Python Examples¶

Real-world examples showing how to use PMCGrab effectively.

Interactive Jupyter Tutorial¶

For a complete hands-on experience, check out our interactive Jupyter notebook:

PMCGrab Tutorial Notebook

You can also download it directly from GitHub: pmcgrab_tutorial.ipynb

This notebook covers:

Installation and setup (including uv)
Single paper processing
Batch processing with multiple papers
Data analysis and visualization
RAG chunk preparation
Export formats for different use cases

Code Examples¶

Batch Processing Multiple Papers¶

This example demonstrates how to process multiple PMC articles and save them as JSON files:

# ─── examples/run_three_pmcs.py ──────────────────────────────────────────────
import json
from pathlib import Path

from pmcgrab.application.processing import process_single_pmc

# The PMC IDs we want to process
PMC_IDS = ["7114487", "3084273", "7690653", "5707528", "7979870"]

OUT_DIR = Path("pmc_output")
OUT_DIR.mkdir(exist_ok=True)

for pmcid in PMC_IDS:
    print(f"Fetching PMC{pmcid}...")
    data = process_single_pmc(pmcid)
    if data is None:
        print(f"  FAILED to parse PMC{pmcid}")
        continue

    # Pretty-print a few key fields
    title = data["article"]["title"]["main"]
    abstract_blocks = data["content"]["abstracts"][0]["blocks"]
    abstract_preview = abstract_blocks[0]["text"] if abstract_blocks else ""
    print(
        f"  Title   : {title[:80]}{'…' if len(title) > 80 else ''}\n"
        f"  Abstract: {abstract_preview[:120]}{'…' if len(abstract_preview) > 120 else ''}\n"
        f"  Authors : {len(data['article']['contributors']['authors'])}"
    )

    # Persist full JSON
    dest = OUT_DIR / f"PMC{pmcid}.json"
    with dest.open("w", encoding="utf-8") as fh:
        json.dump(data, fh, indent=2, ensure_ascii=False)
    print(f"  JSON saved to {dest}\n")

Key Features Demonstrated¶

Batch Processing: Efficiently processes multiple PMC articles
Error Handling: Gracefully handles failed parsing attempts
Pretty Output: Shows key information during processing
JSON Export: Saves complete article data as structured JSON files
NCBI Settings: Uses the built-in email rotation and rate configuration

Expected Output¶

Fetching PMC7114487...
  Title   : COVID-19 pandemic response and lessons learned from the…
  Abstract: The COVID-19 pandemic has posed unprecedented challenges to global health…
  Authors : 5
  JSON saved to pmc_output/PMC7114487.json

Fetching PMC3084273...
  Title   : Machine learning approaches in genomics and personalized medicine…
  Abstract: Recent advances in machine learning have revolutionized the field of…
  Authors : 3
  JSON saved to pmc_output/PMC3084273.json

This example is perfect for:

Building literature review datasets
Creating training data for AI models
Systematic research paper analysis
Academic research workflows