@cerios/csv-nested-json

@cerios/csv-nested-json

A powerful TypeScript CSV parser that transforms flat CSV data into nested JSON objects with support for dot notation, automatic array detection, and complex hierarchical structures.

🚀 Features

• Zero Dependencies - No external CSV parsing libraries
• Nested Objects - Use dot notation in headers (e.g., address.city)
• Automatic Array Detection - Smart array creation for grouped rows
• Multi-Level Nesting - Support for deeply nested structures
• Multiple Input Methods - Parse from files (sync/async), strings, or streams
• True Streaming Parser - Memory-efficient parsing for very large files
• Bidirectional Conversion - Convert CSV to JSON and JSON back to CSV
• Value Transformations - Auto-parse numbers, booleans, dates, or use custom transformers
• Header Transformations - Transform and map column names
• Row Filtering - Filter rows during parsing for memory efficiency
• RFC 4180 Compliant - Handles quoted fields, escaped quotes, and various line endings
• Flexible Delimiters - Support for comma, semicolon, tab, pipe, and custom delimiters
• Custom Encodings - Handle different file encodings (UTF-8, Latin1, etc.)
• BOM Handling - Automatic Byte Order Mark detection and removal
• TypeScript & JavaScript - Full type definitions included
• CommonJS & ESM - Works in both module systems
• Validation Modes - Flexible error handling for malformed data
• Custom Error Classes - Detailed error information for debugging

📦 Installation

npm install @cerios/csv-nested-json

🎯 Quick Start

import { CsvParser } from '@cerios/csv-nested-json';

// Parse CSV file
const result = CsvParser.parseFileSync('data.csv');
console.log(result);

// Output:
// [
//   {
//     id: "1",
//     name: "John Doe",
//     address: {
//       street: "123 Main St",
//       city: "New York",
//       zip: "10001"
//     }
//   }
// ]

📖 API Reference

Method	Description
CsvParser.parseFileSync()	Parse CSV file synchronously
CsvParser.parseFile()	Parse CSV file asynchronously
CsvParser.parseString()	Parse CSV string content
CsvParser.parseStream()	Parse CSV from readable stream
CsvStreamParser	True streaming parser for very large files
JsonToCsv.stringify()	Convert JSON objects to CSV string
JsonToCsv.writeFileSync()	Write JSON objects to CSV file (sync)
JsonToCsv.writeFile()	Write JSON objects to CSV file (async)

🔧 Basic Usage

1. Parse File (Synchronous)

import { CsvParser } from '@cerios/csv-nested-json';

const result = CsvParser.parseFileSync('./data.csv');

When to use: Small to medium files (<10MB), synchronous workflows, simple scripts.

2. Parse File (Asynchronous)

import { CsvParser } from '@cerios/csv-nested-json';

const result = await CsvParser.parseFile('./data.csv');

When to use: Medium to large files, async/await workflows, web servers, non-blocking operations.

3. Parse String

import { CsvParser } from '@cerios/csv-nested-json';

const csvString = `id,name,age
1,Alice,30
2,Bob,25`;

const result = CsvParser.parseString(csvString);

When to use: API responses, in-memory CSV data, testing, dynamic CSV generation.

4. Parse Stream

import { CsvParser } from '@cerios/csv-nested-json';
import { createReadStream } from 'node:fs';

const stream = createReadStream('./large-file.csv');
const result = await CsvParser.parseStream(stream);

When to use: Very large files (>100MB), memory-constrained environments, real-time processing.

5. True Streaming Parser (Memory Efficient)

For very large files where you want to process records one at a time without loading everything into memory:

import { CsvStreamParser } from '@cerios/csv-nested-json';
import { createReadStream } from 'node:fs';

const parser = new CsvStreamParser({
  autoParseNumbers: true,
  autoParseBooleans: true
});

// Using async iteration
const stream = createReadStream('./very-large-file.csv');
for await (const record of stream.pipe(parser)) {
  console.log('Parsed record:', record);
  // Process each record as it's parsed
}

// Or using events
createReadStream('./very-large-file.csv')
  .pipe(new CsvStreamParser())
  .on('data', (record) => {
    console.log('Record:', record);
  })
  .on('end', () => {
    console.log('Done!');
  })
  .on('error', (err) => {
    console.error('Error:', err);
  });

When to use: Files too large to fit in memory, real-time processing, ETL pipelines.

