CSV Data Generator

Convert CSV rows with headers into a clean JSON array.

Convert a JSON array of objects into CSV with generated headers.

Validate CSV syntax and structure — checks column consistency, unclosed quotes, and empty headers.

Normalize CSV by parsing and re-emitting with consistent quoting and proper cell escaping.

Render CSV data as an interactive table for quick visual inspection.

Keep only the columns you need. Specify column names and get a slimmed-down CSV.

Drop unwanted columns from a CSV. Specify which columns to remove and get a cleaned result.

Remove duplicate rows from a CSV, keeping the header and the first occurrence of each unique row.

Trim whitespace and normalize CSV records before conversion.

Validate JSON syntax and surface parse errors.

Pretty-print JSON with consistent indentation.

Minify JSON by removing unnecessary whitespace.

Compare two JSON documents and highlight differences.

Sort object keys alphabetically for stable JSON output.

Escape JSON into a quoted string literal for embedding.

Unescape a quoted JSON string back into readable JSON or plain text.

Flatten nested JSON objects into single-level key paths.

Rebuild nested JSON from flattened path-based keys.

Generate a draft JSON Schema from sample JSON input.

Validate JSON data against a provided JSON Schema.

Evaluate JSONPath expressions against JSON input.

Run jq-like query expressions to extract or transform JSON data.

Convert JSON objects or arrays into well-formed XML.

Parse XML into a formatted JSON representation, preserving nested elements and attributes.

Convert CSV rows into XML records. Each row becomes a record element inside a records root.

Convert CSV content into a standalone HTML table document for browser-friendly sharing.

Convert a structured XML document with repeating record elements into CSV rows.

Convert JSON objects and arrays into clean, human-readable YAML.

Parse YAML and convert it to formatted JSON.

Convert CSV rows into YAML arrays of objects.

Convert YAML arrays of records into CSV rows.

Merge multiple JSON documents into one output object.

Split large JSON arrays into smaller chunks.

Rename one or more keys across JSON objects.

Find and replace values in JSON content.

Filter JSON records using key/value conditions.

Evaluate RFC 6901 JSON Pointer expressions against JSON input.

Sort CSV rows by one or more columns.

Filter CSV rows by column conditions.

Join two CSV datasets on a shared key.

Split CSV content into smaller row chunks.

Transpose CSV rows and columns.

Rename, add, or remove CSV headers.

Run a clearly labeled practical XQuery subset for XML exploration: direct XPath, simple for/let expressions, count(), and string().

Lint JSON syntax and style with stricter practical checks.

Generate RFC 6902-style patch operations from left/right JSON values.

Convert CSV between comma, semicolon, tab, and pipe delimiters.

Compute row/column stats and numeric summaries for CSV data.

Apply RFC 6902-style patch operations to JSON data.

Analyze JSON size and structural complexity metrics.

Add computed CSV columns with basic arithmetic formulas.

Convert JSON row objects into SQL INSERT statements.

Convert CSV text between UTF-8, Latin-1, and Windows-1252 encodings.

Convert a base64-encoded XLSX workbook sheet into CSV.

Convert CSV text into an XLSX base64 output envelope.

Convert JSON row objects into an XLSX base64 output envelope.

Flatten nested XML into a path-based key representation.

Render JSON as an interactive collapsible tree with type labels and copyable paths.

Render a JSON array of objects as a sortable, searchable table for fast inspection.

Profile CSV columns with inferred type, emptiness, uniqueness, top values, and numeric percentiles.

Profile field-level type consistency for JSON arrays, including null-rate and unique count metrics.

Audit JSON Schema complexity with required/optional coverage, depth, and missing description checks.

Analyze XML structure with element counts, tag frequency, depth, attribute totals, and namespace usage.

Analyze YAML structure for key depth, type distribution, array sizing, and anchor/alias usage.

Auto-detect whether input is JSON, XML, YAML, CSV, or plain text.

Detect, pretty-format, and analyze API responses into a JSON report with HTTP metadata and formatted payload previews.

Run JSONPath, XPath, XQuery-subset, YAMLPath, and JSONata queries through one tool.

Auto-detect sources and convert across supported JSON, XML, YAML, CSV, and Excel pairs, including structured JSON input and Excel base64 envelopes.

Generate deterministic mock records for JSON/CSV/XML/YAML testing.

Apply per-column transformation rules to CSV data. Chain operations like trim, uppercase, lowercase, rounding, date conversion, and find-replace.

Create a cross-tabulation (pivot table) from flat CSV data. Specify row key, column key, aggregation field, and function (count, sum, mean).

Project and rename fields from a JSON array of objects. Select which columns to keep and optionally rename them.

Group a JSON array of objects by a specified field. Output as a keyed object or as an array of groups.

Generate a valid JSON template from JSON Schema using defaults, examples, enums, and type placeholders.

Generate UUID or sequential IDs in JSON array, newline, or CSV formats.

Generate timestamps in multiple formats for offsets from an anchor date.

Plot numeric CSV columns as a bar or line chart. Choose axes and chart type; runs 100% in the browser.

Render CSV as a color-graded table where numeric cells are colored from cool (low) to warm (high) within each column.

Identify data quality issues in CSV: missing values, numeric outliers (IQR), type inconsistencies, and duplicate key rows.

Given a regex pattern, generate N example strings that match it. Useful for structured IDs, codes, and format-specific mock values.