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How Can I Efficiently Parse CSV Files in C#?

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Release: 2025-02-02 01:56:11
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How Can I Efficiently Parse CSV Files in C#?

C# CSV File Analysis: Full Guide

Analyze the CSV (comma segmental value) file in C#is a common task. Although you can build a parser yourself, fortunately, the .NET framework provided a built -in solution.

The default CSV parser in the c#

The default CSV parser in C#is , which is located in the

program concentration. To use it, add the following reference to your project:

TextFieldParser Microsoft.VisualBasic Use TextFieldparser to perform CSV parsing

<code>添加 > 引用... > Microsoft.VisualBasic</code>
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After adding a reference, you can use the following code to analyze the CSV file:

The disadvantages of reading CSV using ODBC/OLE DB

<code class="language-csharp">using System.IO;
using Microsoft.VisualBasic.FileIO;

public static class CsvParser
{
    public static IEnumerable<string[]> Parse(string filePath)
    {
        using (var parser = new TextFieldParser(filePath))
        {
            parser.TextFieldType = FieldType.Delimited;
            parser.SetDelimiters(",");
            while (!parser.EndOfData)
            {
                yield return parser.ReadFields();
            }
        }
    }
}</code>
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Although you can read the CSV file through the text driver in ODBC/OLE DB, there are some shortcomings that make it less ideal:

Compared with the special CSV library, the performance overhead is greater

Limited function (for example, no built -in support for the title line)

    Poor support and consistency implementation
  • Read CSV
  • Read the CSV file according to the list name (using the first line of records as the title). You can use the CSVHELPER library. This is an excellent third -party option for processing CSV files.

Summary

Class and CSVHELPER libraries provide efficient and comprehensive solutions for parsing CSV files in C#, including the ability to process the title line and read the list by name. By using these tools, you can simplify the CSV analysis task and effectively process data.

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