CSV to JSON Converter

Common delimiters: comma (,), semicolon (;), tab (\t), pipe (|). Specify delimiter when parsing. Handle quotes: fields with commas must be quoted "value, with comma". Escape quotes: double quotes inside quoted fields "She said ""hi""". Handle newlines: quoted fields can contain newlines. UTF-8 encoding: supports international characters. Excel CSV quirks: may use different delimiters based on locale. This tool auto-detects delimiters and handles quoted fields correctly. Always validate output for edge cases.

Nested JSON can't directly map to flat CSV. Solutions: (1) Flatten nested objects using dot notation: {user: {name: "John"}} → user.name column. (2) JSON.stringify nested objects: keep as JSON string in CSV cell. (3) Create separate CSV files for nested arrays (normalized tables). (4) Repeat parent data for each nested item (denormalized). Example: {name:"John", orders:[1,2]} could become two rows with repeated name. Choose based on use case: analysis (flatten), re-import (stringify), database (normalize). This tool offers flattening options.

Type conversion issues: CSV treats everything as strings, JSON distinguishes types. Numbers become strings: "123" instead of 123. Solution: parse numeric columns. Booleans: "true" vs true. Date handling: CSV has no date type, must parse. Empty fields: empty string vs null vs undefined. Headers: missing or malformed headers break conversion. Special characters: unescaped quotes, newlines in fields. Large files: memory limits for browser-based tools. Encoding: non-UTF-8 causes garbled text. This tool handles type inference and provides validation feedback.

Streaming approach: don't load entire file into memory, process line-by-line or in chunks. Node.js: use streams (csv-parser, JSONStream). Browser: use FileReader with chunking, or Web Workers for background processing. Batch processing: convert in batches of 1000-10000 rows. Memory management: clear processed data, garbage collection between batches. Server-side: handle large files (100MB+) server-side, not in browser. Compress output: gzip JSON reduces size 70-90%. Database import: consider direct CSV import to database, then export JSON from queries for very large datasets. This tool handles files up to browser memory limits (~100MB).

Array of objects (most common): [{name:"John",age:30},{name:"Jane",age:25}]. Each CSV row → JSON object, CSV headers → object keys. Object with arrays (columnar): {names:["John","Jane"],ages:[30,25]}. Each CSV column → array. Better for data analysis. Nested structure: group related fields: [{name:"John",contact:{email:"...",phone:"..."}}]. Use for complex datasets. With metadata: {headers:["name","age"],data:[["John",30],["Jane",25]]}. Preserves structure info. Choose based on API expectations or data processing needs. Most APIs expect array of objects format.

CSV has no type information - everything is a string. Type inference strategies: (1) Numbers: check if string is valid number, convert with parseFloat() or parseInt(). (2) Booleans: check for "true"/"false", "yes"/"no", "1"/"0". (3) Dates: detect date patterns, convert with Date.parse(). (4) Nulls: treat empty strings or "null" as null. (5) Provide type hints: specify column types in UI or via schema. (6) Keep as strings: safest for round-trip conversion. Libraries: csv-parse with cast option, Papaparse with typed columns. This tool offers automatic type detection or manual type specification per column.

CSV headers become JSON keys. Best practices: (1) Use lowercase: first_name not First Name. (2) No spaces: use underscores or camelCase. (3) No special characters: avoid @#$%. (4) Descriptive names: email not e. (5) Consistent naming: pick snake_case or camelCase and stick to it. (6) Avoid reserved words: don't use class, type if possible. (7) Unique headers: no duplicate column names. Transform headers: this tool can normalize headers automatically. Common transformations: trim whitespace, lowercase, replace spaces with underscores. Handle missing headers: generate column_1, column_2 for headerless CSV.