FilePrepper.CLI 0.3.0

FilePrepper

A powerful .NET library and CLI tool for CSV/tabular data processing. Process data files without writing code, or integrate into your .NET applications.

Quick Start

CLI Usage (No Coding Required)

# Normalize multiple columns
fileprepper normalize -i data.csv -o output.csv -c "Age,Salary,Score" -m MinMax

# Convert data types
fileprepper convert-type -i data.csv -o output.csv -c "Date:DateTime:yyyy-MM-dd,Age:Integer"

# Fill missing values
fileprepper fill-missing -i data.csv -o output.csv -c "Age,Salary" -m Mean

# Get help for any command
fileprepper normalize --help

Library Usage

var options = new NormalizeDataOption
{
    InputPath = "data.csv",
    OutputPath = "output.csv",
    TargetColumns = new[] { "Age", "Salary" },
    Method = NormalizationMethod.MinMax
};

var task = new NormalizeDataTask(logger);
await task.ExecuteAsync(new TaskContext(options));

Features

20+ Data Operations

• Manipulation: Add/Remove/Rename/Reorder columns, Column interactions
• Transformation: Type conversion, Date extraction, Normalization, Scaling, One-hot encoding
• Analysis: Aggregation, Statistics, Sampling
• Organization: Deduplication, Filtering, Merging
• Format: CSV/TSV/JSON/XML conversion, Excel support

Key Capabilities

✅ Multi-column processing - Process multiple columns in single command ✅ Type-safe - Compile-time checking for .NET integration ✅ CLI & Library - Use without coding or integrate programmatically ✅ Error handling - Flexible error strategies with logging ✅ Well-tested - 159 unit tests ensuring reliability

Installation

CLI Tool (Recommended)

# Install as global .NET tool
dotnet tool install -g FilePrepper.CLI

# Use anywhere
fileprepper --help
fileprepper normalize -i data.csv -o output.csv -c "Age,Salary" -m MinMax

Library (NuGet)

dotnet add package FilePrepper

See INSTALL.md for detailed installation instructions.

Documentation

• Quick Start Guide - Get running in 5 minutes
• CLI Reference - Complete command reference
• Common Scenarios - Real-world examples
• API Reference - Programmatic usage

Examples

Multi-Column Operations

# Process 3 columns simultaneously
fileprepper normalize -i sales.csv -o normalized.csv \
  -c "Price,Quantity,Revenue" -m MinMax --min 0 --max 1

Data Pipeline

# 1. Fill missing values
fileprepper fill-missing -i raw.csv -o step1.csv -c "Age" -m Mean

# 2. Normalize
fileprepper normalize -i step1.csv -o step2.csv -c "Age,Salary" -m MinMax

# 3. Convert format
fileprepper convert-format -i step2.csv -o final.json -f JSON

Programmatic Usage

// Configure multiple column normalization
var options = new NormalizeDataOption
{
    InputPath = "data.csv",
    OutputPath = "output.csv",
    TargetColumns = new[] { "Col1", "Col2", "Col3" },
    Method = NormalizationMethod.ZScore,
    IgnoreErrors = true
};

// Execute
var task = new NormalizeDataTask(logger);
var result = await task.ExecuteAsync(new TaskContext(options));

Requirements

• .NET 9.0 or later
• Cross-platform (Windows, Linux, macOS)