CSV Library for Dart & Flutter #

csv_plus is a fast, complete, zero-dependency Dart library for parsing, encoding, streaming, querying, and validating CSV data. It works in plain Dart and in Flutter apps, on the VM, Web (JS & WASM), and mobile. csv_plus reads and writes RFC 4180 CSV with automatic type inference, a DataFrame-style table layer, schema validation, and constant-memory streaming, and it is the fastest general-purpose CSV package for Dart on every workload we measure.

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Overview #

csv_plus parses and generates comma-separated-values (CSV) text, along with tab-separated (TSV), pipe-delimited, and custom-delimiter formats. It decodes CSV into typed Dart values, encodes rows back to RFC 4180 output with correct quoting, streams large files with constant memory, and offers a table layer for filtering, sorting, grouping, aggregating, and validating tabular data. In short, it is a complete CSV serialization and deserialization toolkit for data processing and file handling in Dart and Flutter.

What you can do with it:

• Decode CSV strings and files into typed rows (int, double, bool, String, null), or into a queryable table.
• Encode rows, maps, and tables back to CSV, TSV, pipe-delimited, or Excel-flavored output with automatic quoting.
• Stream very large CSV files row by row with a chunked, backpressure-aware transformer that never buffers the whole input.
• Query, sort, group, aggregate, transform, and schema-validate tabular data with a DataFrame-style API.

Performance #

csv_plus is built for throughput: a byte-level (codeUnits) batch parser with no regex and no string allocation in the hot loop, first-byte type detection, and a per-call StringBuffer encoder. Type inference is guarded so speed never costs correctness.

It is the fastest general-purpose CSV package for Dart on every workload, on both JIT and AOT. The numbers below are a median of 5 runs against csv 8.0.0 and serial_csv 0.5.2, on 200k rows x 10 cols plain (14.3 MB) and 100k x 10 quote-heavy (18.4 MB), on the same machine:

serial_csv reads and writes only its own strict format rather than general RFC 4180 CSV, so its decode rows (marked own fmt) are not directly comparable. The full tables with AOT numbers, the seeded data generators, and an edge-case comparison battery live in benchmark/compare/. Timings vary by hardware, so reproduce them on your own machine:

cd benchmark/compare && dart pub get && dart run bench.dart

Table of contents #

• Key features
• Limitations
• Roadmap
• Error handling
• Example
• Other useful links
• Installation
• Getting started
  • Encode and decode
  • Header-aware rows
  • Type inference and typed decoders
  • Query and transform with CsvTable
  • Aggregate and group
  • Stream large files
  • Read and write files
  • Configuration and presets
  • Strict mode
  • Schema validation
  • Maps and two-column CSV
  • dart:convert integration
• csv_plus vs csv
• FAQ
• Support and feedback
• About

Key features #

Everything you need to read, write, stream, and analyze CSV, on every Dart & Flutter platform.

Limitations #

• ❌ Comment-line skipping (#-prefixed rows)
• ❌ Row windowing (skipRows / maxRows)
• ❌ Per-column type coercion on decode (schemas validate, they do not coerce)

Roadmap #

What ships next is driven by user requests on the issue tracker:

• ⬜ Comment-line skipping (#-prefixed rows)
• ⬜ Row windowing (skipRows / maxRows)
• ⬜ Per-column type coercion driven by CsvSchema

Shipped milestones are in the changelog.

Error handling #

Malformed-but-openable CSV degrades gracefully by default: text after a closing quote is appended to the field (Excel behavior), and an unterminated quote consumes the rest of the input. Pass strict: true to turn those into a typed, catchable CsvParseException that carries the row, column, and offset:

try {
  final rows = CsvCodec(CsvConfig(strict: true)).decode('"a"x,b');
} on CsvParseException catch (e) {
  print('Parse error at row ${e.row}, column ${e.column}: ${e.message}');
}

The typed decoders (decodeIntegers, decodeDoubles, decodeBooleans) throw CsvParseException on a cell they cannot convert rather than inventing a 0 or false. Schema violations throw CsvValidationException.

Example #

A complete, runnable set of samples lives in the example/ directory (basic, table, streaming, file IO, and advanced). Clone the repository and run them, or copy any snippet from Getting started below.

Other useful links #

• API reference
• Source code on GitHub
• Changelog
• Issue tracker

Installation #

dart pub add csv_plus
# or, in a Flutter app:
flutter pub add csv_plus

Then import it:

import 'package:csv_plus/csv_plus.dart';

Getting started #

Encode and decode #

final codec = CsvCodec();

// Encode.
final csv = codec.encode([
  ['name', 'age', 'score'],
  ['Alice', 30, 95.5],
  ['Bob', 25, 88.0],
]);

// Decode: types are inferred automatically.
final rows = codec.decode(csv);
// rows[1] == ['Alice', 30, 95.5]  (String, int, double)

Header-aware rows #

final people = codec.decodeWithHeaders(csv);
print(people.first['name']); // Alice
print(people.first['age']);  // 30  (int, not String)

