Quickstart: FHIR REST To DataFrame

Use this when you want to test a FHIR endpoint and inspect resources as a table.

Install

bash

uv add fyron

Query A Small Page

python

from fyron import FHIRRestClient

client = FHIRRestClient("https://hapi.fhir.org/baseR4")

patients = client.search_df(
    "Patient",
    params={"_count": 20},
    max_pages=1,
)

print(patients.head())

Partition Broad Searches

For local or institutional servers, prefer bounded searches during exploration. Use partitioned searches when a resource type is too large for one query.

python

observations = client.search_df_partitioned(
    "Observation",
    partition_param="date",
    start="2024-01-01",
    end="2024-02-01",
    freq="7D",
    params={"_count": 100},
    max_pages_per_partition=2,
)

Next Steps

Use FHIR REST for pagination, partitioning, authentication, and custom extraction.
Use FHIR Paths & Custom Extraction to extract nested fields without preprocessing callbacks.
Keep private endpoints, credentials, and real payloads out of notebooks shared publicly.