pharma-pubmed-fetcher 0.1.1

A Python package to fetch research papers from PubMed based on user-defined queries

Pharma PubMed Fetcher

A Python package to fetch research papers from PubMed based on user-defined queries, specifically targeting papers affiliated with pharmaceutical or biotech companies.

Features

• Search PubMed using custom queries with full PubMed query syntax support
• Filter papers by non-academic affiliations
• Extract company names from author affiliations
• Save results in CSV format with detailed information
• Command-line interface for easy usage
• Python API for programmatic access

Installation

From PyPI

pip install pharma-pubmed-fetcher

From Source

git clone https://github.com/yourusername/pharma-pubmed-fetcher.git
cd pharma-pubmed-fetcher
poetry install

Usage

Command Line

get-papers-list "your search query" --email [e-mail] --file output.csv

Command Line Arguments

• query: Search query for PubMed (supports full PubMed query syntax)
• --email: Your email address (required by NCBI)
• --file: Output CSV file path (default: pubmed_results.csv)
• --max-results: Maximum number of results to return (default: 100)
• --debug: Enable debug mode for verbose output

Python API

from pubmed_fetcher import PubMedFetcher

# Initialize the fetcher
fetcher = PubMedFetcher(email="[e-mail]")

# Search for papers
papers = fetcher.search(query="cancer AND immunotherapy", max_results=10)

# Filter papers by company affiliation
filtered_papers = fetcher.filter_by_company_affiliation(papers)

# Convert to DataFrame
df = fetcher.to_dataframe(filtered_papers)

# Save to CSV
df.to_csv("results.csv", index=False)

Examples

The package includes example scripts demonstrating various use cases:

# Run all examples
python examples/run_examples.py --email [e-mail]

# Run a specific example
python examples/basic_usage.py
python examples/advanced_filtering.py

See the examples directory for more information.

Output Format

The program generates a CSV file with the following columns:

• PubmedID: Unique identifier for the paper
• Title: Title of the paper
• Publication Date: Date the paper was published
• Non-academic Author(s): Names of authors affiliated with non-academic institutions
• Company Affiliation(s): Names of pharmaceutical/biotech companies
• Corresponding Author Email: Email address of the corresponding author

Code Organization

The package is organized as follows:

• pubmed_fetcher/: Main package directory
  • __init__.py: Package initialization
  • pubmed_fetcher.py: Core functionality (module)
  • main.py: Command-line interface
• tests/: Test directory
  • test_main.py: Tests for the module
• examples/: Example scripts
  • basic_usage.py: Basic usage example
  • advanced_filtering.py: Advanced filtering example
  • run_examples.py: Script to run all examples
• pyproject.toml: Poetry configuration
• setup.py: Setuptools configuration (for compatibility)
• requirements.txt: Development dependencies

Development

1. Clone the repository:

git clone https://github.com/yourusername/pharma-pubmed-fetcher.git
cd pharma-pubmed-fetcher

2. Install development dependencies:

poetry install

3. Run tests:

poetry run pytest

4. Format code:

poetry run black pubmed_fetcher tests
poetry run isort pubmed_fetcher tests

5. Run linters:

poetry run flake8 pubmed_fetcher tests
poetry run mypy pubmed_fetcher

6. Check publication readiness:

python check_publication_readiness.py

Publishing

To PyPI

1. Create a PyPI account at https://pypi.org/
2. Configure Poetry with your PyPI credentials:

poetry config pypi-token.pypi your-token-here

3. Run the publish script:

On Linux/macOS:

chmod +x publish_to_pypi.sh
./publish_to_pypi.sh

On Windows:

publish_to_pypi.bat

To Test-PyPI

1. Create a Test-PyPI account at https://test.pypi.org/
2. Configure Poetry to use Test-PyPI:

poetry config repositories.testpypi https://test.pypi.org/legacy/

3. Run the publish script:

On Linux/macOS:

chmod +x publish_to_testpypi.sh
./publish_to_testpypi.sh

On Windows:

publish_to_testpypi.bat

Tools and Libraries Used

Development Tools

• Python 3.10+
• Git for version control
• GitHub for repository hosting
• Poetry for dependency management
• Claude 3.5 Sonnet for code assistance and development

Python Libraries

• Biopython - For PubMed data retrieval
• Click - For command-line interface
• Pandas - For data manipulation and CSV handling
• Requests - For HTTP requests

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request