Analyze PubMed Papers With Python

Export JSON from the PubMed Research Scraper, then load it with pandas:

import pandas as pd

papers = pd.read_json("dataset_items.json")
papers["publication_date"] = pd.to_datetime(papers["publication_date"], errors="coerce")
papers = papers.drop_duplicates("pmid")
print(papers.groupby(papers.publication_date.dt.year).size())

Arrays need deliberate handling. Use explode("mesh_terms") for subject-heading counts or explode("authors") for descriptive collaboration analysis. Keep the untouched table so an exploded row count is never mistaken for a paper count.

Before modeling, profile missing abstracts, DOIs, affiliations, and dates by year and journal. Older records often have different metadata coverage. For text analysis, strip markup conservatively, retain PMID as the traceable key, and inspect samples from every transformation. A chart is much easier to trust when each point can be traced back to a source record and the exact search input.

Separate paper-level and author-level questions

Keep one dataframe at one row per PMID for publication counts, journal share, and abstract coverage. Create separate exploded tables for authors and MeSH terms. Mixing these grains is a common source of inflated totals: a paper with eight authors becomes eight rows after explode, but it is still one publication.

paper_year = papers.groupby(papers.publication_date.dt.year)["pmid"].nunique()
mesh = papers[["pmid", "mesh_terms"]].explode("mesh_terms").dropna()
top_mesh = mesh.groupby("mesh_terms")["pmid"].nunique().sort_values().tail(20)

Use nunique("pmid") whenever an exploded table feeds a paper count. Save both the query and the dataset collection time beside the chart so another analyst can distinguish a source update from an analytical change.

Add quality checks before interpretation

Create a small validation table with total PMIDs, duplicate PMIDs, missing titles, missing dates, abstract coverage, DOI coverage, and the earliest and latest publication dates. Review several rows from the largest journals and from every year in scope. If missingness changes sharply over time, report the coverage rate next to the trend instead of treating the series as uniformly complete.

PubMed metadata supports landscape analysis, not clinical conclusions by itself. Publication counts do not measure evidence quality, and MeSH terms reflect indexing decisions as well as subject matter. Keep derived metrics separate from source fields, link every aggregate back to its PMIDs, and send consequential medical interpretations through an appropriate domain-review process.