Python Ireland Meetup Attendance Analysis

This project analyzes attendance data from Python Ireland meetups over time, focusing on trends, seasonality, and the impact of COVID-19 on attendance patterns.

Overview

The analysis explores several aspects of Python Ireland's meetup attendance:

1. Overall Attendance Trends: Visualizes attendance over time, highlighting pre-COVID, during-COVID, and post-COVID periods.
2. Period Comparison: Compares average and median attendance across different time periods.
3. Monthly Seasonality: Identifies patterns in attendance based on the month of the year.
4. Time Series Decomposition: Breaks down the attendance data into trend, seasonal, and residual components.

Project Structure

analyse_meetup_attendance/
├── data/                  # Data files
│   ├── meetup_extract.html         # Raw HTML (scraped)
│   └── python_ireland_meetups.csv  # Extracted dataset of meetup dates, titles, and attendance figures
├── scripts/               # Python scripts
│   ├── extract_meetup_data.py         # Script for extracting meetup data from the html
│   └── meetup_attendance_analysis.py  # Analysis script
├── generated_figures/     # Output visualizations
│   ├── attendance_trends.png          # Attendance trends visualization
│   └── time_series_decomposition.png  # Time series decomposition visualization
├── LICENSE                # MIT License file
└── README.md              # This file

Key Findings

• Regular monthly meetups show distinct attendance patterns
• COVID-19 had a significant impact on meetup formats but surprisingly stable attendance
• Post-COVID attendance shows some changes compared to pre-COVID levels
• Certain months consistently show higher/lower attendance, with January and June being highest and November and December lowest (accounting for PyCon and holidays)

Generated Visualisations

Attendance Trends

Time Series Decomposition

Requirements

• Python 3.11+
• Pandas
• Numpy
• Matplotlib
• Seaborn
• Statsmodels

Usage

To run the analysis:

python scripts/meetup_attendance_analysis.py

This will process the data and generate two visualization files in the generated_figures directory:

• generated_figures/attendance_trends.png
• generated_figures/time_series_decomposition.png

Credits

This analysis was primarily generated by AI using Windsurf IDE running Claude 3.7 Sonnet.

And thanks to Meetup.com for helping organise our meetups.

License

This project is licensed under the MIT License - see the LICENSE file for details.