Pydata-visualizer Documentation Overview

This repository contains comprehensive documentation for the Pydata-visualizer Python library for data analysis and profiling.

Documentation Files

  1. README.md: Main project documentation with overview, features, and basic usage.

  2. DOCUMENTATION.md: Comprehensive technical documentation covering all components of the library.

  3. USER_GUIDE.md: Step-by-step guide for using the library, suitable for beginners.

  4. INSTALLATION.md: Detailed installation instructions for various environments.

  5. EXAMPLES.md: Practical examples showing how to use the library in different scenarios.

  6. PYPI_DESCRIPTION.md: Description file formatted for PyPI listing.

Quick Start

import pandas as pd
from data_visualizer.profiler import AnalysisReport, Settings

# Load your dataset
df = pd.read_csv("your_dataset.csv")

# Create a report with default settings (uses Seaborn plots)
report = AnalysisReport(df)
report.to_html("report.html")

# Or use interactive Plotly visualizations with custom settings
settings = Settings(
    use_plotly=True,
    text_analysis=True,
    include_correlations=True,
    include_alerts=True
)
report = AnalysisReport(df, settings=settings)
report.to_html("interactive_report.html")

Documentation Structure

  • README.md: Project overview, basic usage, and key features

  • DOCUMENTATION.md: In-depth technical documentation on all aspects of the library

  • USER_GUIDE.md: Practical guide for new users with step-by-step instructions

  • INSTALLATION.md: Installation methods for different environments

  • EXAMPLES.md: Code examples demonstrating common use cases

  • PYPI_DESCRIPTION.md: Concise package description for PyPI

Additional Resources

Contributing

Contributions to the documentation are welcome! Please feel free to submit pull requests to improve any of these documents.

License

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