| --- |
| 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**: 1963 |
| - **Repositories**: 25 (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. |
| |
| --- |
| |
| **Note**: This dataset was created using a custom GitHub scraper tool. |
| |
| |
