# CSV Data Cleaner
A powerful, self-contained tool for cleaning CSV data using industry-standard Python libraries with **AI-powered intelligent suggestions and automatic cleaning capabilities**.
## 🚀 Key Features
### **AI-Powered Features**
- **🤖 AI-Powered Automatic Cleaning**: Execute AI suggestions automatically with `ai-clean` command
- **🧠 Intelligent Suggestions**: Get AI-powered cleaning recommendations with `ai-suggest` command
- **📊 Data Analysis**: AI-powered data analysis and insights with `ai-analyze` command
- **🎯 Learning System**: AI learns from your feedback to improve suggestions over time
- **⚡ Multi-Provider Support**: OpenAI, Anthropic, and local LLM support
### **Core Cleaning Capabilities**
- **🔧 Multiple Libraries**: pandas, pyjanitor, feature-engine, dedupe, missingno
- **⚙️ 30+ Operations**: Remove duplicates, handle missing values, clean text, fix dates, etc.
- **📈 Performance Optimization**: Parallel processing, memory management, chunked processing
- **📊 Data Validation**: Schema validation, data quality assessment, comprehensive reporting
- **🎨 Visualization**: Data quality heatmaps, missing data analysis, correlation matrices
## 🛠️ Installation
### Quick Install
```bash
pip install csv-cleaner
```
### From Source
```bash
git clone https://github.com/your-repo/csv-cleaner.git
cd csv-cleaner
pip install -e .
```
## 🚀 Quick Start
### **AI-Powered Automatic Cleaning** (NEW!)
```bash
# Automatic cleaning with AI suggestions
csv-cleaner ai-clean input.csv output.csv
# Auto-confirm all suggestions
csv-cleaner ai-clean input.csv output.csv --auto-confirm
# Preview execution plan without modifying files
csv-cleaner ai-clean input.csv output.csv --dry-run
# Limit number of suggestions
csv-cleaner ai-clean input.csv output.csv --max-suggestions 10
```
### **AI-Powered Suggestions**
```bash
# Get AI-powered cleaning suggestions
csv-cleaner ai-suggest input.csv
# Get suggestions with specific analysis
csv-cleaner ai-suggest input.csv --output suggestions.json
```
### **AI-Powered Data Analysis**
```bash
# Get comprehensive data analysis
csv-cleaner ai-analyze input.csv
# Save analysis to file
csv-cleaner ai-analyze input.csv --output analysis.json
```
### **Traditional Cleaning**
```bash
# Clean with specific operations
csv-cleaner clean input.csv output.csv --operations "remove_duplicates,fill_missing"
# Interactive mode
csv-cleaner clean input.csv output.csv --interactive
# Performance optimized
csv-cleaner clean input.csv output.csv --parallel --chunk-size 10000
```
## 🤖 AI Configuration
### **Setup AI Providers**
```bash
# Configure OpenAI
csv-cleaner ai-configure set --provider openai --api-key sk-...
# Configure Anthropic
csv-cleaner ai-configure set --provider anthropic --api-key sk-ant-...
# Show current configuration
csv-cleaner ai-configure show
# Validate configuration
csv-cleaner ai-configure validate
```
### **AI Features Overview**
#### **AI-Powered Automatic Cleaning (`ai-clean`)**
- **Automatic Execution**: AI generates and executes cleaning suggestions
- **Execution Planning**: Shows detailed execution plan with confidence levels
- **User Control**: Choose between automatic execution and manual confirmation
- **Dry-Run Mode**: Preview changes without modifying files
- **Learning Integration**: AI learns from execution results
#### **AI-Powered Suggestions (`ai-suggest`)**
- **Intelligent Analysis**: AI analyzes data and suggests optimal cleaning operations
- **Confidence Scoring**: Each suggestion includes confidence level and reasoning
- **Library Selection**: AI recommends the best library for each operation
- **Impact Assessment**: Estimates the impact of each suggestion
#### **AI-Powered Analysis (`ai-analyze`)**
- **Comprehensive Profiling**: Detailed data quality assessment
- **Pattern Recognition**: Identifies data patterns and anomalies
- **Recommendation Engine**: Suggests cleaning strategies based on analysis
- **Exportable Reports**: Save analysis results for further review
## 📋 Available Operations
### **Basic Data Cleaning (Pandas)**
- `remove_duplicates` - Remove duplicate rows
- `fill_missing` - Fill missing values with various strategies
- `drop_missing` - Remove rows/columns with missing values
- `clean_text` - Clean and normalize text data
- `fix_dates` - Convert and standardize date formats
- `convert_types` - Convert data types automatically
- `rename_columns` - Rename columns
- `drop_columns` - Remove unwanted columns
- `select_columns` - Select specific columns
### **Advanced Data Cleaning (PyJanitor)**
- `clean_names` - Clean column names
- `remove_empty` - Remove empty rows/columns
- `fill_empty` - Fill empty values
- `handle_missing` - Advanced missing value handling
