A comprehensive Streamlit-based dashboard for visualizing and analyzing your Goodreads reading data. Upload your Goodreads export CSV and gain deep insights into your reading habits, trends, and statistics.

https://goodreads-dashboard.streamlit.app

The dashboard is organized into several tabs, providing deep dives into different aspects of your reading data.

• Total Books Read: Track the total number of books you've completed.
• Your Average Rating: View the average rating you've given to your books.
• Community Average Rating: Compare with the Goodreads community's average rating for your read books.
• Unique Authors: Discover the total number of unique authors you've read.

This section focuses on your reading speed and volume over time.

• Metrics: Average Pages per Month, Total Pages Read, Longest Reading Streak, and Average Pages per Book.
• Chart: Cumulative Pages Read Over Time (Line chart).

Analyze your reading activity across years and explore your most-read authors.

• Chart 1: Books Read Each Year (Bar chart).
• Chart 2: Top N Authors by Books Read (Bar chart, including co-authors).
• Interactive Table: Select an author to view a detailed list of the books you've read by them.

Insights into the production aspects of your library.

• Chart 1: Top N Publishers by Books Read (Bar chart).
• Chart 2: Binding Distribution (Pie chart).
• Chart 3: Books Read by Year of Publication (Bar chart).

Showcases your highest-rated books and those with the highest community ratings.

• Your Top Rated Books: A table showing your top $N$ books based on your personal 'My Rating'.
• Top Books by Goodreads Average Rating: A table showing your top $N$ books based on the community's 'Average Rating'.

Identifies the books at the extremes of your collection's page counts.

• Longest Books: A table of the top 5 longest books by page count.
• Shortest Books: A table of the top 5 shortest books by page count.

A powerful feature to enhance your dataset by scraping genre information from Goodreads.

• Functionality: Uses multi-threaded web scraping via enrich.py to add a 'Genres' column to your DataFrame based on the book's 'Book Id'.
• Output: Allows you to Download the Enriched CSV and optionally save it to a local path.
• Chart: Top N Genres (Bar chart) based on the enriched data.

Displays the full, processed DataFrame, which will include the enriched genre data if that feature was used.

1. Clone the repository:

   git clone https://github.com/NathanKong06/Goodreads-Dashboard.git
   cd Goodreads-Dashboard

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run dashboard.py

1. Export your Goodreads data as a CSV file:

   • Visit Goodreads Import/Export
   • Follow the instructions to export your library if needed.

2. Launch the dashboard:

   streamlit run dashboard.py

3. Upload your Goodreads CSV file using the file uploader in the app

4. Explore the various insights and visualizations provided

Goodreads-Dashboard/
├── dashboard.py                # Main Streamlit application
├── insights_functions.py       # Data processing and visualization functions
├── enrich.py                   # Genre enrichment script
├── requirements.txt            # Python dependencies
├── README.md                   # This file
└── screenshots/               # Screenshot directory
    ├── metrics.png
    ├── trends.png
    ├── top-books.png
    ├── binding.png
    ├── genre.png
    ├── length-books.png
    ├── pace.png
    └── raw.png

• Python 3.8+
• Streamlit - Web app framework
• Pandas - Data manipulation and analysis
• Plotly - Interactive visualizations
• BeautifulSoup4 - Web scraping (for enrichment)
• Requests - HTTP library (for enrichment)

See requirements.txt for specific versions.

• Handles missing and zero-valued ratings
• Processes author lists including co-authors
• Validates required CSV columns
• Converts date formats and handles invalid dates
• Cleans publisher names with title casing

• Interactive bar charts for reading trends and top authors
• Line charts for cumulative reading progress
• Pie charts for binding distribution
• Data tables for top-rated books
• Responsive layouts that adapt to different screen sizes

• Caches expensive computations using Streamlit's @st.cache_data
• Efficient data filtering and aggregation
• Multi-threaded web scraping for genre enrichment (up to 8 concurrent requests)