Simple data science operations in Haskell

haskds.md

Simple data science operations in Haskell

Simple tasks

1. Read a table of data from disk

Cassava: exploratory CSV parsing

Frames: known, typed CSV parsing

Binary: more specific binary types ad hoc

2. Show a few rows of that table (HTML rendering)

Cassava: something like

import qualified Lucid as L
import qualified Data.CSV as C
render :: (C.DefaultOrdered a, C.ToNamedRecord a) -> [a] -> L.Html ()

Frames: probably something analogous for Vinyl records

3. Examine types and distributions of columns

statistics: mean, variance, etc

4. Map, filter, and project to a new table

5. Plot column values: histograms, lines, etc

Chart, Diagrams can render to PNG, SVG

6. Show images if data is visual

JuicyPixels can interpret array data as images

Haskell4Mac Playgrounds

hfm helps with a lot of this. Here are positives and negatives:

(+) Can render visual results in playground

(-) Horizontal presentation (term type result) is more difficult to read than vertical (term \n type \n result)

(-) Need to write results to file in addition to displaying in the playground (HTML or PDF export of playground would be nice)

(+) Has Lucid/Blaze HTML rendering

(-) Embedded views cannot be resized manually, making text flow weirdly for compressed tables

(+) Has image rendering w/ JuicyPixels

(-) Not straightforward to render charts. Pure-haskell diagrams backend is slow, are rasterrific or cairo better?