fileprepper-cli 0.4.0

FilePrepper

A powerful .NET CLI tool for data preprocessing without coding. Perfect for ML data preparation, ETL pipelines, and data analysis workflows.

🚀 Quick Start

Installation

# Install as global .NET tool
dotnet tool install -g fileprepper-cli

# Verify installation
fileprepper --version

Basic Usage

# Normalize numeric columns
fileprepper normalize-data --input data.csv --output normalized.csv \
  --columns "Age,Salary,Score" --method MinMax

# Fill missing values
fileprepper fill-missing-values --input data.csv --output filled.csv \
  --columns "Age,Salary" --method Mean

# Filter rows
fileprepper filter-rows --input sales.csv --output filtered.csv \
  --conditions "Revenue:GreaterThan:1000,Region:Equals:North"

# Convert file formats
fileprepper file-format-convert --input data.csv --output data.json \
  --format JSON

# Get help
fileprepper --help
fileprepper <command> --help

📦 Supported Formats

Process data in multiple formats:

• CSV (Comma-Separated Values)
• TSV (Tab-Separated Values)
• JSON (JavaScript Object Notation)
• XML (Extensible Markup Language)
• Excel (XLSX/XLS files)

🛠️ Available Commands (20+)

Data Transformation

• normalize-data - Normalize columns (MinMax, ZScore)
• scale-data - Scale numeric data (StandardScaler, MinMaxScaler, RobustScaler)
• one-hot-encoding - Convert categorical to binary columns
• data-type-convert - Convert column data types
• date-extraction - Extract date features (Year, Month, Day, DayOfWeek)

Data Cleaning

• fill-missing-values - Fill missing data (Mean, Median, Mode, Forward, Backward, Constant)
• drop-duplicates - Remove duplicate rows
• value-replace - Replace values in columns

Column Operations

• add-columns - Add new calculated columns
• remove-columns - Delete unwanted columns
• rename-columns - Rename column headers
• reorder-columns - Change column order
• column-interaction - Create interaction features

Data Analysis

• basic-statistics - Calculate statistics (Mean, Median, StdDev, ZScore)
• aggregate - Group and aggregate data
• filter-rows - Filter rows by conditions

Data Organization

• merge - Combine multiple files (Horizontal/Vertical merge)
• data-sampling - Sample rows (Random, Stratified, Systematic)
• file-format-convert - Convert between formats

Feature Engineering

• create-lag-features - Create time-series lag features

💡 Common Use Cases

Data Cleaning Pipeline

# 1. Remove unnecessary columns
fileprepper remove-columns --input raw.csv --output step1.csv \
  --columns "Debug,TempCol,Notes"

# 2. Drop duplicates
fileprepper drop-duplicates --input step1.csv --output step2.csv \
  --columns "Email" --keep First

# 3. Fill missing values
fileprepper fill-missing-values --input step2.csv --output step3.csv \
  --columns "Age,Salary" --method Mean

# 4. Normalize numeric columns
fileprepper normalize-data --input step3.csv --output clean.csv \
  --columns "Age,Salary,Score" --method MinMax

ML Feature Engineering

# Extract date features
fileprepper date-extraction --input orders.csv --output features1.csv \
  --columns "OrderDate" --features Year,Month,DayOfWeek

# Create lag features for time series
fileprepper create-lag-features --input sales.csv --output features2.csv \
  --group-by ProductID --lag-columns Revenue \
  --periods 1,2,3,7 --sort-by Date

# One-hot encode categorical variables
fileprepper one-hot-encoding --input features2.csv --output features3.csv \
  --columns "Category,Region"

# Create interaction features
fileprepper column-interaction --input features3.csv --output final.csv \
  --column-pairs "Price*Quantity,Age*Income"

Format Conversion

# CSV to JSON
fileprepper file-format-convert --input data.csv --output data.json --format JSON

# Excel to CSV
fileprepper file-format-convert --input report.xlsx --output report.csv --format CSV

# CSV to XML
fileprepper file-format-convert --input data.csv --output data.xml --format XML

Data Analysis

# Calculate statistics
fileprepper basic-statistics --input data.csv --output stats.csv \
  --columns "Age,Salary,Score" --statistics Mean,Median,StdDev,ZScore

# Aggregate by group
fileprepper aggregate --input sales.csv --output summary.csv \
  --group-by "Region,Category" --agg-columns "Revenue:Sum,Quantity:Mean"

# Sample data
fileprepper data-sampling --input large.csv --output sample.csv \
  --method Random --sample-size 1000

🔧 Programmatic Usage

FilePrepper can also be used as a .NET library:

dotnet add package FilePrepper

using FilePrepper.Tasks.NormalizeData;
using Microsoft.Extensions.Logging;

var options = new NormalizeDataOption
{
    InputPath = "data.csv",
    OutputPath = "normalized.csv",
    TargetColumns = new[] { "Age", "Salary", "Score" },
    Method = NormalizationMethod.MinMax,
    MinValue = 0,
    MaxValue = 1
};

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

See API Reference for detailed programmatic usage.

📖 Documentation

• Quick Start Guide - Get started in 5 minutes
• CLI Guide - Complete command reference
• Common Scenarios - Real-world use cases
• API Reference - Programmatic usage
• Installation Guide - Detailed installation

For more documentation, see the docs/ directory.

🎯 Use Cases

• Machine Learning - Prepare datasets for training (normalization, encoding, feature engineering)
• Data Analysis - Clean and transform data for analysis
• ETL Pipelines - Extract, transform, and load data workflows
• Data Migration - Convert between formats and clean legacy data
• Automation - Script data processing without custom code

📋 Requirements

• .NET 9.0 or later
• Cross-platform - Windows, Linux, macOS
• No coding required - Command-line only (or use as library)

🤝 Contributing

Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 Links

• NuGet Package: https://www.nuget.org/packages/fileprepper-cli
• GitHub Repository: https://github.com/iyulab/FilePrepper
• Issues: https://github.com/iyulab/FilePrepper/issues
• Documentation: docs/
• Changelog: CHANGELOG.md

Made with ❤️ by iyulab | ML Data Preprocessing Tool - No Coding Required