6. Convert JSON to CSV

import { JsonToCsv } from '@cerios/csv-nested-json';

const data = [
  {
    id: '1',
    name: 'Alice',
    address: { city: 'NYC', zip: '10001' }
  },
  {
    id: '2',
    name: 'Bob',
    address: { city: 'LA', zip: '90001' }
  }
];

// Convert to CSV string
const csvString = JsonToCsv.stringify(data);
console.log(csvString);
// Output:
// id,name,address.city,address.zip
// 1,Alice,NYC,10001
// 2,Bob,LA,90001

// Write directly to file
JsonToCsv.writeFileSync('./output.csv', data);

// Or async
await JsonToCsv.writeFile('./output.csv', data);

🎯 Advanced Examples

Simple Flat CSV

Input CSV:

id,name,email
1,John Doe,[e-mail]
2,Jane Smith,[e-mail]

Output JSON:

[
  {
    "id": "1",
    "name": "John Doe",
    "email": "[e-mail]"
  },
  {
    "id": "2",
    "name": "Jane Smith",
    "email": "[e-mail]"
  }
]

Nested Objects with Dot Notation

Input CSV:

id,name,address.street,address.city,address.zip
1,John Doe,123 Main St,New York,10001

Code:

const result = CsvParser.parseFileSync('./nested-data.csv');

Output JSON:

[
  {
    "id": "1",
    "name": "John Doe",
    "address": {
      "street": "123 Main St",
      "city": "New York",
      "zip": "10001"
    }
  }
]

Arrays from Grouped Rows

Rows without a value in the first column are treated as continuation rows and automatically create arrays:

Input CSV:

id,name,phones.type,phones.number
1,Alice,mobile,555-0001
,,home,555-0002
,,work,555-0003

Code:

const result = CsvParser.parseFileSync('./grouped-data.csv');

Output JSON:

[
  {
    "id": "1",
    "name": "Alice",
    "phones": [
      { "type": "mobile", "number": "555-0001" },
      { "type": "home", "number": "555-0002" },
      { "type": "work", "number": "555-0003" }
    ]
  }
]

Deeply Nested Structures

Input CSV:

id,user.name,user.profile.age,user.profile.address.city,user.profile.address.zip
1,Alice,30,New York,10001

Code:

const result = CsvParser.parseString(csvContent);

Output JSON:

[
  {
    "id": "1",
    "user": {
      "name": "Alice",
      "profile": {
        "age": "30",
        "address": {
          "city": "New York",
          "zip": "10001"
        }
      }
    }
  }
]

Forced Array Fields with [] Suffix

Use the [] suffix in headers to force a field to always be an array, even with a single value:

Input CSV:

id,name,tags[]
1,Alice,javascript
2,Bob,python

Code:

const result = CsvParser.parseString(csvContent);

Output JSON:

[
  { "id": "1", "name": "Alice", "tags": ["javascript"] },
  { "id": "2", "name": "Bob", "tags": ["python"] }
]

Auto-Parse Numbers and Booleans

const csvContent = `id,name,age,price,active,verified
1,Alice,30,19.99,true,FALSE
2,Bob,25,29.99,false,TRUE`;

const result = CsvParser.parseString(csvContent, {
  autoParseNumbers: true,
  autoParseBooleans: true
});

// Result:
// [
//   { id: 1, name: "Alice", age: 30, price: 19.99, active: true, verified: false },
//   { id: 2, name: "Bob", age: 25, price: 29.99, active: false, verified: true }
// ]

Note: Strings with leading zeros (like "007") are preserved as strings to avoid data loss.

Auto-Parse Dates

const csvContent = `id,name,createdAt,updatedAt
1,Alice,2024-01-15,2024-06-30T10:30:00Z`;

const result = CsvParser.parseString(csvContent, {
  autoParseDates: true
});

// Result:
// [
//   {
//     id: "1",
//     name: "Alice",
//     createdAt: Date("2024-01-15"),
//     updatedAt: Date("2024-06-30T10:30:00Z")
//   }
// ]

Custom Value Transformer

const csvContent = `id,name,email
1,alice,[e-mail]
2,bob,[e-mail]`;

const result = CsvParser.parseString(csvContent, {
  valueTransformer: (value, header) => {
    // Uppercase names
    if (header === 'name' && typeof value === 'string') {
      return value.toUpperCase();
    }
    return value;
  }
});

