Description
Apply an exact cell, column or rectangular missingness mask to supplied data and retain pristine answers and explicit masked-cell filling; no statistical missingness mechanism is inferred.
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.
Apply an exact cell, column or rectangular missingness mask to supplied data and retain pristine answers and explicit masked-cell filling; no statistical missingness mechanism is inferred.
When to use this tool
Use this generator to create reproducible csv missing data pattern corpus 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 rows, pattern, defaultValue 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 originalRows, damagedRows, missingMask, defaultFilledCsv. Use the conventions below. Keep answer-bearing fields separate when presenting an exercise.
Plant missingness in distinct cells, entire columns, or a rectangular block and retain the exact mask. Replace marked values with empty text for damagedRows, construct a default-filled table, and delegate the fill-missing CSV convention.1
Delegated operations used for this result: fill missing cells default value. Their calculation or parsing conventions apply to the corresponding result fields.2
| Input field | Meaning and units | Accepted input |
|---|---|---|
| rows | Rows | Required. Array of Array of Text; maximum UTF-16 length 64.; minimum items 1; maximum items 8.; minimum items 1; maximum items 32. |
| pattern | Pattern | Required. Object; no unknown keys. or Object; no unknown keys. or Object; no unknown keys. |
| defaultValue | Default Value | Required. Text; maximum UTF-16 length 64. |
| 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 |
|---|---|---|
| originalRows | Original Rows | Required. Array of Array of Text; maximum UTF-16 length 256.; minimum items 1; maximum items 10.; minimum items 0; maximum items 128. |
| damagedRows | Damaged Rows | Required. Array of Array of Text; maximum UTF-16 length 256.; minimum items 1; maximum items 10.; minimum items 0; maximum items 128. |
| missingMask | Missing Mask | Required. Array of Array of Boolean.; minimum items 0; maximum items 8.; minimum items 0; maximum items 32. |
| defaultFilledCsv | Default Filled Csv | Required. Text; maximum UTF-16 length 100000. |
| 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.
{
"input": {
"rows": [
[
"a",
"b"
],
[
"c",
"d"
]
],
"pattern": {
"kind": "block",
"height": 1,
"width": 1
},
"defaultValue": "NA",
"seed": 42
},
"output": {
"originalRows": [
[
"a",
"b"
],
[
"c",
"d"
]
],
"damagedRows": [
[
"a",
"b"
],
[
"",
"d"
]
],
"missingMask": [
[
false,
false
],
[
true,
false
]
],
"defaultFilledCsv": "a,b\nNA,d",
"randomness": {
"seed": 42,
"source": "seed"
}
}
}Limitations and assumptions
- The mask identifies planted missingness rather than all preexisting empty cells. Default filling does not reconstruct original values, and the delegated filler may also affect empty cells already present in the supplied table.
- 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
Similar or alternative tools
- Sequential Pattern Mining Tool
Mine bounded frequent ordered subsequences of single-event string sequences using prefix projection, allowing arbitrary gaps and repeated events and counting each source sequence at most once per pattern.
- Random CSV Aggregate Reconciliation Exercise Generator
Generate synthetic grouped integer observations, plant one declared subtotal discrepancy and retain exact decimal-string subtotal, grand-total and checksum-row reconciliation answers.