Hi, I was looking for something simple (no macros), relying just on Nim's
standard libraries, to dynamically infer column type and assign it to the
proper seq type. I know that there are pretty advanced packages like
`datamancer` out there, but I wish to build my own small toy, just to better
understand the proper way to do it (more interested on the concept rather than
the performance). I know that Nim is strictly typed and that it encourages the
programmer to model its own types. I devised something like that:
import strutils, parsecsv, sequtils, streams
type
nimSeriesKind = enum
seriesInt,
seriesFloat,
seriesString
nimSeries = ref object
case kind: nimSeriesKind
of seriesInt: intValues: seq[int]
of seriesFloat: floatValues: seq[float]
of seriesString: stringValues: seq[string]
# CSV file parsing and matrix inversion omitted
# after that, I have a seq of string columns that
# I wish to check if they can be evaluated as int or float
proc setSeries(x: seq[string]): nimSeries =
try:
result = nimSeries(kind: seriesInt, intValues: x.map(parseInt))
except:
try:
result = nimSeries(kind: seriesFloat, floatValues: x.map(parseFloat))
except:
result = nimSeries(kind: seriesString, stringValues: x)
Run
Is the object variants the right way to do it? The try/except approach is
possibly a bit inelegant (any relevant drawbacks?), maybe there is a more
idiomatic / efficient way to go for? Thank you.
P.S. It's evident that if the last rows forces the type change (e.g. all
integers, but last row is NaN) it will determine complete sequences
re-mapping... but hopefully typical datasets do not conspire for the worst
case.