// Result:
// [
//   { id: "1", name: "ALICE", email: "[e-mail]" },
//   { id: "2", name: "BOB", email: "[e-mail]" }
// ]

Header Transformation

const csvContent = `User ID,First Name,Last Name,Email Address
1,John,Doe,[e-mail]`;

const result = CsvParser.parseString(csvContent, {
  // Convert headers to camelCase
  headerTransformer: (header) => {
    return header
      .toLowerCase()
      .replace(/\s+(.)/g, (_, c) => c.toUpperCase());
  }
});

// Result:
// [{ userId: "1", firstName: "John", lastName: "Doe", emailAddress: "[e-mail]" }]

Column Mapping

const csvContent = `user_id,first_name,last_name
1,John,Doe`;

const result = CsvParser.parseString(csvContent, {
  columnMapping: {
    'user_id': 'id',
    'first_name': 'firstName',
    'last_name': 'lastName'
  }
});

// Result:
// [{ id: "1", firstName: "John", lastName: "Doe" }]

Row Filtering

Filter rows during parsing for better memory efficiency:

const csvContent = `id,name,status
1,Alice,active
2,Bob,deleted
3,Charlie,active
4,Diana,pending`;

const result = CsvParser.parseString(csvContent, {
  rowFilter: (record, rowIndex) => {
    // Only include active records
    return record.status === 'active';
  }
});

// Result:
// [
//   { id: "1", name: "Alice", status: "active" },
//   { id: "3", name: "Charlie", status: "active" }
// ]

Skip Rows (Metadata Headers)

const csvContent = `Report generated on 2024-01-15
Source: Production Database
id,name,email
1,Alice,[e-mail]
2,Bob,[e-mail]`;

const result = CsvParser.parseString(csvContent, {
  skipRows: 2  // Skip the first 2 metadata rows
});

// Result:
// [
//   { id: "1", name: "Alice", email: "[e-mail]" },
//   { id: "2", name: "Bob", email: "[e-mail]" }
// ]

Default Values

const csvContent = `id,name,status,country
1,Alice,,
2,Bob,active,USA`;

const result = CsvParser.parseString(csvContent, {
  defaultValues: {
    status: 'pending',
    country: 'Unknown'
  }
});

// Result:
// [
//   { id: "1", name: "Alice", status: "pending", country: "Unknown" },
//   { id: "2", name: "Bob", status: "active", country: "USA" }
// ]

Null Value Handling

const csvContent = `id,name,nickname
1,Alice,N/A
2,Bob,null
3,Charlie,Bobby`;

const result = CsvParser.parseString(csvContent, {
  nullValues: ['null', 'NULL', 'N/A', 'n/a', ''],
  nullRepresentation: 'null'  // or 'undefined', 'empty-string', 'omit'
});

// Result with nullRepresentation: 'null':
// [
//   { id: "1", name: "Alice", nickname: null },
//   { id: "2", name: "Bob", nickname: null },
//   { id: "3", name: "Charlie", nickname: "Bobby" }
// ]

// Result with nullRepresentation: 'omit' (default):
// [
//   { id: "1", name: "Alice" },
//   { id: "2", name: "Bob" },
//   { id: "3", name: "Charlie", nickname: "Bobby" }
// ]

BOM Handling

The parser automatically strips UTF-8 and UTF-16 BOM by default:

// BOM is automatically handled
const result = CsvParser.parseFileSync('./windows-excel-export.csv');

// Disable BOM stripping if needed
const result2 = CsvParser.parseString(csvContent, {
  stripBom: false
});

Complex Multi-Group Example

Input CSV:

id,username,profile.firstName,profile.lastName,addresses.type,addresses.city
1,johndoe,John,Doe,home,New York
,,,,work,Boston
2,janedoe,Jane,Doe,home,Chicago

Code:

const result = CsvParser.parseFileSync('./complex-data.csv');

Output JSON:

[
  {
    "id": "1",
    "username": "johndoe",
    "profile": {
      "firstName": "John",
      "lastName": "Doe"
    },
    "addresses": [
      { "type": "home", "city": "New York" },
      { "type": "work", "city": "Boston" }
    ]
  },
  {
    "id": "2",
    "username": "janedoe",
    "profile": {
      "firstName": "Jane",
      "lastName": "Doe"
    },
    "addresses": {
      "type": "home",
      "city": "Chicago"
    }
  }
]

Note: The first record has an array of addresses (multiple entries), while the second has a single address object.

