Now that TinyCsvParser is finally updated, we can add all kinds of features, that had been missing in previous iterations of the library. The source code and documentation for TinyCsvParser can be found at:

• https://github.com/TinyCsvParser/TinyCsvParser

One of things, that had been missing in TinyCsvParser had been dynamic mappings, so you don't need to create dedicated classes to parse some CSV data.

Table of contents

What is TinyCsvParser?

TinyCsvParser is a high-performance CSV parsing library for .NET. This documentation explains the usage, configuration, and extensibility of the library through practical examples.

Upgrading from a previous version? Check out the Migration Guide from 2.x to 3.x

Dynamic Mapping (Dictionary & ExpandoObject)

When the CSV schema is only known at runtime, or you want to avoid creating dedicated classes for simple scripts, you can parse rows directly into dynamic structures (Dictionary<string, object?> or ExpandoObject).

For performance it's maybe better to map to a Dictionary, as it avoids Dynamic Language Runtime overhead.

Inline Configuration

Use the static factory methods on the CsvParser class. The schema is configured inline using a delegate.

using TinyCsvParser;

CsvOptions options = new(Delimiter: ';', QuoteChar: '"', EscapeChar: '"', SkipHeader: false);

// Create the parser and configure the schema in one go
var parser = CsvParser.CreateDictionaryParser(options, schema => 
{
    schema.Add<int>("Id");       // Resolves Int32Converter automatically
    schema.Add<double>("Price"); // Resolves DoubleConverter automatically
});

foreach (var result in parser.ReadFromFile("products.csv"))
{
    if (result.IsSuccess)
    {
        Dictionary<string, object?> row = result.Result;
        Console.WriteLine($"Item {row["Id"]} costs {row["Price"]}");
    }
}

Note: Use CsvParser.CreateExpandoParser(...) if you prefer to access fields via the dynamic keyword like row.Id.

Explicit Converters & Custom Providers

While Add<T> is the most convenient method, you can pass explicit converter instances if you need special configurations (e.g., date formats).

var parser = CsvParser.CreateDictionaryParser(options, schema => 
{
    schema.Add<int>("Id");
    schema.Add("BirthDate", new DateTimeConverter("yyyy-MM-dd")); // Explicit Converter
});

Fallback Behavior

Any column present in the CSV header that is not mapped in your CsvSchema will automatically be parsed as a raw string. This prevents data loss while maintaining strict typing for the columns you care about.