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pawel-up/csv: A CSV tools for the native web platform

Stop wrestling with CSVs! This lightweight, powerful TypeScript library makes parsing CSV data a breeze. Whether you're dealing with files or strings, complex data types, or custom formats, csv has you covered.

• Effortless Parsing: Parse CSV data from files or strings with just a few lines of code.
• Automatic Data Type Detection: Intelligently detects strings, numbers (integers and decimals), booleans, dates, times, and datetimes. No more manual type casting!
• Flexible Output: Get your data as an array of arrays (rows and cells) or an array of objects (rows with named columns).
• Header Row Support: Easily handle CSVs with or without header rows.
• Customizable: Fine-tune the parsing process with options for delimiters, quote characters, comment characters, encoding, and date/time formats.
• Robust Error Handling: Gracefully handles malformed CSV data and provides informative error messages.
• TypeScript-First: Built from the ground up with TypeScript, providing excellent type safety and autocompletion.
• Lightweight: No dependencies and a small footprint, making it ideal for any project.
• Handles comments: Skips lines that start with a comment character.
• Handles max rows: Can limit the number of rows to parse.
• Well tested: The library is well tested and has a high code coverage.

npm install @pawel-up/csv

or

import { CSVParser } from '@pawel-up/csv';

const csvString = "name,age,city\nJohn Doe,30,New York\nJane Smith,25,Los Angeles";
const parser = new CSVParser();

async function parseCSV() {
  const result = await parser.parse(csvString);
  console.log(result);
  // Output:
  // {
  //   format: [
  //     { name: 'name', type: 'string', index: 0 },
  //     { name: 'age', type: 'number', format: 'integer', index: 1 },
  //     { name: 'city', type: 'string', index: 2 }
  //   ],
  //   header: ['name', 'age', 'city'],
  //   values: [
  //     ['John Doe', 30, 'New York'],
  //     ['Jane Smith', 25, 'Los Angeles']
  //   ]
  // }
}

parseCSV();

For large CSV files, you can use the streaming functionality to process data in chunks:

import { CSVParser } from '@pawel-up/csv';

async function streamCSV() {
  // Assuming you have a file input element:
  const fileInput = document.getElementById('fileInput') as HTMLInputElement;
  const file = fileInput.files[0];

  const parser = new CSVParser();
  const stream = await parser.streamFile(file);

  const reader = stream.getReader();
  while (true) {
    const { done, value } = await reader.read();
    if (done) {
      break;
    }
    console.log('Chunk:', value);
    // Process each chunk of data here
    // Example:
    // value.values.forEach(row => {
    //   console.log(row);
    // });
  }
}

streamCSV();

Mind that the headers structure may change witch every chunk as more data becomes available to check for data types.

import { CSVParser, type ParseResult } from '@pawel-up/csv'

const response = await fetch("./data.csv")
const textStream = response.body!.pipeThrough(new TextDecoderStream())
const parser = new CSVParser()
const stream = parser.stream(textStream)
const reader = stream.getReader()
const chunks: ParseResult[] = []
while (true) {
  const { done, value } = await reader.read()
  if (done) break
  chunks.push(value)
}

The CSVParser constructor accepts an optional CSVOptions object to customize the parsing behavior:

interface CSVOptions {
  delimiter?: string; // Default: ','
  quote?: string; // Default: '"'
  comment?: string; // Default: ''
  header?: boolean; // Default: true
  encoding?: string; // Default: 'utf8'
  maxRows?: number; // Default: Infinity
}

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