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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
[](https://www.python.org/)
[](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.
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