Raku Data::Summarizers
This Raku package has data summarizing functions for different data structures that are coercible to full arrays.
The supported data structures (so far) are:
- 1D Arrays
- 1D Lists
- Positional-of-hashes
- Positional-of-arrays
Usage examples
Setup
Here we load the Raku modules
Data::Generators,
Data::Reshapers
and this module,
Data::Summarizers:
use Data::Generators; use Data::Reshapers; use Data::Summarizers;
# (Any)
Summarize vectors
Here we generate a numerical vector, place some NaN's or Whatever's in it:
my @vec = [^1001].roll(12); @vec = @vec.append( [NaN, Whatever, Nil]); @vec .= pick(@vec.elems); @vec
# [740 311 434 300 (Whatever) 192 705 202 576 561 544 NaN (Any) 744 133]
Here we summarize the vector generated above:
records-summary(@vec)
# O────────────────────────────────────O
# │ numerical │
# O────────────────────────────────────O
# │ 1st-Qu => 251 │
# │ Max => 744 │
# │ Median => 489 │
# │ (Any-Nan-Nil-or-Whatever) => 3 │
# │ Mean => 453.5 │
# │ Min => 133 │
# │ 3rd-Qu => 640.5 │
# O────────────────────────────────────O
Summarize tabular datasets
Here we generate a random tabular dataset with 16 rows and 3 columns and display it:
srand(32); my $tbl = random-tabular-dataset(16, <Pet Ref Code>, generators=>[random-pet-name(4), -> $n { ((^20).rand xx $n).List }, random-string(6)]); to-pretty-table($tbl)
# O────────────────O───────────O──────────O
# │ Code │ Ref │ Pet │
# O────────────────O───────────O──────────O
# │ A2Ue69EWAMtJCi │ 0.050176 │ Guinness │
# │ KNwmt0QmoqABwR │ 0.731900 │ Truffle │
# │ A2Ue69EWAMtJCi │ 0.739763 │ Jumba │
# │ aY │ 7.342107 │ Guinness │
# │ xgZjtSP6VrKbH │ 19.868591 │ Jumba │
# │ 20CO9FGD │ 12.956172 │ Jumba │
# │ 20CO9FGD │ 15.854088 │ Guinness │
# │ A2Ue69EWAMtJCi │ 4.774780 │ Guinness │
# │ A2Ue69EWAMtJCi │ 18.729798 │ Guinness │
# │ xgZjtSP6VrKbH │ 13.383997 │ Guinness │
# │ aY │ 9.837488 │ Jumba │
# │ 20CO9FGD │ 2.912506 │ Truffle │
# │ xgZjtSP6VrKbH │ 11.782221 │ Truffle │
# │ KNwmt0QmoqABwR │ 9.825102 │ Truffle │
# │ xgZjtSP6VrKbH │ 16.277717 │ Jumba │
# │ CQmrQcQ4YkXvaD │ 1.740695 │ Guinness │
# O────────────────O───────────O──────────O
Remark: The values of the column "Pet" is sampled from a set of four pet names, and the values of the column and "Code" is sampled from a set of 6 strings.