Custom Delimiters

Semicolon-separated values:

const csvSemicolon = `id;name;city
1;Alice;NYC
2;Bob;LA`;

const result = CsvParser.parseString(csvSemicolon, {
  delimiter: ';'
});

Tab-separated values:

const csvTab = `id\tname\tcity
1\tAlice\tNYC`;

const result = CsvParser.parseString(csvTab, {
  delimiter: '\t'
});

Pipe-separated values:

const csvPipe = `id|name|city
1|Alice|NYC`;

const result = CsvParser.parseString(csvPipe, {
  delimiter: '|'
});

Custom Quote Character

const csvSingleQuote = `id,name,message
1,Alice,'Hello, World'
2,Bob,'It''s working'`;

const result = CsvParser.parseString(csvSingleQuote, {
  quote: "'"
});

Custom Encoding

// Latin1 encoding
const result = await CsvParser.parseFile('./data-latin1.csv', {
  encoding: 'latin1'
});

// UTF-16LE encoding
const result2 = await CsvParser.parseFile('./data-utf16.csv', {
  encoding: 'utf16le'
});

Validation Modes

// Ignore extra columns silently
const result1 = CsvParser.parseString(csvData, {
  validationMode: 'ignore'
});

// Warn about extra columns (default)
const result2 = CsvParser.parseString(csvData, {
  validationMode: 'warn'
});

// Throw error on extra columns
try {
  const result3 = CsvParser.parseString(csvData, {
    validationMode: 'error'
  });
} catch (error) {
  console.error('Validation error:', error.message);
}

Parse CSV from API Response

async function parseApiCsv() {
  const response = await fetch('https://api.example.com/data.csv');
  const csvString = await response.text();

  const data = CsvParser.parseString(csvString, {
    validationMode: 'ignore'
  });

  return data;
}

Parse Large File with Streams

import { createReadStream } from 'node:fs';

async function parseLargeFile(filePath: string) {
  const stream = createReadStream(filePath, {
    highWaterMark: 64 * 1024 // 64KB chunks
  });

  const data = await CsvParser.parseStream(stream, {
    validationMode: 'warn',
    encoding: 'utf-8'
  });

  return data;
}

European CSV Format

European CSV files typically use semicolon delimiters and comma as decimal separator:

const europeanCsv = `id;name;price;location.city;location.country
1;Product A;12,50;Paris;France
2;Product B;8,99;Berlin;Germany`;

const result = CsvParser.parseString(europeanCsv, {
  delimiter: ';',
  validationMode: 'error'
});

// Result:
// [
//   {
//     id: "1",
//     name: "Product A",
//     price: "12,50",
//     location: { city: "Paris", country: "France" }
//   },
//   ...
// ]

Parse Multiple Files Concurrently

const files = ['data1.csv', 'data2.csv', 'data3.csv'];

const results = await Promise.all(
  files.map(file => CsvParser.parseFile(file))
);

🧪 Options Reference

CsvParserOptions

interface CsvParserOptions {
  // Validation
  validationMode?: 'ignore' | 'warn' | 'error';  // Default: 'warn'

  // Parsing
  delimiter?: string;                             // Default: ','
  quote?: string;                                 // Default: '"'

  // File I/O
  encoding?: BufferEncoding;                      // Default: 'utf-8'

  // Row handling
  skipRows?: number;                              // Default: 0
  stripBom?: boolean;                             // Default: true
  rowFilter?: (record, rowIndex) => boolean;      // Filter rows during parsing

  // Value transformations
  autoParseNumbers?: boolean;                     // Default: false
  autoParseBooleans?: boolean;                    // Default: false
  autoParseDates?: boolean;                       // Default: false
  valueTransformer?: (value, header) => any;      // Custom value transformer

  // Header transformations
  headerTransformer?: (header) => string;         // Transform header names
  columnMapping?: Record<string, string>;         // Rename columns

  // Array handling
  arraySuffixIndicator?: string;                  // Default: '[]'
  emptyArrayBehavior?: 'empty-array' | 'omit';    // Default: 'omit'

  // Null handling
  nullValues?: string[];                          // Values to treat as null
  nullRepresentation?: 'null' | 'undefined' | 'empty-string' | 'omit';  // Default: 'omit'

  // Default values
  defaultValues?: Record<string, string>;         // Default values for empty cells
}

Option Details

validationMode

Controls how the parser handles rows with more values than headers:

