apldyalog

Best way to import and handle non-rectangular (ragged) data

I am studying APL and I am facing this problem: in real life cases, it is common to deal with non-rectangular datasets (imagine importing data from a SQL database where the "count(*) ... group by ..." gives different counts for different groups). I have setup an example where I import a time series of currencies exchange rates (both with ⎕CSV and ⎕JSON) and I end up with a table with lot of rows and three columns, the ISO code, the reference date and the rate. I find this table is useless in many cases, for instance if one wants to calculate daily return rates. I have manage to transform this table into a two entries table with Iso Codes in rows and dates in columns. I'd like to know if there are better ways to achieve this (or better) result.

(d h)←⎕csv 'dayliesjan.csv' '' (0 1 0 4 0 1)  1 ⍝ remove header row from data

This creates table d with rows that look like this:

├───┼──────────┼──────────┤
│FJD│2.451     │2024-01-10│
├───┼──────────┼──────────┤
│FJD│2.4514    │2024-01-29│
├───┼──────────┼──────────┤
│FJD│2.4523    │2024-01-12│
├───┼──────────┼──────────┤

Then

dates←∪d[;3]            ⍝ create a vector of unique dates
isos←∪d[;1]             ⍝ create a vector of unique isos
hdates←(⊂'---'),dates   ⍝ prepare a header for the two entries table

PRint the two entries table

hdates⍪isos,(≢isos) (≢dates) ⍴ d[{⊃⍵}⌸d[;1 3];2]

┌───┬──────────┬──────────┬──────────┬──────────┬──────────┬──────────┬──────────┬
│---│2024-01-02│2024-01-03│2024-01-04│2024-01-05│2024-01-08│2024-01-09│2024-01-10│
├───┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼
│AFN│76.7668   │76.7351   │77.1061   │76.8808   │77.2931   │77.1918   │77.3973   │
├───┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼
│ALL│103.17    │103.2     │103.88    │104.3     │104.47    │104.29    │104.28    │
├───┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼
│DZD│147.147   │146.9074  │147.3182  │146.7768  │147.0278  │146.9367  │147.0292  │
├───┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼
│AOA│917.42    │914.384   │917.186   │914.427   │916.706   │916.444   │917.425   │
├───┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼
│XCD│2.9581    │2.9481    │2.9573    │2.9487    │2.9554    │2.9538    │2.9554    │
├───┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼
│ARS│886.1662  │885.2383  │888.5621  │886.6029  │889.3809  │890.4455  │891.496   │
├───┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼
│AMD│443.13    │441.54    │443.1     │441.75    │442.66    │442.4     │443.14    │
├───┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼
│AWG│1.9611    │1.9545    │1.9606    │1.9549    │1.9593    │1.9583    │1.9593    │
├───┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼──────────┼

On such a table I could easily (so I think) calculate daily return rates.

Does this make sense? Is there a better way?

Solution

  • Really, all you want is the exchange rate for all combinations of isos and dates. This calls for an outer product:

          isos∘.{⍺ ⍵}dates
┌────────────────┬────────────────┬────────────────┬────────────────┬─────────────
│┌───┬──────────┐│┌───┬──────────┐│┌───┬──────────┐│┌───┬──────────┐│┌───┬────────
││AFN│2024-01-02│││AFN│2024-01-03│││AFN│2024-01-04│││AFN│2024-01-05│││AFN│2024-01-
│└───┴──────────┘│└───┴──────────┘│└───┴──────────┘│└───┴──────────┘│└───┴────────
├────────────────┼────────────────┼────────────────┼────────────────┼─────────────
│┌───┬──────────┐│┌───┬──────────┐│┌───┬──────────┐│┌───┬──────────┐│┌───┬────────
││ALL│2024-01-02│││ALL│2024-01-03│││ALL│2024-01-04│││ALL│2024-01-05│││ALL│2024-01-
│└───┴──────────┘│└───┴──────────┘│└───┴──────────┘│└───┴──────────┘│└───┴────────
├────────────────┼────────────────┼────────────────┼────────────────┼─────────────
│┌───┬──────────┐│┌───┬──────────┐│┌───┬──────────┐│┌───┬──────────┐│┌───┬────────
││DZD│2024-01-02│││DZD│2024-01-03│││DZD│2024-01-04│││DZD│2024-01-05│││DZD│2024-01-
│└───┴──────────┘│└───┴──────────┘│└───┴──────────┘│└───┴──────────┘│└───┴────────
├────────────────┼────────────────┼────────────────┼────────────────┼─────────────

    For each combination, we want the row number in d:

          isos∘.{d[;1 3]⍳⍺ ⍵}dates
 1  2  3  4  5  6  7
 8  9 10 11 12 13 14
15 16 17 18 19 20 21
22 23 24 25 26 27 28
29 30 31 32 33 34 35
36 37 38 39 40 41 42
43 44 45 46 47 48 49
50 51 52 53 54 55 56

    Now it is trivial to get the rate:

          isos∘.{d[d[;1 3]⍳⍺ ⍵;2]}dates
 76.7668  76.7351  77.1061  76.8808  77.2931  77.1918  77.3973
103.17   103.2    103.88   104.3    104.47   104.29   104.28  
147.147  146.9074 147.3182 146.7768 147.0278 146.9367 147.0292
917.42   914.384  917.186  914.427  916.706  916.444  917.425 
  2.9581   2.9481   2.9573   2.9487   2.9554   2.9538   2.9554
886.1662 885.2383 888.5621 886.6029 889.3809 890.4455 891.496 
443.13   441.54   443.1    441.75   442.66   442.4    443.14  
  1.9611   1.9545   1.9606   1.9549   1.9593   1.9583   1.9593

    From here, you can add the headers if you want, but that'll just make usage of the table harder.

    You can maybe make the code easier to follow through naming:

          isos_dates←d[;1 3]
      rates←d[;2]
      isos∘.{rates[isos_dates⍳⍺ ⍵]}dates
 76.7668  76.7351  77.1061  76.8808  77.2931  77.1918  77.3973

    Or even creating a custom lookup function:

          RateOn←{rates[isos_dates⍳⍺ ⍵]}
      isos ∘.RateOn dates