🚲 London Cycleways Dashboard

This is a simple but powerful dashboard built with Streamlit and Plotly to explore bike usage data in London.
You can filter by location and date, and see different visualizations that help make sense of the numbers.

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📦 What's Inside?

We take a bunch of CSV files (with cycle counts), clean them up, store them in a small SQLite database, and show everything on a web dashboard!

🚀 How to Run It

1. Install the tools

Make sure you have Python 3. Then open terminal and run:

pip install streamlit pandas plotly

2. Put your data in

Drop all the .csv files into the data/ folder.

3. Load data into the database

Run this script to clean and save everything into a local SQLite database:

python scripts/load_to_sqlite.py

It will create a file called cycleways.db in the data/ folder.

4. Run the dashboard

Now launch the Streamlit app:

streamlit run scripts/main.py

It will open in your browser automatically 🎉

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📊 What Can You See?

Here’s what the dashboard shows and what each chart does:

1. 📄 Filtered Data Preview

See a live table of the data based on your filters (location + date).

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2. 📆 Daily Bike Count Over Time

Line chart that shows how bike usage changes day by day. Great for spotting trends like peaks during weekdays or dips on rainy days.

fig = px.line(daily, x="date", y="count", title="Daily Total Bike Count")

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3. 🧭 Direction Breakdown

Pie chart showing how bikes are moving: inbound, outbound, or both. Helps understand traffic flow.

fig = px.pie(dir_data, names="Direction", values="Count")

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4. 🚲 Bike Count by Mode

Bar chart showing how many bikes were private vs. rental. (Useful for city planning or bike sharing analysis.)

fig = px.bar(mode_counts, x="Mode", y="Total Bikes")

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🧠 How It Works (in simple terms)

• We use pandas to read and clean the CSV files.
• We save the cleaned data into SQLite (a mini database stored as a file).
• Then we build interactive charts using Plotly inside a Streamlit app.
• You can pick locations and dates using the sidebar, and the charts update live!

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📸 Preview

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✨ Credits

Built with ❤️ by Albert Just having fun with data and making dashboards more fun to explore!