Dash.jl
Chain.jl
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Dash.jl | Chain.jl | |
---|---|---|
5 | 8 | |
479 | 346 | |
0.0% | - | |
7.3 | 4.2 | |
22 days ago | 2 months ago | |
Julia | Julia | |
MIT License | MIT License |
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.
Dash.jl
- Dash.jl – Julia interface to the Dash ecosystem
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Python is COOL
If you have to move to another language maybe check out julia, it looks similar to python to some extent, I hear it has very nice support for CUDA and for the web interface part there's Dash which should be familiar I guess.
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A dashboard like Plotly Dash, but for a Julia?
Besides Plotly Dash itself (which is mainly a JavaScript library with both Python and Julia bindings) there are the following Julia alternatives:
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building web apps in Julia
Depends on what you’re doing, but I’ve been using Dash to great success for my purposes at my company. The documentation kinda overlaps/misses some edge cases with the Python/JavaScript versions, but the framework is mostly analogous
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Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?
So can Julia:
https://github.com/plotly/Dash.jl
Chain.jl
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Pains of Julia compared to python
The [Chain.jl package](https://github.com/jkrumbiegel/Chain.jl) is becoming idiomatic for these kind of pipelines.
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Transition from R Tidyverse to Julia (VS Code)
If you do have tabular data in a dataframe you have a few options for data manipulation, the most popular packages are probably DataFramesMeta and Query, although in my opinion the best way to manipulate dataframes is with the functions built in to DataFrames.jl and using a package like Chain.jl or Pipe.jl to pipe the functions into each other like magrittr in R.
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The (updated) history of the pipe operator in R
The Julia community built a better piping method than any other language has AFAIK: Chain.jl.
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What are some of your favourite macros?
@chain and @match.
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Why is piping so well-accepted in the R community compared to those in Julia and Python?
Have you ever tried Infiltrator.jl and Chain.jl?
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https://np.reddit.com/r/Julia/comments/nnu6if/julia_object_oriented_programming_with_dot/h0anaru/
You are right. However, sometimes well used is very useful, and readable. One suggestion, in Julia I suggest Chain.jl, because it allows intercalate easily the output for debugging:
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Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?
I also like pipe syntax and I've found there is nice support for it in Julia. There are some nice packages to improve it over base [1].
Have you checked queryverse [2]?
[1] https://github.com/jkrumbiegel/Chain.jl
What are some alternatives?
Genie.jl - 🧞The highly productive Julia web framework
Pipe.jl - An enhancement to julia piping syntax
Pluto.jl - 🎈 Simple reactive notebooks for Julia
Plotly.jl - A Julia interface to the plot.ly plotting library and cloud services
Revise.jl - Automatically update function definitions in a running Julia session
PackageCompiler.jl - Compile your Julia Package
JLD2.jl - HDF5-compatible file format in pure Julia
PlutoSliderServer.jl - Web server to run just the `@bind` parts of a Pluto.jl notebook
PaddedViews.jl - Add virtual padding to the edges of an array
StatsPlots.jl - Statistical plotting recipes for Plots.jl
Infiltrator.jl - No-overhead breakpoints in Julia