• 'ignore': Silently ignore extra values
• 'warn' (default): Log a warning to console
• 'error': Throw a CsvValidationError

delimiter

Field delimiter character. Common values:

• ',' (default) - Comma-separated values
• ';' - Semicolon-separated values (common in Europe)
• '\t' - Tab-separated values
• '|' - Pipe-separated values

quote

Quote character for escaping fields containing delimiters or newlines:

• '"' (default) - Double quotes
• "'" - Single quotes

encoding

File encoding when reading from files or streams:

• 'utf-8' (default)
• 'utf-16le'
• 'latin1'
• 'ascii'

skipRows

Number of rows to skip before the header row. Useful for files with metadata at the top.

stripBom

Automatically remove BOM (Byte Order Mark) from the beginning of content. Default: true

autoParseNumbers

Automatically convert numeric strings to numbers. Strings with leading zeros (like "007") are preserved.

autoParseBooleans

Automatically convert 'true'/'false' strings to booleans (case-insensitive).

autoParseDates

Automatically convert date strings to JavaScript Date objects using Date.parse().

valueTransformer

Custom function to transform values after parsing. Called after auto-parse options.

valueTransformer: (value, header) => {
  if (header === 'email') return value.toLowerCase();
  return value;
}

headerTransformer

Transform header names before processing. Useful for converting to camelCase, lowercase, etc.

headerTransformer: (header) => header.toLowerCase().replace(/\s+/g, '_')

columnMapping

Map/rename column headers. Applied after headerTransformer.

columnMapping: { 'user_id': 'id', 'first_name': 'firstName' }

rowFilter

Filter rows during parsing. More memory-efficient than filtering after parsing.

rowFilter: (record, rowIndex) => record.status === 'active'

arraySuffixIndicator

Suffix in headers to force array type. Default: '[]'

emptyArrayBehavior

How to handle forced array fields with no values:

• 'omit' (default): Don't include the field
• 'empty-array': Include as []

nullValues

Strings to interpret as null values. Default: ['null', 'NULL', 'nil', 'NIL']

nullRepresentation

How to represent null values in output:

• 'omit' (default): Remove the field
• 'null': Use JavaScript null
• 'undefined': Use JavaScript undefined
• 'empty-string': Use empty string ''

defaultValues

Default values for columns when cells are empty.

defaultValues: { status: 'pending', country: 'Unknown' }

Complete Example with All Options

const result = await CsvParser.parseFile('./data.csv', {
  // Validation
  validationMode: 'error',

  // Parsing
  delimiter: ',',
  quote: '"',
  encoding: 'utf-8',

  // Row handling
  skipRows: 2,
  stripBom: true,
  rowFilter: (record) => record.status !== 'deleted',

  // Value transformations
  autoParseNumbers: true,
  autoParseBooleans: true,
  autoParseDates: true,
  valueTransformer: (value, header) => {
    if (header === 'email') return value.toLowerCase();
    return value;
  },

  // Header transformations
  headerTransformer: (h) => h.toLowerCase().replace(/\s+/g, '_'),
  columnMapping: { 'user_id': 'id' },

  // Array handling
  arraySuffixIndicator: '[]',
  emptyArrayBehavior: 'empty-array',

  // Null handling
  nullValues: ['null', 'N/A', '-'],
  nullRepresentation: 'null',

  // Defaults
  defaultValues: { status: 'pending' }
});

📚 API Reference

CsvParser Class

parseFileSync<T>(filePath: string, options?: CsvParserOptions): T[]

Parses a CSV file synchronously and returns an array of nested JSON objects.

parseFile<T>(filePath: string, options?: CsvParserOptions): Promise<T[]>

Parses a CSV file asynchronously.

parseString<T>(csvContent: string, options?: CsvParserOptions): T[]

Parses CSV string content.

parseStream<T>(stream: Readable, options?: CsvParserOptions): Promise<T[]>

Parses CSV from a readable stream.

