pytics 1.1.1

An interactive data profiling library for Python notebooks with rich HTML reports and PDF export capabilities

pytics

An interactive data profiling library for Python that generates comprehensive HTML reports with rich visualizations and PDF export capabilities.

Features

• 📊 Interactive Visualizations: Built with Plotly for dynamic, interactive charts
• 📱 Responsive Design: Reports adapt to different screen sizes
• 📄 PDF Export: Generate publication-ready PDF reports
• 🎯 Target Analysis: Special insights for classification/regression tasks
• 🔍 Comprehensive Profiling: Detailed statistics and distributions
• ⚡ Performance Optimized: Efficient handling of large datasets
• 🛠️ Customizable: Configure sections and visualization options

Example Reports

Full Profile Report

Targeted Analysis Report

Installation

pip install pytics

Quick Start

import pandas as pd
from pytics import profile

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

# Generate an HTML report
profile(df, output_file='report.html')

# Generate a PDF report
profile(df, output_format='pdf', output_file='report.pdf')

# Profile with a target variable
profile(df, target='target_column', output_file='report.html')

# Select specific sections
profile(
    df,
    include_sections=['overview', 'correlations'],
    output_file='report.html'
)

Report Sections

1. Overview

   • Dataset summary
   • Memory usage
   • Data types distribution
   • Missing values summary

2. Variable Analysis

   • Detailed statistics
   • Distribution plots
   • Missing value patterns
   • Unique values analysis

3. Correlations

   • Correlation matrix
   • Feature relationships
   • Interactive heatmaps

4. Target Analysis (when target specified)

   • Target distribution
   • Feature importance
   • Target correlations

Configuration Options

profile(
    df,
    target='target_column',           # Target variable for supervised learning
    include_sections=['overview'],    # Sections to include
    exclude_sections=['correlations'],# Sections to exclude
    output_format='pdf',             # 'html' or 'pdf'
    output_file='report.html',       # Output file path
    theme='light',                   # Report theme
    title='Custom Report Title'      # Report title
)

Edge Cases and Limitations

Data Size Limits

• Recommended maximum rows: 1 million
• Recommended maximum columns: 1000
• Large datasets may require increased memory allocation

Special Cases

• Missing Values: Automatically handled and reported
• Categorical Variables: Limited to 1000 unique values by default
• Date/Time: Automatically detected and analyzed
• Mixed Data Types: Handled with appropriate warnings

Error Handling

• Custom exceptions for clear error reporting
• Warning system for non-critical issues
• Graceful degradation for memory constraints

Best Practices

1. Memory Management

   • Sample large datasets if needed
   • Use section selection for focused analysis
   • Monitor memory usage for big datasets

2. Performance Optimization

   • Limit categorical variables when possible
   • Use targeted section selection
   • Consider data sampling for initial exploration

3. Report Generation

   • Choose appropriate output format
   • Use meaningful report titles
   • Save reports with descriptive filenames

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. See the CONTRIBUTING.md file for guidelines.

License

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