Public healthcare datasets are some of the largest open data you can get your hands on. The CMS Medicare Part D "by Provider and Drug" file is tens of millions of rows: one row for every combination of prescriber and drug billed to Medicare in a year. The instinct is to stand up a warehouse, write a loader, and wait. You do not need any of that. With DuckDB you can query the whole file on a laptop, in seconds, from Python.
In this tutorial we build a peer-group outlier finder over that prescriber data. The idea: group providers into fair peer groups (same specialty, same drug), compute how far each provider's billing sits from the peer-group average in standard deviations, and rank the biggest deviations. The technique is general. Swap the dataset and the peer-group definition and the same code finds outliers in procedure billing, lab volumes, or any aggregate table you have.
One thing up front, because it matters: an outlier is a lead, not a verdict. Being far from the mean means "worth a human look," nothing more. We will come back to why.
Why DuckDB
DuckDB is an in-process analytical database. It runs inside your Python process the way SQLite does, so there is no server to install and no cluster to manage. It is columnar and vectorized, which is exactly the shape that aggregate queries over wide tables want, and it reads CSV and Parquet files directly off disk or over HTTPS without an import step. For a one-machine analysis of a large public file, that combination (zero infra, reads files in place, fast on aggregates) is hard to beat.
