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Datasets:
ronantakizawa
/
github-top-code

Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
code
Size:
1M - 10M
Tags:
code
github
source-code
trending-developers
software-engineering
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
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metadata 
license: mit
task_categories:
  - text-generation
language:
  - code
tags:
  - code
  - github
  - source-code
  - trending-developers
  - software-engineering
size_categories:
  - 1M<n<10M

GitHub Top Developer Source Code

A curated dataset of source code from GitHub's top trending developers (2015-2025). Unlike bulk code scrapes, this dataset is filtered to code written by developers who repeatedly appeared on GitHub's trending page, linking each file to rich developer and repository metadata.

Dataset Summary

  • 1.3M+ source code files from ~13,500 repositories across ~4,700 unique developers
  • All programming languages included (Python, JavaScript, TypeScript, Rust, Go, C/C++, Java, and 80+ more)
  • Permissive licenses only (MIT, Apache-2.0, BSD, ISC, etc.)
  • Rich metadata per file: repo stars, commit count, developer trending rank, company affiliation

Schema

Each row represents a single source file:

Column Type Description
file_path string Path within the repo (e.g. src/main.py)
file_language string Detected programming language
file_size int64 File size in bytes
content string Raw source code (UTF-8)
repo_name string Full repository name (owner/repo)
repo_stars int64 GitHub star count at time of collection
repo_commit_count int64 Total commits on default branch
repo_license string SPDX license identifier
repo_description string Repository description
repo_primary_language string GitHub-detected primary language
repo_topics list[string] Repository topics/tags
developer_username string GitHub username
developer_name string Developer display name
developer_company string Company affiliation
developer_times_trended int64 Number of times on GitHub trending page
developer_best_rank int64 Best trending rank achieved
developer_avg_rank float64 Average trending rank
developer_median_rank float64 Median trending rank

Usage 

from datasets import load_dataset

ds = load_dataset("ronantakizawa/github-top-code", split="train")

# Filter by language
python_files = ds.filter(lambda x: x["file_language"] == "Python")

# Filter by stars
popular = ds.filter(lambda x: x["repo_stars"] > 1000)

# Get files from a specific developer
dev_files = ds.filter(lambda x: x["developer_username"] == "torvalds")

What Makes This Dataset Unique

Feature This Dataset The Stack codeparrot/github-code
Curated by developer reputation Yes No No
Developer metadata (trending rank, company) Yes No No
Stars per repo Yes Yes No
Commit count Yes No No
Permissive licenses only Yes Yes Mixed

Existing code datasets are massive bulk scrapes of all public GitHub repos. This dataset is intentionally curated: every file comes from a developer who was recognized on GitHub's trending page, providing a higher signal-to-noise ratio for studying elite developer practices or fine-tuning code models.

Collection Methodology

  1. Developer sourcing: 4,763 unique developers extracted from ronantakizawa/github-top-developers, which tracks GitHub trending page appearances from 2015-2025 via Wayback Machine snapshots.
  2. Repository discovery: For each developer, their top 5 repositories by stars were selected using the GitHub API, filtered to repos they own (not forks or contributions).
  3. License filtering: Only repositories with permissive licenses (MIT, Apache-2.0, BSD, ISC, Unlicense, etc.) were included.
  4. Code extraction: Repository tarballs were downloaded and source files extracted, skipping binary files, vendored directories (node_modules, vendor, etc.), generated files, and files exceeding 1MB.
  5. Metadata enrichment: Each file is linked to repository metadata (stars, language, topics) and developer metadata (trending history, company).

Filtering Applied

  • Repos: Owner-created only (no forks), permissive license, non-empty
  • Directories skipped: node_modules, vendor, third_party, dist, build, __pycache__, .git, venv, and 30+ more
  • Files skipped: Binary files, files >1MB, non-UTF-8 files
  • Top 5 repos per developer by star count

Limitations

  • Star counts and commit counts reflect the time of collection, not real-time values
  • Some developers may have deleted or renamed their accounts since trending
  • Commit counts are for the default branch only
  • The dataset reflects trending developers specifically, which may over-represent certain languages or project types popular on GitHub

Source

Built from ronantakizawa/github-top-developers using the GitHub REST API.