Quick Metric

Welcome to Quick Metric - a framework for quickly creating metrics using easy-to-edit YAML configs and reusable methods to filter, calculate, and transform data.

Installation

# Clone the repository
git clone <repository-url>
cd quick_metric

# Create virtual environment and install
uv venv && source .venv/bin/activate
uv pip install -e .

Quick Start

from quick_metric import metric_method, generate_metrics
import pandas as pd

# 1. Define custom metrics
@metric_method
def count_records(data):
    return len(data)

@metric_method
def mean_value(data, column='value'):
    return data[column].mean()

# 2. Create data and config
data = pd.DataFrame({'category': ['A', 'B', 'A'], 'value': [10, 20, 30]})
config = {
    'category_a_metrics': {
        'method': ['count_records', 'mean_value'],
        'filter': {'category': 'A'}
    }
}

# 3. Generate metrics
results = generate_metrics(data, config)
print(results['category_a_metrics']['count_records'])  # 2
print(results['category_a_metrics']['mean_value'])     # 20.0

Key Features

• Simple decorator: Register metrics with @metric_method
• Flexible configuration: Use dictionaries or YAML files
• Multiple output formats: Nested dict, DataFrame, records, or flat DataFrame
• Advanced filtering: Complex data filtering with logical operators
• Pipeline integration: Works with oops-its-a-pipeline workflows

Documentation

📚 Full Documentation

• Getting Started - Installation and setup
• Usage Guide - Comprehensive tutorials and examples
• API Reference - Complete API documentation

License

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

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Quick Metric - Making data metrics simple, configurable, and maintainable! 🚀