Type inference and typed decoders #

// Inference is guarded so identifier-like data is not corrupted.
codec.decode('id,qty\n007,3');
// ['id', 'qty'], ['007', 3]  (007 stays a String; 3 becomes an int)

// Or force a whole grid to one type. These throw on a bad cell instead of
// inventing a value; pass emptyAs to fill blanks.
codec.decodeStrings(csv);            // List<List<String>>
codec.decodeIntegers('1,2\n3,4');    // List<List<int>>
codec.decodeDoubles('1.5,2.5');      // List<List<double>>
codec.decodeBooleans('true,0');      // List<List<bool>>  (true/false/1/0)
codec.decodeFlexible('  a , b ');    // lenient: trims, recovers bad quotes

Query and transform with CsvTable #

final table = CsvTable.parse('name,age,city\nAlice,30,NYC\nBob,25,LA\nEve,35,NYC');

// Filter (returns a new table).
final adults = table.where((row) => (row['age'] as int) >= 30);

// Sort in place (stable).
table.sortBy('age');

// ...or get a sorted copy without touching the source.
final byAge = table.sortedBy('age');

// Export.
print(table.toCsv());
print(table.toFormattedString()); // pretty-printed aligned table

Aggregate and group #

print(table.avg('age'));   // 30.0
print(table.sum('age'));   // 90
print(table.max('age'));   // 35

// Group rows by a column value into sub-tables.
final byCity = table.groupBy('city'); // {NYC: CsvTable, LA: CsvTable}

Stream large files #

import 'package:csv_plus/io.dart';

// Constant memory, any file size.
await for (final row in CsvFile.stream('huge.csv')) {
  process(row);
}

Any string or byte stream works, with backpressure handled for you:

final rows = codec.decoder.bindBytes(byteStream); // Stream<List<int>>

Read and write files #

import 'package:csv_plus/io.dart';

final table = await CsvFile.read('data.csv');
await CsvFile.write('out.csv', table);
await CsvFile.append('out.csv', [['Zoe', 41]]);

Configuration and presets #

final excel = CsvCodec.excel(); // ';' delimiter + UTF-8 BOM
final tsv = CsvCodec.tsv();      // tab-separated
final pipe = CsvCodec.pipe();    // pipe-separated

// Or configure fully.
final custom = CsvCodec(CsvConfig(
  fieldDelimiter: '::',
  quoteMode: QuoteMode.always,
  skipEmptyLines: true,
));

Strict mode #

// Throw on structurally malformed input instead of recovering.
final strict = CsvCodec(CsvConfig(strict: true));
strict.decode('"unterminated'); // throws CsvParseException

Schema validation #

final schema = CsvSchema(columns: [
  CsvColumnDef(name: 'email', type: String, required: true, pattern: r'@'),
  CsvColumnDef(name: 'age', type: int, nullable: false),
]);

final errors = table.validate(schema);   // List<CsvValidationException>
final ok = table.conformsTo(schema);      // bool

Maps and two-column CSV #

codec.encodeMap({'host': 'localhost', 'port': 8080});
codec.decodeMap('host,localhost\nport,8080'); // {host: localhost, port: 8080}

dart:convert integration #

final adapter = codec.asCodec(); // Codec<List<List<dynamic>>, String>
final rows = adapter.decode('a,b\n1,2');
final piped = adapter.fuse(utf8); // fuse with other codecs

csv_plus vs csv #

csv_plus and the csv package cover similar ground; csv_plus adds speed, a table layer, and stricter correctness.

Numbers are from the reproducible benchmark above.

FAQ #

Is csv_plus a drop-in for the csv package? No, the APIs differ, but the concepts map directly (codec, typed decode, headers, streaming). Most migrations are a small, mechanical change.

Which platforms are supported? Dart VM, Web (both JavaScript and WebAssembly), and mobile (Android & iOS) via Flutter, plus desktop. It is pure Dart with no dart:io in the core path.

Does it handle large files without running out of memory? Yes. The streaming decoder and encoder process input in chunks with real backpressure, so memory stays constant regardless of file size.

Will type inference corrupt my ids or codes? No. Values with leading zeros, a leading plus, surrounding whitespace, or more than 15 digits stay strings, and quoted fields are never inferred, on every platform including the web.

Does it support TSV, pipe-delimited, and Excel CSV? Yes, via CsvCodec.tsv(), CsvCodec.pipe(), CsvCodec.excel(), or a custom CsvConfig with any single or multi-character delimiter.

Support and feedback #

• Found a bug or want a feature? Open an issue on the issue tracker.
• Questions and ideas are welcome via GitHub Discussions.
• Pull requests are welcome; see the repository for contribution guidelines.

About #

csv_plus is an open-source, MIT-licensed, zero-dependency CSV library for Dart and Flutter, built around a byte-level parser and a chunked streaming transformer for speed and low memory on large files.

csv_plus is created and owned by Nurullah Al Masum.

Contributors #

csv_plus grows with its community; every contributor is listed here:

Want to help? Pull requests are welcome; see Support and feedback.