Working with CSV in C# (.NET): Libraries and Examples

Working with CSV in C# (.NET): Tool Overview and Examples

CSV (Comma-Separated Values) is one of the most common formats for exchanging tabular data. In the C# (.NET) ecosystem, there are several reliable ways to work with this format: from built-in tools to powerful third-party libraries. In this article, we will review the main approaches and best practices.

Why You Shouldn't Write a CSV Parser Manually?

The CSV format seems simple at first glance, but in practice it contains many nuances: quote escaping, commas inside fields, different delimiters, newline characters in values. Using ready-made libraries saves you headaches and edge case handling.

CsvHelper Library — The Gold Standard

CsvHelper is the most popular library for working with CSV in .NET. It supports mapping to POCO classes, streaming reads, delimiter and encoding configuration.

Installation via NuGet:

Install-Package CsvHelper

Example of Reading CSV into a List of Objects

using CsvHelper;using System.Globalization;

public class User{    public int Id { get; set; }    public string Name { get; set; }    public string Email { get; set; }}

var users = new List<User>();using (var reader = new StreamReader("users.csv"))using (var csv = new CsvReader(reader, CultureInfo.InvariantCulture)){    users = csv.GetRecords<User>().ToList();}

Example of Writing CSV from a Collection

using (var writer = new StreamWriter("output.csv"))using (var csv = new CsvWriter(writer, CultureInfo.InvariantCulture)){    csv.WriteRecords(users);}

Built-in Parser: TextFieldParser (Microsoft.VisualBasic)

If you don't want to add external dependencies, .NET has a built-in TextFieldParser from the Microsoft.VisualBasic.FileIO namespace. It is available in .NET Framework and .NET Core/.NET 5+ (requires installing the Microsoft.VisualBasic package).

Example of Using TextFieldParser

using Microsoft.VisualBasic.FileIO;

using (var parser = new TextFieldParser("data.csv")){    parser.TextFieldType = FieldType.Delimited;    parser.SetDelimiters(",");    parser.HasFieldsEnclosedInQuotes = true;

    while (!parser.EndOfData)    {        string[] fields = parser.ReadFields();        Console.WriteLine(string.Join(" | ", fields));    }}

Pros: no third-party libraries required. Cons: less flexible than CsvHelper, no automatic class mapping.

Working with Large Files: Streaming Processing

When working with CSV files larger than 100 MB, it is important to avoid loading the entire file into memory. Both libraries support streaming processing:

• CsvHelper — use GetRecords<T>() in a foreach loop without calling .ToList().
• TextFieldParser — reads line by line, which is also efficient.

Example of Streaming Reading with CsvHelper

using (var reader = new StreamReader("large.csv"))using (var csv = new CsvReader(reader, CultureInfo.InvariantCulture)){    foreach (var record in csv.GetRecords<User>())    {        ProcessUser(record); // process one by one    }}

Error Handling and Data Validation

When importing CSV, data may be invalid. CsvHelper allows configuring error handling through events:

csv.Configuration.BadDataFound = context =>{    Console.WriteLine($"Error in row {context.RawRow}: {context.RawRecord}");};

You can also use attributes for field validation:

public class Product{    [Name("ProductId")]    public int Id { get; set; }        [Required]    public string Nam