pubmed-client-py 0.2.0

Python bindings for PubMed and PMC APIs for retrieving biomedical research articles

PubMed Client - Python Bindings

Python bindings for the PubMed and PMC (PubMed Central) API client library.

Overview

This package provides Python bindings to the Rust-based PubMed client library, enabling high-performance access to PubMed and PMC APIs from Python.

Features

• PubMed API: Search and retrieve article metadata
• PMC API: Access full-text articles from PubMed Central
• ELink API: Get related articles, citations, and PMC links
• SearchQuery Builder: Build complex queries programmatically with filters
• High Performance: Built with Rust for speed and reliability
• Type-Safe: Full type hints for better IDE support

Installation

From PyPI

pip install pubmed-client-py

With uv

uv add pubmed-client-py

From Source

# Clone the repository
git clone https://github.com/illumination-k/pubmed-client.git
cd pubmed-client/pubmed-client-py

# Create virtual environment and install
uv venv
uv run --with maturin maturin develop

Quick Start

Basic Usage

import pubmed_client

# Create a unified client
client = pubmed_client.Client()

# Search PubMed
articles = client.pubmed.search_and_fetch("covid-19 vaccine", max_results=10)

for article in articles:
    print(f"Title: {article.title}")
    print(f"PMID: {article.pmid}")
    print(f"Journal: {article.journal}")
    print()

Fetch Single Article

import pubmed_client

client = pubmed_client.Client()

# Fetch article by PMID
article = client.pubmed.fetch_article("31978945")

print(f"Title: {article.title}")
print(f"Authors: {article.author_count}")
print(f"Abstract: {article.abstract_text[:200]}...")

# Access authors and affiliations
for author in article.authors():
    print(f"  {author.full_name}")
    if author.orcid:
        print(f"    ORCID: {author.orcid}")

Fetch PMC Full-Text

import pubmed_client

client = pubmed_client.Client()

# Fetch full text from PMC
full_text = client.pmc.fetch_full_text("PMC7906746")

print(f"Title: {full_text.title}")
print(f"Sections: {len(full_text.sections())}")
print(f"References: {len(full_text.references())}")

# Access article sections
for section in full_text.sections():
    print(f"Section: {section.title}")
    print(f"Content: {section.content[:200]}...")

# Access figures and tables
for figure in full_text.figures():
    print(f"Figure: {figure.label}")
    print(f"Caption: {figure.caption}")

# Convert to Markdown
markdown = full_text.to_markdown()
print(markdown)

Citation Analysis

import pubmed_client

client = pubmed_client.Client()

# Get citations for an article
citations = client.get_citations([31978945])
print(f"Citation count: {len(citations)}")

# Get citing article PMIDs
for citing_pmid in citations.citing_pmids[:10]:
    print(f"Cited by: {citing_pmid}")

Related Articles and PMC Links

import pubmed_client

client = pubmed_client.Client()

# Find related articles
related = client.get_related_articles([31978945])
print(f"Found {len(related.related_pmids)} related articles")

# Check PMC full-text availability
pmc_links = client.get_pmc_links([31978945])
print(f"PMC IDs available: {pmc_links.pmc_ids}")

Using SearchQuery Builder

import pubmed_client

client = pubmed_client.Client()

# Build a complex query
query = (
    pubmed_client.SearchQuery()
    .query("cancer")
    .published_between(2020, 2024)
    .article_type("Clinical Trial")
    .free_full_text_only()
    .limit(50)
)

# Execute the search
articles = client.pubmed.search_and_fetch(query, 0)  # limit ignored when using SearchQuery

for article in articles:
    print(f"[{article.pmid}] {article.title}")

Boolean Query Combinations

import pubmed_client

# Build complex queries with boolean logic
q1 = pubmed_client.SearchQuery().query("covid-19")
q2 = pubmed_client.SearchQuery().query("vaccine")
q3 = pubmed_client.SearchQuery().query("efficacy")

# Combine with AND
combined = q1.and_(q2).and_(q3)
print(combined.build())  # ((covid-19) AND (vaccine)) AND (efficacy)

# Combine with OR
either = q1.or_(q2)
print(either.build())  # (covid-19) OR (vaccine)

# Exclude specific terms
base = pubmed_client.SearchQuery().query("treatment")
excluded = pubmed_client.SearchQuery().query("animal studies")
human_only = base.exclude(excluded)
print(human_only.build())  # (treatment) NOT (animal studies)

Extract Figures from PMC Articles

import pubmed_client

client = pubmed_client.Client()

# Download and extract figures with captions
figures = client.pmc.extract_figures_with_captions("PMC7906746", "./output")

for fig in figures:
    print(f"Figure: {fig.figure.label}")
    print(f"Caption: {fig.figure.caption}")
    print(f"File: {fig.extracted_file_path}")
    print(f"Size: {fig.file_size} bytes")
    if fig.dimensions:
        print(f"Dimensions: {fig.dimensions[0]}x{fig.dimensions[1]}")

Configuration

With API Key

Using an NCBI API key increases rate limits from 3 to 10 requests per second:

import pubmed_client

config = (
    pubmed_client.ClientConfig()
    .with_api_key("your_ncbi_api_key")
    .with_email("[e-mail]")
    .with_tool("YourAppName")
)

client = pubmed_client.Client.with_config(config)

Rate Limiting

import pubmed_client

config = (
    pubmed_client.ClientConfig()
    .with_rate_limit(2.0)  # 2 requests per second
    .with_timeout_seconds(60)  # 60 second timeout
)

client = pubmed_client.Client.with_config(config)

Type Hints and IDE Support

The package includes complete type stubs (.pyi files) for full IDE autocomplete and type checking:

import pubmed_client

# Type hints work automatically
config: pubmed_client.ClientConfig = pubmed_client.ClientConfig()
client: pubmed_client.Client = pubmed_client.Client.with_config(config)
articles: list[pubmed_client.PubMedArticle] = client.pubmed.search_and_fetch("query", 10)

Run type checking with mypy:

mypy your_script.py

Development

Prerequisites

• Python >= 3.12
• Rust toolchain (installed via rustup)
• uv (for Python package management)
• maturin (for building Python bindings)

Setup Development Environment

# Install uv if not already installed
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create virtual environment
cd pubmed-client-py
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Build and install in development mode
uv run --with maturin maturin develop

Running Tests

# Install dev dependencies
uv sync --group dev

# Run tests
uv run pytest

# Run tests with coverage
uv run pytest --cov=pubmed_client

Code Quality

# Format code
uv run ruff format

# Lint code
uv run ruff check

# Type checking
uv run mypy tests/

Building

# Build wheel
uv run --with maturin maturin build --release

# Build for distribution
uv run --with maturin maturin build --release --sdist

Publishing

# Publish to PyPI (requires credentials)
uv run --with maturin maturin publish

License

MIT

Links

• Repository
• PyPI Package
• Core Rust Library
• CLI Tool
• WebAssembly Bindings
• NCBI E-utilities Documentation