Genre Galaxy is an interactive data visualization tool designed to map and analyze the relationships between literary genres. This project utilizes a comprehensive dataset sourced from Goodreads to reveal patterns and connections between genres, providing insights for academic researchers, publishers, and literature enthusiasts.
- Interactive Visualization: Explore the interconnectedness of literary genres through an interactive network graph. Nodes represent genres, and edges indicate the strength of their co-occurrence.
- Search Functionality: Search for specific books to highlight their associated genres and discover how they relate to other genres in the dataset.
- Dynamic Interaction: Click on genre nodes to see all direct connections, with related genres ranked by their co-occurrence strength.
- Python: Core programming language used for data processing and application development.
- Dash: Web framework for building the interactive application.
- Plotly: Visualization library used to create the network graph and interactive elements.
- Pandas: Data manipulation library for cleaning and processing the Goodreads dataset.
- NetworkX: Library used to construct and analyze the network graph of genres.
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Clone the repository:
git clone https://github.com/afsanab/genregalaxy.git cd genregalaxy -
Install the required Python packages:
pip install -r requirements.txt
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Run the application:
python create_graph.py
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Open your browser and go to
http://127.0.0.1:8050/to view the application.
The dataset used for this project was sourced from Kaggle's Best Books 10K Multi-Genre Data, which includes data on thousands of books from Goodreads.
- create_graph.py: Main application file containing the Dash setup and layout and script for generating the network graph using NetworkX.
- process_data.py: Script for cleaning and processing the Goodreads dataset.
- calculate_stats.py: Script for performing statistical analysis on genre pairs.