# PyDataQuality

**The Enterprise-Grade Data Quality Engine for Python.**

PyDataQuality automates the tedious 80% of data science: validating, profiling, and cleaning new datasets. It transforms raw pandas DataFrames into publication-ready quality reports with a single line of code. Designed for high-velocity data teams, it features batch processing for big data, custom rule validation, and AI-powered remediation suggestions.

![Data Quality Analysis](https://raw.githubusercontent.com/DominionAkinrotimi/pydataquality/main/docs/images/sample_visualization.png)

## Features

### Core Capabilities
- **Data Quality Analysis**: Detect missing values, outliers, inconsistencies, and data type issues
- **Data Extraction**: Pinpoint and extract problematic rows (e.g., specific outliers) for remediation via `get_problematic_rows()`
- **Multi-Format Support**: Native support for CSV, Excel (.xlsx, .xls), JSON, and Parquet files
- **CLI Interface**: Auto-detects file formats from the terminal - no coding required
- **Interactive Notebook Display**: Direct rendering in Jupyter/Colab with `show_report()`
- **Batch Sampling**: Process large datasets (GBs) efficiently with chunk-based sampling
- **Custom YAML Rules**: Enterprise-grade validation with configuration files
- **AI Remediation Prompts**: Auto-generated Python scripts for fixing detected issues
- **Comprehensive Visualizations**: Publication-quality plots for data quality assessment
- **Professional Reports**: HTML, text, and JSON reports with actionable insights
- **Easy Integration**: Works seamlessly with pandas DataFrames from any source

### Supported Input Formats
**CLI**: Auto-detects CSV, Excel (.xlsx, .xls), JSON, Parquet (`python -m pydataquality data.csv`)  
**Python API**: Accepts any pandas DataFrame from:
- CSV files (`pd.read_csv()`)
- Excel files (`pd.read_excel()`)
- JSON files (`pd.read_json()`)
- Parquet files (`pd.read_parquet()`)
- SQL databases (`pd.read_sql()`)
- APIs (any source that produces a DataFrame)
- Cloud platforms (Snowflake, BigQuery, etc.)

**Summary**: PyDataQuality works with CSV, Excel, JSON, Parquet files, and any pandas DataFrame from SQL databases, APIs, or cloud platforms. The library is format-agnostic - it only requires a pandas DataFrame as input.

## Why PyDataQuality?

### The Problem
You just received a new dataset. You need to know what's wrong with it and how to fix it - **fast**. Existing tools either:
- Take too long (pandas-profiling: 10+ minutes, 200MB reports)
- Require too much setup (Great Expectations: YAML configs, checkpoints, data contexts)
- Don't give actionable insights (pandas `.describe()`: just basic stats)

### The Solution
PyDataQuality fills the gap between "too simple" and "too complex":

> **"I just got a new dataset. What's wrong with it, and how do I fix it? I need answers in 30 seconds, not 30 minutes."**

### Comparison with Alternatives

| Feature | pandas-profiling | Great Expectations | **PyDataQuality** |
|:--------|:-----------------|:-------------------|:------------------|
| One-liner usage | ❌ | ❌ | ✅ |
| Extract bad rows | ❌ | ❌ | ✅ |
| CLI support | ❌ | ❌ | ✅ |
| Fast on large data | ❌ | ✅ | ✅ |
| No config needed | ✅ | ❌ | ✅ |
| AI integration | ❌ | ❌ | ✅ |
| Beginner-friendly | ⚠️ | ❌ | ✅ |

### Real-World Example

**Scenario**: Data scientist gets a CSV with 1M rows

**With pandas-profiling**:
```python
# Takes 10+ minutes, generates 200MB HTML
profile = ProfileReport(df)
profile.to_file("report.html")
# Now what? How do I fix the issues?
```

**With PyDataQuality**:
```python
# Takes 30 seconds
analyzer = pdq.analyze_dataframe(df)
bad_ages = analyzer.get_problematic_rows('age', 'outliers')
bad_ages.to_csv('fix_these.csv')  # Send to data team
```

**Bottom line**: PyDataQuality is **simple by design**. It just works, without making you read 50 pages of documentation.

