metadata
license: mit
task_categories:
- text-classification
- time-series-forecasting
language:
- en
tags:
- github
- trending
- repositories
- software-engineering
- popularity
- time-series
size_categories:
- 100K<n<1M
GitHub Trending Projects (2013-2025)
A comprehensive dataset of 423,098 GitHub trending repository entries spanning 12+ years (August 2013 - November 2025), scraped from Wayback Machine snapshots of GitHub's trending page.
π― Dataset Overview
This dataset captures the evolution of GitHub's trending repositories over time, providing insights into:
- Software development trends across programming languages and domains
- Popular open-source projects and their trending patterns
- Community interests and shifts in developer focus over 12 years
- Viral repository dynamics and sustained popularity patterns
Key Statistics:
- π 423,098 trending repository entries
- ποΈ 14,500 unique repositories
- π 128 months of coverage (2013-08 to 2025-11)
- β 89.8% scraping success rate from Wayback Machine
- π Pre-processed monthly rankings with weighted scoring
π§ Dataset Configurations
This dataset has two configurations:
Configuration: full (Default)
Complete daily trending data with 423,098 entries
from datasets import load_dataset
ds = load_dataset('ronantakizawa/github-top-projects', 'full')
Configuration: monthly
Top 25 repositories per month with 3,200 entries
from datasets import load_dataset
ds = load_dataset('ronantakizawa/github-top-projects', 'monthly')
π Dataset Files
1. github-trending-projects-full.csv (19 MB)
Complete daily trending data - All 423,098 entries
| Column | Type | Description |
|---|---|---|
name |
string | Repository name |
star_count |
integer | Star count (max recorded, may be empty for pre-2020) |
fork_count |
integer | Fork count (max recorded, may be empty for pre-2020) |
repo_owner |
string | Repository owner/organization |
rank |
integer | Position in trending (1-25) |
date |
date | Snapshot date (YYYY-MM-DD) |
Use cases:
- Custom time-series analysis
- Training ML models on trending patterns
- Analyzing daily trending dynamics
- Creating custom aggregations (weekly, yearly, etc.)
- Studying viral repository behavior
2. github-top-projects-by-month.csv (211 KB)
Monthly top 25 repositories - Pre-processed with weighted scoring
| Column | Type | Description |
|---|---|---|
month |
string | Month (YYYY-MM) |
rank |
integer | Monthly rank (1-25) |
repository |
string | Full repository name (owner/name) |
repo_owner |
string | Repository owner |
repo_name |
string | Repository name |
star_count |
integer | Maximum recorded stars |
fork_count |
integer | Maximum recorded forks |
ranking_appearances |
integer | Times appeared in trending that month |
Use cases:
- Quick monthly insights and visualizations
- Dashboard creation
- Identifying consistently popular projects
- Trend analysis without processing overhead
π Scoring Methodology
Monthly rankings use a weighted frequency and position-based scoring system:
Score = Ξ£ (25 - rank + 1) for each trending appearance
Where:
- Rank 1 β 25 points
- Rank 2 β 24 points
- ...
- Rank 25 β 1 point
Example:
- Project appears 10 times at rank #1 β 250 points
- Project appears 20 times at rank #10 β 320 points (higher ranked!)
This rewards both consistency (frequent appearances) and position (higher ranks).
π Data Collection
Source: GitHub Trending page via Wayback Machine (web.archive.org) Period: August 21, 2013 - November 30, 2025 Method: Python web scraping with BeautifulSoup Snapshots: 17,127 successfully scraped from 19,064 available Retry Logic: Up to 15 retries with exponential backoff
HTML Parsing:
- Multiple extraction methods for different page structures (2013-2019, 2020+)
- Handles changes in GitHub's trending page design
- Robust error handling for incomplete snapshots
β οΈ Known Limitations
1. Missing Star/Fork Data (Pre-2020)
- 100% of 2013-2019 entries lack star/fork counts
- Only 67.8% of dataset has popularity metrics
- Cause: Different HTML structure in older Wayback snapshots
- Impact: Cannot compare absolute popularity for historical projects
2. Uneven Temporal Distribution
- Snapshot frequency: 1 to 31 per month (31x variance)
- 2019-2020 heavily over-represented
- Some months: 25 projects, others: 17,446 projects
- Impact: Monthly scores favor periods with more snapshots
3. Star Count Timing Inconsistency
- Star counts are "maximum ever recorded" across all snapshots
- A 2015 project's stars might be from 2025 scraping
- Not temporally aligned - older projects had more time to accumulate stars
- Impact: Can't fairly compare popularity across eras
4. Multiple Appearances Bias
- Top projects appear 1,700-1,900 times
- 1,129 projects appear only once
- Favors "evergreen" educational repos
- Impact: Brief viral projects may be undervalued
5. Failed Scrapes
- 1,937 URLs failed (10.2%)
- Mainly 2014-2019 due to SSL/TLS incompatibility
- Some date ranges completely missing
- Impact: Gaps in temporal coverage
