I analyzed the lineups at the most popular nightclubs
16 days ago
- #visualization
- #data-analysis
- #nightclubs
- The author analyzed nightclub lineups using data from Resident Advisor (RA) to understand booking patterns.
- A Python scraper with Beautiful Soup was used to gather data from RA, respecting server load with throttling and caching.
- Data was cleaned and verified, with artist names normalized and edge cases handled.
- Similarity between clubs was calculated using the Jaccard index, and clusters were identified using NetworkX.
- In 2019, 131 clubs hosted 8,502 events with 9,405 unique artists, averaging 3.24 bookings per artist and 3.5 artists per event.
- Average overlap in bookings between clubs was only 1%, lower than expected.
- D3 was used for visualization, allowing interactive exploration and scrollytelling to narrate the data story.
- The 'resident factor' (repeat bookings) was lower than anticipated, indicating more rotating talent than resident DJs.
- The project combined web scraping, data analysis, and interactive visualization, revealing insights into club booking diversity.
- Technical stack included Python, Pandas, NetworkX, D3, and React, with the project available on GitHub.