CsvStreamParser Class

A Transform stream that parses CSV data chunk by chunk, emitting records as they become available.

import { CsvStreamParser } from '@cerios/csv-nested-json';

const parser = new CsvStreamParser({
  nested: true,           // Emit nested objects (default: true)
  autoParseNumbers: true,
  // ... other CsvParserOptions
});

createReadStream('./large.csv')
  .pipe(parser)
  .on('data', (record) => console.log(record))
  .on('end', () => console.log('Done'));

JsonToCsv Class

stringify(data: object[], options?: JsonToCsvOptions): string

Convert array of objects to CSV string.

writeFileSync(filePath: string, data: object[], options?: JsonToCsvOptions): void

Write objects to CSV file synchronously.

writeFile(filePath: string, data: object[], options?: JsonToCsvOptions): Promise<void>

Write objects to CSV file asynchronously.

import { JsonToCsv } from '@cerios/csv-nested-json';

const data = [
  { id: 1, user: { name: 'Alice', age: 30 }, tags: ['js', 'ts'] }
];

const csv = JsonToCsv.stringify(data, {
  delimiter: ',',
  quote: '"',
  arrayMode: 'rows'  // 'rows' (continuation rows) or 'json' (stringify arrays)
});

Error Classes

The library provides custom error classes for better error handling:

import {
  CsvParseError,
  CsvFileNotFoundError,
  CsvValidationError,
  CsvEncodingError
} from '@cerios/csv-nested-json';

try {
  const result = CsvParser.parseFileSync('./data.csv', {
    validationMode: 'error'
  });
} catch (error) {
  if (error instanceof CsvFileNotFoundError) {
    console.error(`File not found: ${error.filePath}`);
  } else if (error instanceof CsvValidationError) {
    console.error(`Validation error at row ${error.row}`);
    console.error(`Expected ${error.expectedColumns}, got ${error.actualColumns}`);
  } else if (error instanceof CsvEncodingError) {
    console.error(`Encoding error: ${error.encoding}`);
  } else if (error instanceof CsvParseError) {
    console.error(`Parse error at row ${error.row}, column ${error.column}`);
  }
}

💡 How It Works

1. Row Grouping

Records are grouped by the first column (identifier). When the first column is empty, the row is treated as a continuation of the previous group:

id,name,item
1,Alice,Book
,,Pen
,,Notebook
2,Bob,Laptop

Groups:

• Group 1: Rows with id=1 and the two continuation rows
• Group 2: Row with id=2

2. Dot Notation Parsing

Column headers with dots create nested object structures:

user.profile.name,user.profile.age
Alice,30

Creates:

{
  "user": {
    "profile": {
      "name": "Alice",
      "age": "30"
    }
  }
}

3. Automatic Array Detection

When the same key path appears multiple times within a group, an array is automatically created:

id,contact.type,contact.value
1,email,[e-mail]
,,phone,555-1234

Creates:

{
  "id": "1",
  "contact": [
    { "type": "email", "value": "[e-mail]" },
    { "type": "phone", "value": "555-1234" }
  ]
}

4. Empty Value Handling

Empty or null values are omitted from the output:

id,name,optional
1,Alice,
2,Bob,Value

Creates:

[
  { "id": "1", "name": "Alice" },
  { "id": "2", "name": "Bob", "optional": "Value" }
]

🆚 Comparison

When to Use Each Method

Method	Best For	File Size	Blocking
parseFileSync()	Scripts, small files	<10MB	Yes
parseFile()	Web servers, medium files	10MB-100MB	No
parseString()	API responses, testing	Any (in-memory)	Yes
parseStream()	Large files, memory efficiency	>100MB	No
CsvStreamParser	Very large files, ETL pipelines	Any size	No

Traditional CSV Parsing

// ❌ Manual parsing - tedious and error-prone
const fs = require('fs');
const data = fs.readFileSync('data.csv', 'utf-8');
const lines = data.split('\n');
const headers = lines[0].split(',');
const result = [];

for (let i = 1; i < lines.length; i++) {
  const values = lines[i].split(',');
  const obj = {};

  for (let j = 0; j < headers.length; j++) {
    const keys = headers[j].split('.');
    let current = obj;

    // Manually handle nesting...
    // ... complex nested object logic
  }

  result.push(obj);
}

With @cerios/csv-nested-json

// ✅ Simple, type-safe, and powerful
const result = CsvParser.parseFileSync('data.csv');

// ✅ Automatic nested object creation
// ✅ Automatic array detection
// ✅ RFC 4180 compliant parsing
// ✅ Flexible configuration options

📋 CSV Format Support

The library is fully RFC 4180 compliant and supports:

