IndexedTables.jl
Chain.jl
IndexedTables.jl | Chain.jl | |
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2 | 8 | |
119 | 348 | |
1.7% | - | |
5.9 | 4.2 | |
about 1 month ago | 2 months ago | |
Julia | Julia | |
MIT License | MIT License |
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IndexedTables.jl
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Table Oriented Programming (2002)
unfortunately, I don't have access to that code anymore, I wrote a number of loaders for different data set types including CSV. The time series were all modeled as forward iterating stream of tuples, so there is no specific table abstraction. There is an implicit assumption that the stream is ordered by the join key, in a time series this being the timestamp, though nothing in the implementation enforced that.
Joins are always n-way merge joins, so you can write something like y = 2x^2 - 3z and fold that into a single streaming operation y = f( x, z ) where y, x and z are time streams.
When rendered to screen they looked very similar to your examples. With plugins in the IDE you could directly plot and array of time series as a chart.
Since the time I wrote NamedTuples the Julia core team folded the functionality into the core of Julia https://docs.julialang.org/en/v1/manual/types/#Named-Tuple-T.... This is the core of https://juliadb.org/ all credit to the Julia core team
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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?
I am talking about https://github.com/JuliaData/IndexedTables.jl. I’m just getting started with Julia so I might not really know what I’m doing right now lol
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?
Julia-DataFrames-Tutorial - A tutorial on Julia DataFrames package
Pipe.jl - An enhancement to julia piping syntax
julia - The Julia Programming Language
Genie.jl - 🧞The highly productive Julia web framework
empirical-lang - A language for time-series analysis
Revise.jl - Automatically update function definitions in a running Julia session
JLD2.jl - HDF5-compatible file format in pure Julia
PaddedViews.jl - Add virtual padding to the edges of an array
Infiltrator.jl - No-overhead breakpoints in Julia
RCall.jl - Call R from Julia
StatsPlots.jl - Statistical plotting recipes for Plots.jl
AlgebraOfGraphics.jl - Combine ingredients for a plot