fintech-algorithms
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Numeric, Categorical, Ordinal, and Binary Variables

Install and import#

bash
npm install fintech-algorithms
ts
import { numericCategoricalOrdinalAndBinaryVariables } from "fintech-algorithms/foundations/data-variables-samples-and-measurement/numeric-categorical-ordinal-and-binary-variables";

Signature#

numericCategoricalOrdinalAndBinaryVariables(input)

Classifies every column of a table as binary, numeric, ordinal or categorical by inspecting the values actually present in it.

Parameters#

NameTypeNotes
inputD00InputA plain object. Every F03 topic first reads rows, which must be a non-empty array of row objects, and derives the sorted union of every key seen across them before any per-topic branch runs. This topic then reads ordinalColumns, an optional array of column names that should be treated as ordered rather than merely categorical; it defaults to empty.

Returns#

{ types: Record<string, string> }

types maps each column name to one of binary, numeric, ordinal or categorical. Null and undefined entries are ignored when judging a column. A column whose remaining values are all booleans is binary; all numbers with two or fewer distinct values is also binary, otherwise numeric; anything else is ordinal when the column is listed in ordinalColumns and categorical when it is not.

Errors#

  • When input is not a plain object — throws TypeError
  • When rows is missing, empty, or not an array — throws RangeError

Complexity: time O(r * c), space O(r * c).

Worked example#

verified This is the worked example published in the article, replayed by the test suite on every run. The output cannot drift.

Input#

input
{
  "rows": [
    {
      "entity": "A",
      "timestamp": "2025-01-01T00:00:00Z",
      "value": 10,
      "rating": "low"
    },
    {
      "entity": "A",
      "timestamp": "2025-01-02T00:00:00Z",
      "value": null,
      "rating": "medium",
      "censored": true
    },
    {
      "entity": "B",
      "timestamp": "2025-01-01T00:00:00Z",
      "value": 12,
      "rating": "high"
    }
  ],
  "ordinalColumns": ["rating"],
  "populationSize": 100,
  "frameSize": 80,
  "keyColumns": ["entity", "timestamp"],
  "truncationRule": "values below 5 excluded",
  "measurements": [9.9, 10, 10.1, 10],
  "referenceValue": 10,
  "vintages": [
    {
      "availableAt": "2025-02-01T00:00:00Z",
      "value": 100
    },
    {
      "availableAt": "2025-03-01T00:00:00Z",
      "value": 102
    },
    {
      "availableAt": "2025-04-01T00:00:00Z",
      "value": 101
    }
  ],
  "asOf": "2025-03-15T00:00:00Z",
  "provenance": {
    "source": "teaching.csv",
    "owner": "Fintech Builder",
    "license": "CC-BY-4.0",
    "retrievedAt": "2026-08-10",
    "transformations": ["parse", "validate"]
  }
}

Call#

numericCategoricalOrdinalAndBinaryVariables(input)

Returns#

object with 1 field: types

{
  "types": {
    "censored": "binary",
    "entity": "categorical",
    "rating": "ordinal",
    "timestamp": "categorical",
    "value": "numeric"
  }
}

Diagrams#

Numeric, Categorical, Ordinal, and Binary Variables — article hero
Numeric, Categorical, Ordinal, and Binary Variables — concept anatomy
Numeric, Categorical, Ordinal, and Binary Variables — mistake contrast
Numeric, Categorical, Ordinal, and Binary Variables — teaching map

How it works#

This page states the contract — how to call it correctly. The article explains the concept: why it works, and where it breaks.

Read the article →

References#

  • Categorical Data
  • 2. W3C Data on the Web Best Practices
  • Evidence boundary

The rest of the Data, Variables, Samples, and Measurement family#