Project Description

This visualization explores travel patterns using Capital Bikeshare data (2024–2025). The raw trip records were cleaned and combined, then analyzed to uncover trends by year, month, weekday, rider type, bike type, and stations. To highlight usage intensity, I aggregated millions of rides into total trips, I also identified the Top 20 most popular start and end stations, presenting them in both bar charts and interactive maps. By structuring the data in a stacked format, the dashboards allow viewers to compare start versus end station activity side‑by‑side, while also exploring overall totals. The result is a clear, engaging view of how Washington, DC’s bikeshare system is used across time, location, and rider segments.

Source: Capital Bikeshare data

If you’d like to dive deeper into the analysis and see the full Python workflow behind these visualizations, I’ve published my notebooks on Kaggle. You can explore the code, experiment with the data, and even fork the project to try your own variations. Visit my Kaggle page here: Link