- `remove_constant_columns` - Remove columns with constant values
- `remove_columns_with_nulls` - Remove columns with null values
- `coalesce_columns` - Combine multiple columns
### **Feature Engineering (Feature-Engine)**
- `advanced_imputation` - Advanced missing value imputation
- `categorical_encoding` - Encode categorical variables
- `outlier_detection` - Detect and handle outliers
- `variable_selection` - Select relevant variables
- `data_transformation` - Apply data transformations
- `missing_indicator` - Create missing value indicators
### **Missing Data Analysis (MissingNo)**
- `missing_matrix` - Generate missing data matrix visualization
- `missing_bar` - Generate missing data bar chart
- `missing_heatmap` - Generate missing data heatmap
- `missing_dendrogram` - Generate missing data dendrogram
- `missing_summary` - Generate missing data summary
### **ML-Based Deduplication (Dedupe)**
- `dedupe` - ML-based deduplication with fuzzy matching
## 📊 Examples
### **Example 1: AI-Powered Automatic Cleaning**
```bash
# Clean messy data automatically
csv-cleaner ai-clean messy_data.csv cleaned_data.csv --auto-confirm
```
**Output:**
```
🤖 AI-Powered Data Cleaning
===========================
📊 Data Analysis Complete
- Rows: 10,000 | Columns: 15
- Missing values: 1,250 (8.3%)
- Duplicates: 150 (1.5%)
- Data quality score: 78%
🎯 AI Suggestions Generated (5 suggestions)
1. Remove duplicates (confidence: 95%)
2. Fill missing values with median (confidence: 88%)
3. Clean column names (confidence: 92%)
4. Convert date columns (confidence: 85%)
5. Handle outliers in 'price' column (confidence: 76%)
📋 Execution Plan
================
1. clean_names (pandas) - Clean column names
2. remove_duplicates (pandas) - Remove 150 duplicate rows
3. fill_missing (pandas) - Fill 1,250 missing values
4. fix_dates (pandas) - Convert date columns
5. handle_outliers (feature-engine) - Handle price outliers
🚀 Executing AI suggestions...
████████████████████████████████████████ 100%
✅ Successfully executed 5 operations
📊 Results: 9,850 rows → 9,700 rows (150 duplicates removed)
💾 Saved to: cleaned_data.csv
```
### **Example 2: AI-Powered Suggestions**
```bash
csv-cleaner ai-suggest data.csv
```
**Output:**
```
🤖 AI-Powered Cleaning Suggestions
==================================
📊 Data Analysis
- Dataset: 5,000 rows × 12 columns
- Quality issues detected: Missing values, inconsistent dates, duplicates
🎯 Recommended Operations:
1. **Remove Duplicates** (Confidence: 94%)
- Library: pandas
- Impact: Remove ~50 duplicate rows
- Reasoning: Found exact duplicates in customer data
2. **Fill Missing Values** (Confidence: 89%)
- Library: pandas
- Strategy: Forward fill for dates, median for numeric
- Impact: Fill 200 missing values
3. **Fix Date Columns** (Confidence: 87%)
- Library: pandas
- Columns: 'order_date', 'ship_date'
- Impact: Standardize date formats
4. **Clean Column Names** (Confidence: 92%)
- Library: pyjanitor
- Impact: Standardize naming convention
5. **Handle Outliers** (Confidence: 76%)
- Library: feature-engine
- Column: 'amount'
- Impact: Cap extreme values
```
## 🔧 Configuration
### **Performance Settings**
```bash
# Set memory limit
csv-cleaner config set performance.memory_limit 4.0
# Enable parallel processing
csv-cleaner config set performance.parallel_processing true
# Set chunk size
csv-cleaner config set performance.chunk_size 5000
```
### **AI Settings**
```bash
# Set default AI provider
csv-cleaner config set ai.default_provider openai
# Set suggestion confidence threshold
csv-cleaner config set ai.confidence_threshold 0.7
# Enable learning mode
csv-cleaner config set ai.learning_enabled true
```
## 📈 Performance Features
- **Parallel Processing**: Multi-core data processing
- **Memory Management**: Efficient memory usage for large datasets
- **Chunked Processing**: Process large files in chunks
- **Progress Tracking**: Real-time progress monitoring
- **Performance Monitoring**: Track processing times and resource usage
## 🧪 Testing
```bash
# Run all tests
pytest
# Run with coverage
pytest --cov=csv_cleaner
# Run specific test categories
pytest tests/unit/
pytest tests/integration/
```
## 🚀 Deployment
### **PyPI Deployment**
The project includes automated deployment scripts for PyPI:
```bash
# Setup basic version
python scripts/setup-pypi.py
# Deploy to TestPyPI
python scripts/deploy-pypi.py --test
# Deploy to production PyPI
python scripts/deploy-pypi.py --version 1.0.0
```
### **Deployment Features**
- ✅ Automated testing and validation
- ✅ Safety checks and prerequisites verification
- ✅ Package building and quality checks
- ✅ Version management and tagging
- ✅ Release notes generation
For detailed deployment instructions, see [scripts/deployment-guide.md](scripts/deployment-guide.md).
