Hierarchical or multi-index for data frames

Specific Domains Data
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Thank you for your responses.
The Lisp Curse essay has been eye-opening.
I always considered it positive for a programming language when library development was simple and similar to the application development. Now, I see that it makes consensus much harder.

I’m not very familiar with Pandas, but AFAIK you can achieve the same thing without multi-indexes using subsetting or split-apply-combine operations.

You certainly can achieve this in many different ways. If you store as follows, you can add and subtract columns, completely ignoring the symbols.

symbol | seqnum | open | close
AAPL   | 1      | 1    | 2
AAPL   | 2      | 3    | 4
MSFT   | 1      | 10   | 11
MSFT   | 2      | 12   | 13
....
df['close'].max() # would require some grouping first but should not be too cumbersome
df['range'] = df['close'] - df['open'] # should just do the right thing

Can’t really decide whether I find this “good enough” right now.

Is there an overview on what’s happening in this domain right now?
I’m aware of:

Interfaces that aim to translate between different implementions appear to be:

What’s the best way to find out how these implementations compare? Has someone written a meta-review of some sort?
Who are all the Julia_ companies behind these projects? JuliaComputing is the main driver behind Julia, right?
Are JuliaData and JuliaArrays different companies, subdivisions of JuliaComputing or “casual” groups of people that happen to be interested in this topic?

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