Random CSV Pivot Practice Dataset Generator

Description

Construct exact-count repeated categorical groups with seeded integer/missing values, serialize to CSV and delegate count/sum/mean/min/max pivot answers; count includes missing-value rows, other aggregates omit missing values and return zero for all-missing groups, with the core's floating arithmetic and sorted group keys—not a missing cross-tab fill rule.

CSV stores records as delimited text fields. Quoting can protect embedded delimiters and newlines, so a record need not equal one physical line. Cleaning field contents, interpreting their types, and preserving an exact import/export round trip are separate operations.

When to use this tool

Use this generator to create reproducible csv pivot practice dataset cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

Supply categories, distinctGroupCount, rowCount, minimumValue, maximumValue, missingProbability, aggregation, delimiter in the field shapes below. Set seed when you need an explicit replay.
Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
Read groupColumns, selectedGroups, rows, csv, aggregation, table, missingValueCount. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.

Sample distinct grouping combinations from the Cartesian category space, guarantee each appears, and fill remaining rows from them. Draw integer or missing values, serialize CSV, and delegate the chosen aggregation pivot with explicit grouping columns.^[1]

Delegated operations used for this result: pivot; json to csv. Their calculation or parsing conventions apply to the corresponding result fields.^{[2, 3]}

Input field	Meaning and units	Accepted input
categories	Categories	Required. Array of Array of Text; maximum UTF-16 length 64.; minimum items 1; maximum items 16.; minimum items 1; maximum items 3.
distinctGroupCount	Distinct Group Count	Required. Safe integer; minimum 1; maximum 32.
rowCount	Row Count	Required. Safe integer; minimum 1; maximum 256.
minimumValue	Minimum Value	Required. Safe integer.
maximumValue	Maximum Value	Required. Safe integer.
missingProbability	Missing Probability	Required. Finite number; minimum 0; maximum 1.
aggregation	Aggregation	Required. Text; one of "count", "sum", "mean", "min", "max".
delimiter	Delimiter	Required. Text; one of ",", ";", "\t", "\|".
seed	Optional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.	Optional. Safe integer.

Output field	Interpretation	Returned shape
groupColumns	Group Columns	Required. Array of Text; minimum UTF-16 length 2; maximum UTF-16 length 2.; minimum items 1; maximum items 3.
selectedGroups	Selected Groups	Required. Array of Array of Text; maximum UTF-16 length 64.; minimum items 1; maximum items 3.; minimum items 1; maximum items 32.
rows	Rows	Required. Array of Object; no unknown keys.; minimum items 1; maximum items 256.
rows[].group	Group	Required. Array of Text; maximum UTF-16 length 64.; minimum items 1; maximum items 3.
rows[].value	Value	Required. Safe integer. or Null.
csv	Csv	Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 131072.
aggregation	Aggregation	Required. Text; one of "count", "sum", "mean", "min", "max".
table	Table	Required. Object; no unknown keys.
table.columns	Columns	Required. Array of Text; minimum UTF-16 length 1; maximum UTF-16 length 16.; minimum items 2; maximum items 4.
table.rows	Rows	Required. Array of Array of Text; maximum UTF-16 length 64. or Finite number.; minimum items 2; maximum items 4.; minimum items 1; maximum items 32.
missingValueCount	Missing Value Count	Required. Safe integer; minimum 0; maximum 256.
randomness	Replay source and normalized 32-bit seed, or null seed for an advancing context source.	Required. Object; no unknown keys.
randomness.seed	Optional safe-integer replay seed; normalization and precedence are explained below.	Required. Safe integer. or Null.
randomness.source	Source	Required. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json

{
  "input": {
    "categories": [
      [
        "east",
        "west"
      ]
    ],
    "distinctGroupCount": 2,
    "rowCount": 4,
    "minimumValue": 1,
    "maximumValue": 5,
    "missingProbability": 0.5,
    "aggregation": "sum",
    "delimiter": ",",
    "seed": 42
  },
  "output": {
    "groupColumns": [
      "g0"
    ],
    "selectedGroups": [
      [
        "west"
      ],
      [
        "east"
      ]
    ],
    "rows": [
      {
        "group": [
          "east"
        ],
        "value": 5
      },
      {
        "group": [
          "east"
        ],
        "value": null
      },
      {
        "group": [
          "east"
        ],
        "value": null
      },
      {
        "group": [
          "west"
        ],
        "value": 4
      }
    ],
    "csv": "g0,value\neast,5\neast,\neast,\nwest,4\n",
    "aggregation": "sum",
    "table": {
      "columns": [
        "g0",
        "value_sum"
      ],
      "rows": [
        [
          "east",
          5
        ],
        [
          "west",
          4
        ]
      ]
    },
    "missingValueCount": 2,
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

Missing values and aggregation interpretation follow the linked pivot tool. Group count cannot exceed either the category population or rowCount. This is practice data rather than a conclusion about real groups.
A replay requires the same complete input and implementation. The stated sampling convention applies within its eligible space. A finite draw does not exhaust all possible outputs; repeats remain possible unless a distinct-selection rule is explicit.
The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

Tool behavior and example: Random CSV Pivot Practice Dataset Generator — tool.io
Calculation or validation: csv/pivot — tool.io
Calculation or validation: convert/json-to-csv — tool.io

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