## 📚 Documentation
- [User Guide](docs/user-guide.md)
- [API Reference](docs/api-reference.md)
- [Configuration Guide](docs/configuration.md)
- [AI Features Guide](docs/ai-features.md)
- [Performance Tuning](docs/performance.md)
## 🤝 Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🆘 Support
- **Documentation**: [docs/](docs/)
- **Issues**: [GitHub Issues](https://github.com/your-repo/csv-cleaner/issues)
- **Discussions**: [GitHub Discussions](https://github.com/your-repo/csv-cleaner/discussions)
---
**Made with ❤️ for data scientists and analysts**
Raw data
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"description": "# CSV Data Cleaner\n\nA powerful, self-contained tool for cleaning CSV data using industry-standard Python libraries with **AI-powered intelligent suggestions and automatic cleaning capabilities**.\n\n## \ud83d\ude80 Key Features\n\n### **AI-Powered Features**\n- **\ud83e\udd16 AI-Powered Automatic Cleaning**: Execute AI suggestions automatically with `ai-clean` command\n- **\ud83e\udde0 Intelligent Suggestions**: Get AI-powered cleaning recommendations with `ai-suggest` command\n- **\ud83d\udcca Data Analysis**: AI-powered data analysis and insights with `ai-analyze` command\n- **\ud83c\udfaf Learning System**: AI learns from your feedback to improve suggestions over time\n- **\u26a1 Multi-Provider Support**: OpenAI, Anthropic, and local LLM support\n\n### **Core Cleaning Capabilities**\n- **\ud83d\udd27 Multiple Libraries**: pandas, pyjanitor, feature-engine, dedupe, missingno\n- **\u2699\ufe0f 30+ Operations**: Remove duplicates, handle missing values, clean text, fix dates, etc.\n- **\ud83d\udcc8 Performance Optimization**: Parallel processing, memory management, chunked processing\n- **\ud83d\udcca Data Validation**: Schema validation, data quality assessment, comprehensive reporting\n- **\ud83c\udfa8 Visualization**: Data quality heatmaps, missing data analysis, correlation matrices\n\n## \ud83d\udee0\ufe0f Installation\n\n### Quick Install\n```bash\npip install csv-cleaner\n```\n\n### From Source\n```bash\ngit clone https://github.com/your-repo/csv-cleaner.git\ncd csv-cleaner\npip install -e .\n```\n\n## \ud83d\ude80 Quick Start\n\n### **AI-Powered Automatic Cleaning** (NEW!)\n```bash\n# Automatic cleaning with AI suggestions\ncsv-cleaner ai-clean input.csv output.csv\n\n# Auto-confirm all suggestions\ncsv-cleaner ai-clean input.csv output.csv --auto-confirm\n\n# Preview execution plan without modifying files\ncsv-cleaner ai-clean input.csv output.csv --dry-run\n\n# Limit number of suggestions\ncsv-cleaner ai-clean input.csv output.csv --max-suggestions 10\n```\n\n### **AI-Powered Suggestions**\n```bash\n# Get AI-powered cleaning suggestions\ncsv-cleaner ai-suggest input.csv\n\n# Get suggestions with specific analysis\ncsv-cleaner ai-suggest input.csv --output suggestions.json\n```\n\n### **AI-Powered Data Analysis**\n```bash\n# Get comprehensive data analysis\ncsv-cleaner ai-analyze input.csv\n\n# Save analysis to file\ncsv-cleaner ai-analyze input.csv --output analysis.json\n```\n\n### **Traditional Cleaning**\n```bash\n# Clean with specific operations\ncsv-cleaner clean input.csv output.csv --operations \"remove_duplicates,fill_missing\"\n\n# Interactive mode\ncsv-cleaner clean input.csv output.csv --interactive\n\n# Performance optimized\ncsv-cleaner clean input.csv output.csv --parallel --chunk-size 10000\n```\n\n## \ud83e\udd16 AI Configuration\n\n### **Setup AI Providers**\n```bash\n# Configure OpenAI\ncsv-cleaner ai-configure set --provider openai --api-key sk-...\n\n# Configure Anthropic\ncsv-cleaner ai-configure set --provider anthropic --api-key sk-ant-...\n\n# Show current configuration\ncsv-cleaner ai-configure show\n\n# Validate configuration\ncsv-cleaner ai-configure validate\n```\n\n### **AI Features Overview**\n\n#### **AI-Powered Automatic Cleaning (`ai-clean`)**\n- **Automatic Execution**: AI generates and executes cleaning suggestions\n- **Execution Planning**: Shows detailed execution plan with confidence levels\n- **User Control**: Choose between automatic execution and manual confirmation\n- **Dry-Run Mode**: Preview changes without modifying files\n- **Learning Integration**: AI learns from execution results\n\n#### **AI-Powered Suggestions (`ai-suggest`)**\n- **Intelligent Analysis**: AI analyzes data and suggests optimal cleaning operations\n- **Confidence Scoring**: Each suggestion includes confidence level and reasoning\n- **Library Selection**: AI recommends the best library for each operation\n- **Impact Assessment**: Estimates the impact of each suggestion\n\n#### **AI-Powered Analysis (`ai-analyze`)**\n- **Comprehensive Profiling**: Detailed data quality assessment\n- **Pattern Recognition**: Identifies data patterns and anomalies\n- **Recommendation Engine**: Suggests cleaning strategies based on analysis\n- **Exportable Reports**: Save analysis results for further review\n\n## \ud83d\udccb Available Operations\n\n### **Basic Data Cleaning (Pandas)**\n- `remove_duplicates` - Remove duplicate rows\n- `fill_missing` - Fill missing values with various strategies\n- `drop_missing` - Remove rows/columns with missing values\n- `clean_text` - Clean and normalize text data\n- `fix_dates` - Convert and standardize date formats\n- `convert_types` - Convert data types automatically\n- `rename_columns` - Rename columns\n- `drop_columns` - Remove unwanted columns\n- `select_columns` - Select specific columns\n\n### **Advanced Data Cleaning (PyJanitor)**\n- `clean_names` - Clean column names\n- `remove_empty` - Remove empty rows/columns\n- `fill_empty` - Fill empty values\n- `handle_missing` - Advanced missing value handling\n- `remove_constant_columns` - Remove columns with constant values\n- `remove_columns_with_nulls` - Remove columns with null values\n- `coalesce_columns` - Combine multiple columns\n\n### **Feature Engineering (Feature-Engine)**\n- `advanced_imputation` - Advanced missing value imputation\n- `categorical_encoding` - Encode categorical variables\n- `outlier_detection` - Detect and handle outliers\n- `variable_selection` - Select relevant variables\n- `data_transformation` - Apply data transformations\n- `missing_indicator` - Create missing value indicators\n\n### **Missing Data Analysis (MissingNo)**\n- `missing_matrix` - Generate missing data matrix visualization\n- `missing_bar` - Generate missing data bar chart\n- `missing_heatmap` - Generate missing data heatmap\n- `missing_dendrogram` - Generate missing data dendrogram\n- `missing_summary` - Generate missing data summary\n\n### **ML-Based Deduplication (Dedupe)**\n- `dedupe` - ML-based deduplication with fuzzy matching\n\n## \ud83d\udcca Examples\n\n### **Example 1: AI-Powered Automatic Cleaning**\n```bash\n# Clean messy data automatically\ncsv-cleaner ai-clean messy_data.csv cleaned_data.csv --auto-confirm\n```\n\n**Output:**\n```\n\ud83e\udd16 AI-Powered Data Cleaning\n===========================\n\n\ud83d\udcca Data Analysis Complete\n- Rows: 10,000 | Columns: 15\n- Missing values: 1,250 (8.3%)\n- Duplicates: 150 (1.5%)\n- Data quality score: 78%\n\n\ud83c\udfaf AI Suggestions Generated (5 suggestions)\n1. Remove duplicates (confidence: 95%)\n2. Fill missing values with median (confidence: 88%)\n3. Clean column names (confidence: 92%)\n4. Convert date columns (confidence: 85%)\n5. Handle outliers in 'price' column (confidence: 76%)\n\n\ud83d\udccb Execution Plan\n================\n1. clean_names (pandas) - Clean column names\n2. remove_duplicates (pandas) - Remove 150 duplicate rows\n3. fill_missing (pandas) - Fill 1,250 missing values\n4. fix_dates (pandas) - Convert date columns\n5. handle_outliers (feature-engine) - Handle price outliers\n\n\ud83d\ude80 Executing AI suggestions...