IndexedTables.jl
smlsharp
| IndexedTables.jl | smlsharp | |
|---|---|---|
| 2 | 2 | |
| 119 | 211 | |
| 1.7% | 0.0% | |
| 5.9 | 3.7 | |
| about 1 month ago | 7 months ago | |
| Julia | Standard ML | |
| 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
smlsharp
- SML – a new language in the ML family
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Table Oriented Programming (2002)
Another relevant software category is the statistical analysis languages, including SAS, Stata and SPSS.
Old-school SAS included only two data types (floats and character strings), but allowed for SQL and sequential data-steps to live together. Persistence was baked in. The floats could be used to represent dates, datetimes and other formats. I particularly appreciated being able to use macros to define a data-step view to split the follow-up for an individual from a table. Such a view could then be collapsed using SQL. SAS also allows for using variables. More recently, R tools such as dplyr have brought together data-frames and relational operations. However, I miss the sequential coding in SAS, using macros as higher-level tools to define the logic, including corner cases.
For strictly typed records, I have always wanted to spend more time with SML# [0] this allows for record updating, with close ties to SQL -- an under-appreciated version of SML.
[0] https://github.com/smlsharp/smlsharp
What are some alternatives?
Julia-DataFrames-Tutorial - A tutorial on Julia DataFrames package
empirical-lang - A language for time-series analysis
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
julia - The Julia Programming Language