See DATASET_ISSUES.md for detailed analysis
π Data Quality by Era
| Era | Quality | Star Data | Snapshot Density | Grade |
|---|---|---|---|---|
| 2013-2019 | Limited | β 0% | Low-Medium | C+ |
| 2020-2025 | Excellent | β 100% | High | A- |
Recommendation: Use 2020-2025 data for analyses requiring star/fork counts
π‘ Usage Examples
Load with Hugging Face Datasets (Recommended)
from datasets import load_dataset
# Load complete daily dataset (423,098 entries)
ds_full = load_dataset('ronantakizawa/github-top-projects', 'full')
df_full = ds_full['train'].to_pandas()
# Load monthly top 25 dataset (3,200 entries)
ds_monthly = load_dataset('ronantakizawa/github-top-projects', 'monthly')
df_monthly = ds_monthly['train'].to_pandas()
# Filter to 2020+ (with star data)
df_recent = df_full[df_full['date'] >= '2020-01-01']
# Get November 2025 top 10
nov_2025 = df_monthly[df_monthly['month'] == '2025-11'].head(10)
print(nov_2025[['rank', 'repository', 'star_count', 'ranking_appearances']])
Load Directly from CSV
import pandas as pd
# Download files from the dataset page, then:
df_full = pd.read_csv('github-trending-projects-full.csv')
df_monthly = pd.read_csv('github-top-projects-by-month.csv')
# Get top trending projects of 2024
df_2024 = df_full[df_full['date'].str.startswith('2024')]
top_2024 = df_2024.groupby(['repo_owner', 'name']).size().sort_values(ascending=False).head(10)
print(top_2024)
Time Series Analysis
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('github-trending-projects-full.csv')
df['date'] = pd.to_datetime(df['date'])
# Analyze a specific project over time
project = 'microsoft/vscode'
project_df = df[(df['repo_owner'] == 'microsoft') & (df['name'] == 'vscode')]
# Plot trending frequency over time
monthly_counts = project_df.groupby(project_df['date'].dt.to_period('M')).size()
monthly_counts.plot(title=f'{project} Trending Frequency')
plt.ylabel('Days in Trending')
plt.show()
Language Trends (requires additional metadata)
# Note: Language data not included in this dataset
# Would need to join with GitHub API data or another dataset
π Research Applications
This dataset enables analysis of:
Trending Dynamics
- What makes a repository go viral?
- How long do projects stay trending?
- Seasonal patterns in software development
Technology Adoption
- Rise and fall of programming languages
- Framework popularity over time
- Shift from monolithic to microservices
Open Source Evolution
- Growth of educational repositories
- Corporate open source contributions
- Regional trending patterns
Predictive Modeling
- Forecasting future trending projects
- Identifying early viral indicators
- Star growth prediction models
Developer Behavior
- Community interest shifts
- Popular project categories
- Documentation and tutorial demand
π Example Insights
Most Consistently Trending (2020-2025):
jwasham/coding-interview-university- 1,948 appearancesTheAlgorithms/Python- 1,891 appearancesdonnemartin/system-design-primer- 1,865 appearances
Trending Patterns:
- Educational repositories dominate long-term trending
- AI/ML projects saw massive spike in 2023-2024
- Web frameworks remain consistently popular
π License
MIT License
This dataset is released under the MIT License. You can:
- β Use for commercial purposes
- β Modify and distribute
- β Use in research (attribution appreciated!)
- β Include in proprietary software
Source Data: Wayback Machine (public archive) Original Content: GitHub Trending pages
π Acknowledgments
- GitHub for maintaining the trending page
- Internet Archive for the Wayback Machine
- Open Source Community for creating amazing projects
π§ Contact & Contributions
- Issues/Questions: Open an issue on the dataset repository
- Data Errors: Please report any inconsistencies
- Contributions: Additional metadata or corrections welcome
π Citation
If you use this dataset in your research, please cite:
@dataset{github_trending_2013_2025,
title={GitHub Trending Projects Dataset (2013-2025)},
author={Your Name},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/datasets/YOUR_USERNAME/github-top-projects}
}
π Related Datasets
- GitHub Archive (gharchive.org) - Complete GitHub event stream
- GHTorrent - GitHub data for research
- Libraries.io - Package manager dependency data
π Updates
- 2025-12: Initial release (2013-08 to 2025-11)
- Future updates planned quarterly
βοΈ Technical Details
Scraping Configuration:
- Retry attempts: 15 with exponential backoff
- Delay between requests: 4-6 seconds (randomized)
- Timeout: 45 seconds per request
- User-Agent: Mozilla/5.0 (standard browser)
Data Processing:
- Deduplication: By date + repo + rank
- Sorting: Chronological (newest first)
- Encoding: UTF-8
- Format: CSV with headers
π Known Issues
See DATASET_ISSUES.md for comprehensive list including:
- Missing data gaps
- Star count timing issues
- Temporal distribution variance
- Recommended usage guidelines
Last Updated: December 2025 Dataset Version: 1.0 Status: β Complete and ready for use