\n\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 100%\n\n\u2705 Successfully executed 5 operations\n\ud83d\udcca Results: 9,850 rows \u2192 9,700 rows (150 duplicates removed)\n\ud83d\udcbe Saved to: cleaned_data.csv\n```\n\n### **Example 2: AI-Powered Suggestions**\n```bash\ncsv-cleaner ai-suggest data.csv\n```\n\n**Output:**\n```\n\ud83e\udd16 AI-Powered Cleaning Suggestions\n==================================\n\n\ud83d\udcca Data Analysis\n- Dataset: 5,000 rows \u00d7 12 columns\n- Quality issues detected: Missing values, inconsistent dates, duplicates\n\n\ud83c\udfaf Recommended Operations:\n\n1. **Remove Duplicates** (Confidence: 94%)\n - Library: pandas\n - Impact: Remove ~50 duplicate rows\n - Reasoning: Found exact duplicates in customer data\n\n2. **Fill Missing Values** (Confidence: 89%)\n - Library: pandas\n - Strategy: Forward fill for dates, median for numeric\n - Impact: Fill 200 missing values\n\n3. **Fix Date Columns** (Confidence: 87%)\n - Library: pandas\n - Columns: 'order_date', 'ship_date'\n - Impact: Standardize date formats\n\n4. **Clean Column Names** (Confidence: 92%)\n - Library: pyjanitor\n - Impact: Standardize naming convention\n\n5. **Handle Outliers** (Confidence: 76%)\n - Library: feature-engine\n - Column: 'amount'\n - Impact: Cap extreme values\n```\n\n## \ud83d\udd27 Configuration\n\n### **Performance Settings**\n```bash\n# Set memory limit\ncsv-cleaner config set performance.memory_limit 4.0\n\n# Enable parallel processing\ncsv-cleaner config set performance.parallel_processing true\n\n# Set chunk size\ncsv-cleaner config set performance.chunk_size 5000\n```\n\n### **AI Settings**\n```bash\n# Set default AI provider\ncsv-cleaner config set ai.default_provider openai\n\n# Set suggestion confidence threshold\ncsv-cleaner config set ai.confidence_threshold 0.7\n\n# Enable learning mode\ncsv-cleaner config set ai.learning_enabled true\n```\n\n## \ud83d\udcc8 Performance Features\n\n- **Parallel Processing**: Multi-core data processing\n- **Memory Management**: Efficient memory usage for large datasets\n- **Chunked Processing**: Process large files in chunks\n- **Progress Tracking**: Real-time progress monitoring\n- **Performance Monitoring**: Track processing times and resource usage\n\n## \ud83e\uddea Testing\n\n```bash\n# Run all tests\npytest\n\n# Run with coverage\npytest --cov=csv_cleaner\n\n# Run specific test categories\npytest tests/unit/\npytest tests/integration/\n```\n\n## \ud83d\ude80 Deployment\n\n### **PyPI Deployment**\n\nThe project includes automated deployment scripts for PyPI:\n\n```bash\n# Setup basic version\npython scripts/setup-pypi.py\n\n# Deploy to TestPyPI\npython scripts/deploy-pypi.py --test\n\n# Deploy to production PyPI\npython scripts/deploy-pypi.py --version 1.0.0\n```\n\n### **Deployment Features**\n- \u2705 Automated testing and validation\n- \u2705 Safety checks and prerequisites verification\n- \u2705 Package building and quality checks\n- \u2705 Version management and tagging\n- \u2705 Release notes generation\n\nFor detailed deployment instructions, see [scripts/deployment-guide.md](scripts/deployment-guide.md).\n\n## \ud83d\udcda Documentation\n\n- [User Guide](docs/user-guide.md)\n- [API Reference](docs/api-reference.md)\n- [Configuration Guide](docs/configuration.md)\n- [AI Features Guide](docs/ai-features.md)\n- [Performance Tuning](docs/performance.md)\n\n## \ud83e\udd1d Contributing\n\n1. 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