🚕 Uber Pickups in NYC (Streamlit Demo)

A simple interactive dashboard visualizing Uber pickup data in New York City.

I built this project to learn the basics of Streamlit and how to handle data visualization in Python. It processes a dataset of Uber pickups to show how caching, filtering, and 3D mapping work in a real-time web app.

🚀 Live Demo

View the Live App Here

🔍 What I Learned

Through this project, I explored:

• Data Caching: Using @st.cache_data to load 10k rows of data without slowing down the app on every interaction.
• NumPy Histograms: Converting raw timestamps into a "busiest hours" bar chart.
• PyDeck Visualization: Mapping geospatial data into a 3D hexagonal density layer to visualize hotspots.

🛠️ Tech Stack

• Python 3.13
• Streamlit
• Pandas & NumPy
• PyDeck

📦 How to Run Locally

1. Clone the repository

   git clone [https://github.com/taco0cat/uber-data-app.git](https://github.com/taco0cat/uber-data-app.gitt)
   cd uber-data-app

2. Setup Environment

   # Windows
   python -m venv .venv
   .\.venv\Scripts\Activate.ps1
   
   # Mac/Linux
   python3 -m venv .venv
   source .venv/bin/activate

3. Install Dependencies

   pip install -r requirements.txt

4. Run the App

   streamlit run streamlit_app.py

📚 Acknowledgements

This project is based on the official Streamlit "Create an App" tutorial. The source code and dataset are adapted from their documentation for educational purposes.