for running Julia on Google Colab
db-benchmark - reproducible benchmark of database-like ops
JuliaCall - Embed Julia in R
Genie.jl - 🧞The highly productive Julia web framework