• ✅ Quoted Fields with Commas: "value, with, commas"
• ✅ Quoted Fields with Newlines: Multi-line values within quotes
• ✅ Escaped Quotes: "He said ""Hello""" → He said "Hello"
• ✅ Various Line Endings: Windows (CRLF), Unix (LF), Mac (CR)
• ✅ BOM Handling: UTF-8 and UTF-16 BOM automatically stripped
• ✅ Empty Lines: Automatically skipped
• ✅ Flexible Column Counts: Continuation rows can have different column counts
• ✅ Custom Delimiters: Comma, semicolon, tab, pipe, or any character
• ✅ Custom Quote Characters: Double quotes, single quotes, or any character
• ✅ Multiple Encodings: UTF-8, Latin1, UTF-16, and more

Quoted Fields Examples

id,name,description
1,Alice,"Product with, comma"
2,Bob,"Product with ""quotes"""
3,Charlie,"Multi-line
description here"

All of these are correctly parsed!

💻 TypeScript Support

Full TypeScript support with comprehensive type definitions:

import {
  CsvParser,
  CsvStreamParser,
  JsonToCsv,
  CsvParserOptions,
  CsvParseError,
  CsvValidationError,
  NestedObject
} from '@cerios/csv-nested-json';

// Generic type support
interface Person {
  id: number;
  name: string;
  address: {
    city: string;
    zip: string;
  };
}

const result = CsvParser.parseFileSync<Person>('people.csv', {
  autoParseNumbers: true
});

// result is typed as Person[]
console.log(result[0].address.city);

Exported Types

// Options
type ValidationMode = 'ignore' | 'warn' | 'error';
type EmptyArrayBehavior = 'empty-array' | 'omit';
type NullRepresentation = 'null' | 'undefined' | 'empty-string' | 'omit';
type ArrayMode = 'rows' | 'json';

// Function types
type ValueTransformer = (value: unknown, header: string) => unknown;
type HeaderTransformer = (header: string) => string;
type RowFilter = (record: CsvRecord, rowIndex: number) => boolean;

// Data types
type CsvRecord = Record<string, string>;
type NestedObject = { [key: string]: NestedValue };
type NestedValue = string | number | boolean | Date | null | NestedObject | NestedValue[];

// Options interface
interface CsvParserOptions { /* ... */ }

🎯 Best Practices

• Choose the Right Method:

  • Use parseFileSync() for small files in scripts
  • Use parseFile() for web servers and async workflows
  • Use parseString() for API responses and testing
  • Use parseStream() for large files
  • Use CsvStreamParser for very large files or when you need to process records one at a time

• Use Appropriate Validation Mode:

  • Use 'ignore' when you trust the data source
  • Use 'warn' (default) during development
  • Use 'error' for strict validation in production

• Enable Auto-Parsing When Appropriate:

  const result = CsvParser.parseFileSync('./data.csv', {
    autoParseNumbers: true,
    autoParseBooleans: true,
    autoParseDates: true
  });
  

• Handle Errors Gracefully:

  import { CsvParseError, CsvValidationError } from '@cerios/csv-nested-json';
  
  try {
    const result = CsvParser.parseFileSync('./data.csv', {
      validationMode: 'error'
    });
  } catch (error) {
    if (error instanceof CsvValidationError) {
      console.error(`Row ${error.row}: expected ${error.expectedColumns} columns`);
    } else {
      throw error;
    }
  }
  

• Use Streaming for Large Files:

  // ✅ Good for very large files
  const parser = new CsvStreamParser({ autoParseNumbers: true });
  for await (const record of createReadStream('./huge.csv').pipe(parser)) {
    await processRecord(record);
  }
  
  // ❌ May cause memory issues with large files
  const result = CsvParser.parseFileSync('./huge.csv');
  

• Use Row Filtering for Memory Efficiency:

  // ✅ Filter during parsing - uses less memory
  const result = CsvParser.parseFileSync('./data.csv', {
    rowFilter: (record) => record.status === 'active'
  });
  
  // ❌ Filter after parsing - loads everything into memory first
  const all = CsvParser.parseFileSync('./data.csv');
  const filtered = all.filter(r => r.status === 'active');
  

• Specify Encoding for Non-UTF8 Files:

  const result = await CsvParser.parseFile('./data.csv', {
    encoding: 'latin1'
  });
  

• Use Consistent Column Headers:

  • Ensure the first column is always the identifier for grouping
  • Use consistent dot notation for nested structures
  • Keep header names descriptive and use headerTransformer for normalization

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

MIT © Ronald Veth - Cerios

🔗 Links

• GitHub Repository
• Issue Tracker
• NPM Package