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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

Attendance Trends

Time Series Decomposition

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.

About

No description, website, or topics provided.

Resources

Readme
MIT license
Activity
Custom properties

Stars

1 star

Watchers

3 watching

Forks

0 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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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
Attendance Trends
Time Series Decomposition
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.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
3 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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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
Attendance Trends
Time Series Decomposition
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.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
3 watching
Forks
0 forks
Report repository
Releases
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Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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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
Attendance Trends
Time Series Decomposition
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.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
3 watching
Forks
0 forks
Report repository
Releases
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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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
Attendance Trends
Time Series Decomposition
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.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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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
Attendance Trends
Time Series Decomposition
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.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
3 watching
Forks
0 forks
Report repository
Releases
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Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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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
Attendance Trends
Time Series Decomposition
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.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
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1 star
Watchers
3 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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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
Attendance Trends
Time Series Decomposition
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.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
3 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages