Julia-DataFrames-Tutorial VS IndexedTables.jl

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.

Take a look at the these notebooks: https://github.com/bkamins/Julia-DataFrames-Tutorial

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

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