Tool of Thought

APL for the Practical Man

⎕VFI with ⎕CSV

January 25, 2022

A long-standing problem with ⎕VFI is that it does not accept the traditional minus sign (only an APL high-minus), nevermind thousand separators, a plus sign in front of E notation, and a million other things that Excel users might produce in a CSV file headed your way. It takes a lot of preprocessing to take outside data and run it through ⎕VFI.

The relatively new sytem function ⎕CSV handles all the specific cases noted above when converting columns to numeric. Furthermore it works on in-memory data, so we don't need a file. This makes it trivial to write an implementation of ⎕VFI using ⎕CSV:

     ⍝ ⎕VFI using ⎕CSV
     ⍝ ⍵ ←→ Char Mat
     ⍝ ← ←→ Num vector
     o←⊂'Separator' '⎕'
     o,←⊂'Thousands' ','
     r←,(⎕CSV⍠o)(,⍵,⎕UCS 13)'S' 3


  1. The argument here is a char mat, not a delimited vector like ⎕VFI.
  2. The separator is arbitrary, and should not exist in the argument.
  1. There is currently a bug in ⎕CSV where it throws an error on a number

(UPDATE: This is is now fixed in version 18.2.45319.0)

  1. We should probably test there are no line endings in the argument.

This is almost as fast as ⎕VFI with no preprocessing, and much faster than a solution with ⎕VFI that requires preprocessing for negative signs and thousand separators.

However, there are still things we might want to preprocess for, like parentheses for a negative number, leading currency characters, and trailing percentage signs. Here is a first attempt that handles all three cases which no doubt can be improved:

     ⍝ ⍵ ←→ Char mat
     ⍝   ←→ Modified char mat
     ⎕IO ⎕ML←0 1
     m[⍸m[;0]='$';0]←' '
     ljust←{⍵⌽⍨+/∧\' '=⍵}
     m[⍸m[;0]='%';0]←' '
     m[n;0]←' '

Note that m[;]← is used rather than m← because the former does the assignment in place, while the latter makes a copy.

When processing CSV files, it is useful to apply ⎕CSV first with no numeric conversion bringing everything into the workspace as char columns, and then applying vfi to the columns we want to convert to numeric. Not only does this technique allow us to apply our own custom preprocess function, but it provide the ability to analyze the before-and-after conversion.