## Installation

```bash
# Clone the repository
git clone https://github.com/DominionAkinrotimi/pydataquality.git
cd pydataquality

# Install in development mode
pip install -e .

# Install requirements directly
pip install -r requirements.txt

# For interactive notebook support (Jupyter/Colab)
pip install ".[notebook]"
```

## Quick Start

```python
import pandas as pd
import pydataquality as pdq

# Load your data
df = pd.read_csv('your_data.csv')

# Quick quality check
summary = pdq.quick_quality_check(df, name="My Dataset")

# Comprehensive analysis
analyzer = pdq.analyze_dataframe(df, name="My Dataset")

# Analysis with custom thresholds (e.g., stricter missing data check)
# Analysis with custom thresholds and excluded values
config = {
    'missing_critical': 0.1, 
    'outlier_threshold': 2.0,
    'exclude_values': {
        'age': [150, -5],  # specific values to ignore
        'sales': [-100]
    }
}
analyzer = pdq.analyze_dataframe(df, name="My Dataset", config=config)

# Generate visualizations
visualizer = pdq.create_visual_report(analyzer)

# Generate HTML report
pdq.generate_report(analyzer, output_path="quality_report.html", format='html')

# Get AI assistance for fixes (includes EDA context by default)
prompt = pdq.generate_ai_prompt(analyzer)
print(prompt) 

# Output example:
# "I have a dataset with 1000 rows. Issues: age (outliers: outside [18, 65]).
#  Statistical Context: age (int64): Mean=42, Max=150...
#  Please write a script..."
```

## CLI Usage

Run the analysis directly from your terminal:

```bash
# Basic usage
python -m pydataquality data.csv

# Generate professional HTML report
python -m pydataquality data.csv --report html --theme professional

# Create visualizations
python -m pydataquality data.csv --visualize
```

## Documentation

- [Quickstart Guide](https://github.com/DominionAkinrotimi/pydataquality/blob/main/docs/quickstart.md)
- [API Reference](https://github.com/DominionAkinrotimi/pydataquality/blob/main/docs/api.md)
- [Examples](https://github.com/DominionAkinrotimi/pydataquality/blob/main/examples/)

## Examples

Check the `examples` directory for comprehensive usage examples:

1. **Basic Usage**: Simple analysis and reporting
2. **Advanced Features**: Custom configurations and visualizations
3. **Real-world Scenarios**: Handling various data quality issues


## Project Structure
```
PyDataQuality/
├── pydataquality/       # Main package directory
│   ├── __init__.py      # Exports
│   ├── __main__.py      # Package entry point
│   ├── analyzer.py      # Core analysis engine
│   ├── cli.py           # Package CLI logic
│   ├── comparator.py    # Data drift detection
│   ├── config.py        # Settings management
│   ├── reporter.py      # Report generation
│   ├── utils.py         # Helper functions
│   └── visualizer.py    # Visualization engine
├── docs/                # Documentation
├── examples/            # Usage examples
├── tests/               # Unit tests
├── cli.py               # Root CLI script (local usage)
├── setup.py             # Installation script
└── requirements.txt     # Dependencies
```

## Key Components

### 1. DataQualityAnalyzer
Core analysis engine that examines data structure, detects issues, and computes statistics.

### 2. DataQualityVisualizer
Creates comprehensive visualizations including:
- Missing value heatmaps
- Outlier detection plots
- Distribution analysis
- Correlation heatmaps
- Categorical value distributions

### 3. QualityReportGenerator
Generates professional reports in multiple formats with actionable recommendations.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgments

- Built with pandas, matplotlib, and seaborn
- Inspired by real-world data quality challenges

- Designed for data scientists and analysts





