Pandatech.FluentImporter

Fluent API for importing CSV and Excel data into .NET 8+ applications with customizable property mapping and validation.

Installation

dotnet add package PandaTech.FluentImporter

Quick Start

Define Your Model

public class FileData { public long Id { get; set; } public string Name { get; set; } public string Description { get; set; } public DateTime Date { get; set; } public string? Comment { get; set; } public DateTime CreatedAt { get; init; } public string CreatedBy { get; init; } }

Create Import Rules

public class FileDataImportRule : ImportRule<FileData> { public FileDataImportRule() { RuleFor(x => x.Name) .NotEmpty(); RuleFor(x => x.Description) .ReadFromColumn("Description text") .Default("No Description"); RuleFor(x => x.Date) .ReadFromColumn("Date") .Convert(DateTime.Parse); RuleFor(x => x.Comment) .ReadFromColumn("Comment"); RuleFor(x => x.Id) .ReadFromColumn("Id") .Convert(s => long.Parse(s)); RuleFor(x => x.CreatedAt) .WriteValue(DateTime.UtcNow); RuleFor(x => x.CreatedBy) .ReadFromModel(x => x.CreatedBy + " - Modified"); } }

Import Data

var importRule = new FileDataImportRule(); // From CSV file var data = importRule.ReadCsv("path/to/file.csv"); // From CSV stream using var stream = File.OpenRead("file.csv"); var data = importRule.ReadCsv(stream); // From Excel file var data = importRule.ReadXlsx("path/to/file.xlsx"); // From Excel stream using var stream = File.OpenRead("file.xlsx"); var data = importRule.ReadXlsx(stream); // From in-memory data var dict = new List<Dictionary<string, string>> { new() { ["Name"] = "John", ["Date"] = "2024-01-01" } }; var data = importRule.GetRecords(dict);

API Reference

Property Rules

Method	Description
ReadFromColumn(string)	Map to a different column name
NotEmpty()	Require non-empty value
Default(T)	Set default value if null/empty
Convert(Func<string, T>)	Custom converter function
Convert(Func<string, TModel, T>)	Converter with access to model instance
Validate(string)	Regex validation pattern
WriteValue(T)	Set a constant value
ReadFromModel(Func<TModel, T>)	Compute value from model

Supported Types

• Primitives: string, int, long, decimal, double, bool, etc.
• DateTime, DateTimeOffset, Guid
• Nullable<T> for all value types
• Enum types (case-insensitive)
• Custom types via Convert() method

Boolean Parsing

The library automatically handles multiple boolean representations:

• true/false
• 1/0
• yes/no

Error Handling

All import exceptions inherit from ImportException:

• EmptyFileImportException - File contains no data rows
• InvalidCellValueException - Cell value failed validation or conversion
• InvalidColumnValueException - Required column not found
• InvalidPropertyNameException - Property doesn't exist on model

Each exception includes:

• Descriptive error message
• Row number (when applicable)
• Column name
• Property name
• Raw value that failed

License

MIT