Here we summarize the tabular dataset generated above:
records-summary($tbl)
# O───────────────O──────────────────────────────O─────────────────────O
# │ Pet │ Ref │ Code │
# O───────────────O──────────────────────────────O─────────────────────O
# │ Guinness => 7 │ Min => 0.0501758995572299 │ xgZjtSP6VrKbH => 4 │
# │ Jumba => 5 │ 1st-Qu => 2.3266005718178704 │ A2Ue69EWAMtJCi => 4 │
# │ Truffle => 4 │ Mean => 9.175443804770861 │ 20CO9FGD => 3 │
# │ │ Median => 9.831294839627123 │ KNwmt0QmoqABwR => 2 │
# │ │ 3rd-Qu => 14.619042446877677 │ aY => 2 │
# │ │ Max => 19.868590809216744 │ CQmrQcQ4YkXvaD => 1 │
# O───────────────O──────────────────────────────O─────────────────────O
Summarize collections of tabular datasets
Here is a hash of tabular datasets:
my %group = group-by($tbl, 'Pet'); %group.pairs.map({ say("{$_.key} =>"); say to-pretty-table($_.value) });
# Guinness =>
# O────────────────O───────────O──────────O
# │ Code │ Ref │ Pet │
# O────────────────O───────────O──────────O
# │ A2Ue69EWAMtJCi │ 0.050176 │ Guinness │
# │ aY │ 7.342107 │ Guinness │
# │ 20CO9FGD │ 15.854088 │ Guinness │
# │ A2Ue69EWAMtJCi │ 4.774780 │ Guinness │
# │ A2Ue69EWAMtJCi │ 18.729798 │ Guinness │
# │ xgZjtSP6VrKbH │ 13.383997 │ Guinness │
# │ CQmrQcQ4YkXvaD │ 1.740695 │ Guinness │
# O────────────────O───────────O──────────O
# Truffle =>
# O─────────O───────────O────────────────O
# │ Pet │ Ref │ Code │
# O─────────O───────────O────────────────O
# │ Truffle │ 0.731900 │ KNwmt0QmoqABwR │
# │ Truffle │ 2.912506 │ 20CO9FGD │
# │ Truffle │ 11.782221 │ xgZjtSP6VrKbH │
# │ Truffle │ 9.825102 │ KNwmt0QmoqABwR │
# O─────────O───────────O────────────────O
# Jumba =>
# O───────────O────────────────O───────O
# │ Ref │ Code │ Pet │
# O───────────O────────────────O───────O
# │ 0.739763 │ A2Ue69EWAMtJCi │ Jumba │
# │ 19.868591 │ xgZjtSP6VrKbH │ Jumba │
# │ 12.956172 │ 20CO9FGD │ Jumba │
# │ 9.837488 │ aY │ Jumba │
# │ 16.277717 │ xgZjtSP6VrKbH │ Jumba │
# O───────────O────────────────O───────O
Here is the summary of that collection of datasets:
records-summary(%group)
# summary of Guinness =>
# O──────────────────────────────O─────────────────────O───────────────O
# │ Ref │ Code │ Pet │
# O──────────────────────────────O─────────────────────O───────────────O
# │ Min => 0.0501758995572299 │ A2Ue69EWAMtJCi => 3 │ Guinness => 7 │
# │ 1st-Qu => 1.7406953436440742 │ CQmrQcQ4YkXvaD => 1 │ │
# │ Mean => 8.839377375678543 │ 20CO9FGD => 1 │ │
# │ Median => 7.34210706081909 │ xgZjtSP6VrKbH => 1 │ │
# │ 3rd-Qu => 15.854088005472917 │ aY => 1 │ │
# │ Max => 18.72979803423013 │ │ │
# O──────────────────────────────O─────────────────────O───────────────O
# summary of Truffle =>
# O──────────────O──────────────────────────────O─────────────────────O
# │ Pet │ Ref │ Code │
# O──────────────O──────────────────────────────O─────────────────────O
# │ Truffle => 4 │ Min => 0.7318998724597869 │ KNwmt0QmoqABwR => 2 │
# │ │ 1st-Qu => 1.822202836225727 │ 20CO9FGD => 1 │
# │ │ Mean => 6.312932174017679 │ xgZjtSP6VrKbH => 1 │
# │ │ Median => 6.368803873269801 │ │
# │ │ 3rd-Qu => 10.803661511809633 │ │
# │ │ Max => 11.782221077071329 │ │
# O──────────────O──────────────────────────────O─────────────────────O
# summary of Jumba =>
# O──────────────────────────────O────────────O─────────────────────O
# │ Ref │ Pet │ Code │
# O──────────────────────────────O────────────O─────────────────────O
# │ Min => 0.7397628145038704 │ Jumba => 5 │ xgZjtSP6VrKbH => 2 │
# │ 1st-Qu => 5.28862527360509 │ │ 20CO9FGD => 1 │
# │ Mean => 11.935946110102654 │ │ A2Ue69EWAMtJCi => 1 │
# │ Median => 12.956171789492936 │ │ aY => 1 │
# │ 3rd-Qu => 18.073154106905072 │ │ │
# │ Max => 19.868590809216744 │ │ │
# O──────────────────────────────O────────────O─────────────────────O
Skim
TBD...
TODO
User specified
NAmarkerTabular dataset summarization tests
Skimmer
Peek-er
References
Functions, repositories
[AAf1] Anton Antonov, RecordsSummary, (2019), Wolfram Function Repository.