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Random CSV Ragged Row Repair Exercise Generator

Mutate distinct supplied records of at least two fields by removing or appending one field, retaining pristine repair answers, field provenance and the CSV core's record-width audit.

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Description

Mutate distinct supplied records of at least two fields by removing or appending one field, retaining pristine repair answers, field provenance and the CSV core's record-width audit.

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.

Mutate distinct supplied records of at least two fields by removing or appending one field, retaining pristine repair answers, field provenance and the CSV core's record-width audit.

When to use this tool

Use this generator to create reproducible csv ragged row repair exercise 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

  1. Supply rows, mutationCount, extraValue in the field shapes below. Set seed when you need an explicit replay.
  2. 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.
  3. Read damagedRows, answerRows, changes, raggedIndices, csv. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.

Select distinct rows and either remove one cell or append extraValue. Serialize the ragged table, delegate wrong-field-count detection for the original width, and retain the unchanged original rows as answerRows.1

Delegated operations used for this result: ragged rows wrong field count finder. Their calculation or parsing conventions apply to the corresponding result fields.2

Input fieldMeaning and unitsAccepted input
rowsRowsRequired. Array of Array of Text; maximum UTF-16 length 64.; minimum items 2; maximum items 8.; minimum items 1; maximum items 32.
mutationCountMutation CountRequired. Safe integer; minimum 1; maximum 32.
extraValueExtra ValueRequired. Text; maximum UTF-16 length 64.
seedOptional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.Optional. Safe integer.
Output fieldInterpretationReturned shape
damagedRowsDamaged RowsRequired. Array of Array of Text; maximum UTF-16 length 256.; minimum items 1; maximum items 10.; minimum items 0; maximum items 128.
answerRowsAnswer RowsRequired. Array of Array of Text; maximum UTF-16 length 256.; minimum items 1; maximum items 10.; minimum items 0; maximum items 128.
changesChangesRequired. Array of Object; no unknown keys.; minimum items 0; maximum items 32.
changes[].rowRowRequired. Safe integer; minimum 0; maximum 31.
changes[].columnColumnRequired. Safe integer; minimum 0; maximum 8.
changes[].kindKindRequired. Text; one of "remove", "append".
changes[].valueValueRequired. Text; maximum UTF-16 length 64.
raggedIndicesRagged IndicesRequired. Array of Safe integer; minimum 0; maximum 31.; minimum items 0; maximum items 32.
csvCsvRequired. Text; maximum UTF-16 length 100000.
randomnessReplay source and normalized 32-bit seed, or null seed for an advancing context source.Required. Object; no unknown keys.
randomness.seedOptional safe-integer replay seed; normalization and precedence are explained below.Required. Safe integer. or Null.
randomness.sourceSourceRequired. 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": {
    "rows": [
      [
        "a",
        "1"
      ],
      [
        "b",
        "2"
      ]
    ],
    "mutationCount": 1,
    "extraValue": "extra",
    "seed": 42
  },
  "output": {
    "damagedRows": [
      [
        "a",
        "1"
      ],
      [
        "b"
      ]
    ],
    "answerRows": [
      [
        "a",
        "1"
      ],
      [
        "b",
        "2"
      ]
    ],
    "changes": [
      {
        "row": 1,
        "column": 1,
        "kind": "remove",
        "value": "2"
      }
    ],
    "raggedIndices": [
      1
    ],
    "csv": "a,1\nb",
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

  • The answer restores known source data; it is not inferred recovery of missing cells. Record indexes use the delegated audit convention, which should be distinguished from displayed physical lines in quoted CSV.
  • 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

  1. Tool behavior and example: Random CSV Ragged Row Repair Exercise Generator — tool.io

  2. Calculation or validation: csv/ragged-rows-wrong-field-count-finder — tool.io

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