RFC 4180 · Delimiter Auto-Detection · Live Table Preview

CSV to JSON Converter

Convert comma-separated data, TSV exports, and database dumps into structured JSON arrays and back. Inspect results in a live searchable preview table.

Presets:

CSV Delimiter

First Row as Headers

Parse Numbers & Booleans

2-Space Indented JSON

Input CSV Text506 characters

Generated JSON

[
  {
    "id": 101,
    "name": "Smith, Dr. Sarah",
    "role": "Lead Architect",
    "department": "Infrastructure",
    "salary": 145000,
    "active": true
  },
  {
    "id": 102,
    "name": "Marcus Vance",
    "role": "Senior Engineer",
    "department": "Frontend",
    "salary": 128000,
    "active": true
  },
  {
    "id": 103,
    "name": "Elena Rostova",
    "role": "Product Designer",
    "department": "UX",
    "salary": 115000,
    "active": false
  },
  {
    "id": 104,
    "name": "David Kim",
    "role": "Security Specialist",
    "department": "SecOps, Cloud",
    "salary": 138000,
    "active": true
  },
  {
    "id": 105,
    "name": "Chloe Bennett",
    "role": "DevOps Specialist",
    "department": "Platform",
    "salary": 122000,
    "active": true
  },
  {
    "id": 106,
    "name": "Amir Patel",
    "role": "Data Analyst",
    "department": "Business Intelligence",
    "salary": 98000,
    "active": true
  },
  {
    "id": 107,
    "name": "Hannah Scott",
    "role": "Technical Writer",
    "department": "Documentation",
    "salary": 85000,
    "active": true
  },
  {
    "id": 108,
    "name": "Carlos Rivera",
    "role": "QA Engineer",
    "department": "Quality Assurance",
    "salary": 92000,
    "active": false
  }
]

Interactive Data Preview (8 rows)

Live tabular preview of structured columns

id	name	role	department	salary	active
101	Smith, Dr. Sarah	Lead Architect	Infrastructure	145000	true
102	Marcus Vance	Senior Engineer	Frontend	128000	true
103	Elena Rostova	Product Designer	UX	115000	false
104	David Kim	Security Specialist	SecOps, Cloud	138000	true
105	Chloe Bennett	DevOps Specialist	Platform	122000	true

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RFC 4180 Specifications, Serialization Tradeoffs & Common Pitfalls

The RFC 4180 Standard Rules

Many developers attempt naive CSV parsing using line.split(','), which invariably fails in production. RFC 4180 defines the authoritative standard:

Embedded Commas: If a field value contains a comma or newline, the entire field must be enclosed in double quotes (e.g. "Smith, Dr. Sarah").
Escaped Double Quotes: If double quotes appear inside a field, they must be escaped by preceding them with another double quote (e.g. "He said ""Hello""").
Record Endings: Standard records terminate with CRLF (\r\n), though modern Unix tools frequently use LF (\n). Robust parsers must support both.

CSV vs. JSON Serialization Tradeoffs

Selecting between flat tabular formats and hierarchical serialization depends on your system architecture:

CSV Bandwidth Efficiency: CSV repeats headers only once at the top of the file, making large flat tabular datasets (millions of rows) up to 70% smaller than JSON, which repeats key strings for every single record object.
JSON Expressiveness: JSON natively represents typed values (booleans, integers, floats, nulls) and nested trees (arrays of objects). Flattening deep nested trees into CSV requires artificial dot notation (e.g. user.address.street).

Data Type Inference and Localization

In continental Europe, Excel exports use semicolons (;) as column separators because commas serve as the decimal mark (e.g., 14,50 €). Our converter automatically detects semicolons and tabs, and converts string literals like "true" or "128000" to native JavaScript booleans and numbers when requested.