Infer JSON Schema from CSV data with column type detection, patterns, and validation rules.
Paste representative CSV rows to obtain a JSON Schema describing the fields, detected value types, recurring text shapes, and applicable validation constraints. Inference is based on the values present, so the result is a starting contract for CSV-derived records rather than proof of every value the dataset may contain. The common surprise is over-specificity: a small or unusually clean sample can make identifiers, optional fields, or patterns look stricter than the complete data really is.
Type detection considers values within each CSV column as evidence for that field. Consistent numeric, boolean, or other recognizable values can support a narrower JSON Schema type, while mixed values require a representation compatible with the sample. The column label connects those observations to a named field. Because CSV carries no native value types, every result is an inference from text.
Recurring shapes can be expressed as patterns or related validation rules in the generated schema. Those rules summarize what the pasted rows demonstrate; they do not establish business meaning. Review each constraint before treating the schema as authoritative, especially when a column contains few distinct values, leading zeroes, blank cells, or values that resemble dates but are identifiers.
No. It describes constraints inferred from the pasted sample. A future file may contain a blank, a new category, a larger number, or a differently formatted identifier that the sample did not reveal. Validate additional representative files and loosen or edit inferred constraints before using the schema as a production contract.
CSV cells do not carry a JSON type. When every observed value contains only digits, numeric inference may appear plausible even though the field is semantically an identifier. That is unsuitable for leading zeroes or values never used in arithmetic. Change that property to a string and, if its format is stable, apply a reviewed pattern.
The behavioral notes were checked against the browser implementation. Standards and primary references below define the relevant format, formula, or platform behavior.
Format, validate, and beautify JSON with syntax highlighting and error detection.
Encode text or files to Base64 and decode Base64 strings back to their original form.
Format and indent SQL queries for better readability across all major dialects.
Convert HTML markup into clean Markdown syntax with support for tables and lists.
Write Markdown and see the rendered HTML output in real time with live preview.
Visualize unified diffs and patch files with color-coded additions and deletions.