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Datasets:
FurkanNar
/
PyHub

Tasks:
Text Generation
Text Classification
Question Answering
Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
1K - 10K
Tags:
code
Libraries:
Datasets
pandas
Polars
License:
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metadata 
license: mit
task_categories:
  - text-generation
  - text-classification
  - question-answering
language:
  - en
tags:
  - code
size_categories:
  - 10K<n<100K

license: mit task_categories: - text-generation - text-classification - question-answering language: - en tags: - code size_categories: - 10K<n<100K

PyHub: Vetted Python Code from Popular GitHub Repositories

Python License

A large-scale dataset of Python source code, test files, and documentation scraped from high-quality GitHub repositories. Designed for training code understanding and generation models, particularly for software engineering benchmarks like SWE-bench.

Dataset Statistics

  • Total files: 271,995
  • Repositories: 50+ (minimum 50 stars)
  • File types: Python source, test files, READMEs
  • Time period: Repositories created before January 1, 2020
  • Size limit: Maximum 100 MB per repository
  • License: MIT

Dataset Structure

Each row in dataset.csv represents a single file with the following columns:

Column Description
repo_name Repository name (e.g., "requests")
repo_full_name Full repository name (e.g., "psf/requests")
owner Repository owner (e.g., "psf")
stars Star count
license SPDX license identifier
repo_description Repository description
filepath Relative path within repository
file_type "python", "test", or "readme"
language "Python", "Markdown", "reStructuredText", or ""
content Full file text
size_bytes File size in bytes
num_lines Number of lines

File Types

  • Python source (.py): Production code files
  • Test files (*test*.py): Unit tests and test suites
  • README files (README.*): Documentation in Markdown, reStructuredText, or plain text

Collection Methodology

The dataset was collected using a custom GitHub scraper with the following process:

  1. Repository selection: GitHub API search for repositories with x ≥ 50 stars, created before 2020-01-01, non-fork
  2. Cloning: Shallow git clone (--depth 1) with 100 MB size filter to exclude large monorepos
  3. File collection: Recursive walk through cloned repositories, excluding hidden directories (files starting with .)
  4. File type filtering: Only Python source files (.py), test files (*test*.py), and README files (README.*) were collected
  5. Content extraction: UTF-8 encoding with error handling for robust text extraction
  6. Parallel processing: 3 concurrent workers for efficient processing
  7. CSV generation: All file data consolidated into a single CSV with repository metadata embedded in each row

Quality Filters

  • Star threshold: Minimum 50 stars (indicates community vetting)
  • Size limit: 100 MB to exclude monorepos and binary-heavy projects
  • File type filtering: Only Python, test, and documentation files
  • Hidden files excluded: Files/directories starting with . ignored
  • Encoding handling: UTF-8 with error fallback

Intended Use Cases

  • Code completion: Training autocompletion models on real-world Python patterns
  • Bug detection: Learning from production codebases with established testing practices
  • Test generation: Understanding test-code relationships from included test files
  • Documentation generation: Learning code-documentation correlations from READMEs
  • SWE-bench training: Base dataset for software engineering benchmark preparation
  • Code understanding: Repository structure and dependency learning

Limitations

  • Temporal bias: Pre-2020 code, missing modern Python features (type hints, match statements, structural pattern matching)
  • Popularity bias: High-star repos only, may not represent niche or edge-case patterns
  • Size limitation: 100 MB cap excludes large enterprise monorepos
  • Language bias: Primarily English documentation and comments
  • Static only: No execution data, test results, or runtime behavior

Recommended Supplements

For comprehensive model training, consider supplementing with:

  • Post-2020 repositories for modern Python patterns
  • Smaller repositories for edge-case and niche patterns
  • Synthetic examples for specific bug types
  • Negative examples (buggy code) for robustness

License

This dataset is licensed under the MIT License. See the LICENSE file for details.

Contact

furkannar168@hotmail.com

or you can simply open up an issue for issues or questions that you'd like to adress or ask.

Note: This dataset was created using a custom GitHub scraper tool.