Streamlit BI Dashboard Starter

A beginner-friendly starter template for building Business Intelligence (BI) dashboards using Streamlit and Python. Perfect for hands-on learning and rapid prototyping of data visualization applications.

One-click deployment

What You'll Learn

• How to build interactive web dashboards with Streamlit
• Data visualization and analysis with Python
• Working with CSV data and Pandas
• Creating multi-page applications
• Using Claude Code for AI-assisted development

Quick Start

Prerequisites

• Python 3.11 or higher
• uv package manager (recommended) or pip

Installation

1. Fork this repository to your GitHub account

2. Clone your fork to your local machine:

   git clone https://github.com/YOUR_USERNAME/streamlit-claude-code-starter.git
   cd streamlit-claude-code-starter

3. Install dependencies:

   # Using uv (recommended)
   uv sync
   
   # Or using pip
   pip install streamlit pandas plotly

4. Run the application:

   # Using uv
   uv run streamlit run app.py
   
   # Or using python directly
   streamlit run app.py

5. Open your browser to http://localhost:8501 to see your dashboard!

Project Structure

streamlit-claude-code-starter/
├── app.py              # Main dashboard application
├── pages/              # Additional pages for multi-page app
│   └── About_Us.py     # About page
├── sample_data/        # Sample CSV datasets
│   ├── orders.csv      # E-commerce orders data
│   └── users.csv       # User profiles data
├── pyproject.toml      # Python dependencies
└── README.md           # This file

Sample Data

The sample_data/ directory contains realistic e-commerce datasets for building your dashboard:

Core Datasets

• orders.csv (37,957 records): E-commerce order transactions from 2025

  • Fields: order_id, user_id, status, gender, created_at, returned_at, shipped_at, delivered_at, num_of_item
  • Filtered to contain only 2025+ data for current analysis

• users.csv (29,357 records): Customer profiles linked to the filtered orders

  • Fields: id, first_name, last_name, email, age, gender, state, street_address, postal_code, city, country, latitude, longitude, traffic_source, created_at, user_geom
  • Contains only users who have placed orders in the filtered dataset

Supporting Data Files (in local/ directory)

• order_items.csv (55,150 records): Individual items within each order
  • Fields: id, order_id, user_id, product_id, inventory_item_id, status, created_at, shipped_at, delivered_at, returned_at, sale_price
• products.csv (24,640 records): Product catalog for items in the orders
  • Fields: id, cost, category, name, brand, retail_price, department, sku, distribution_center_id

All supporting datasets are filtered to only include data related to the current orders in sample_data/.

Date Format Standardization

All date columns in the CSV files follow a consistent format:

• Format: YYYY-MM-DD HH:MM:SS (ISO 8601 standard)
• Example: 2025-07-04 15:33:00
• Timezone: All dates are normalized to remove timezone information for consistency

Date columns include:

• created_at: When the record was created
• shipped_at: When the order was shipped
• delivered_at: When the order was delivered
• returned_at: When the order was returned

A standardization script (local/standardize_dates.py) is available to ensure date format consistency across all CSV files.

Development with Claude Code

This project is optimized for use with Claude Code, an AI coding assistant:

1. Ask Claude to add features: "Add a sales chart to the dashboard"
2. Request improvements: "Make the data tables more interactive"
3. Get explanations: "Explain how Streamlit caching works"
4. Debug issues: "Why isn't my chart displaying?"

Example Prompts for Claude Code

• "Add a sidebar filter for order status"
• "Create a line chart showing orders over time"
• "Add user authentication to the dashboard"
• "Implement data export functionality"
• "Add responsive design for mobile devices"

Customization Ideas

Start with these beginner-friendly modifications:

1. Add Charts: Create bar charts, line graphs, or pie charts
2. Implement Filters: Add dropdown menus or sliders for data filtering
3. New Pages: Create additional pages for different views
4. Styling: Customize colors, fonts, and layout
5. Real Data: Replace sample data with your own CSV files

Learning Resources

• Streamlit Documentation
• Pandas Tutorial
• Plotly Documentation
• Claude Code Documentation

Contributing

This is a learning project! Feel free to:

• Fork and experiment
• Share your improvements
• Ask questions in the issues
• Submit pull requests with enhancements

Next Steps

1. Explore the code: Start by understanding app.py
2. Make small changes: Try modifying the title or adding a new metric
3. Add features: Use Claude Code to help implement new functionality
4. Share your work: Deploy your dashboard to Streamlit Cloud

License

MIT License - feel free to use this for learning and commercial projects.

---

Happy Dashboard Building!

Start building your first BI dashboard today and see how AI-assisted development with Claude Code can accelerate your learning journey.

Deploy here!