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Universal table transformer combining univariate transformations dispatched on schema

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#49 3 comments 3 reactions 0 assignees View on GitHub

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
25/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
julia
Domain
data

Research direction

Start by reviewing the current collection of table transformers and the Tables.jl schema and mutability constraints described in the issue. Compare the function-based and compositional approaches, then define the user-facing dispatch behavior and confirm that all specified columns are transformed while unspecified columns remain unchanged.

Written by the indexing model from the issue text.

Description

It has been proposed on Slack that it be possible to have a single table transformer that transforms individual columns according to user-specified univariate transformations. This sounds like a good idea, which would also force some uniformity that's a little bit lacking in the current collection of table transformers.

  1. In the most general case I can imagine implementing, the univariate transformer that applies to a particular column is defined by a function that operates on both the name and scitype of the the column (as encoded in the table schema). This has the disadvantage that the user must specify a function with two arguments - or interact through some other complicated interface.

  2. The alternative would be a compositional approach. Each tabular transformer only carries out a single univariate transformer, applying to all specified names and scitypes (or "not"-names and "not"-scitypes, through ignore Boolean parameter), which would cover all conceivable use-cases. (columns not referred to are left alone). However, as we are currently locked into Tables.jl (which are non-mutable in general) we get a lot more copying of data.

Thoughts anyone?

Dominant language
Julia
Stars
1
Forks
2
Avg merge
9d 22h
Merged PRs (30d)
2

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