The Best Polymarket Data Libraries

The stack has to survive three motions without breaking code: joining book and tape on the second, resampling 250ms frames to bars, and filtering the one-sided/crossed flags in and out of a study.

Every library in the ranking handles those; the tie-breakers are memory footprint (a single 5-minute market's frames are ~2,400 rows, but a month of a series is millions) and portability of the Parquet files.

The archive ships CSV, JSON, and Parquet; for anything beyond a single market, Parquet is the honest default — columnar reads, compressed, and DuckDB/Polars-native.

The practice this site recommends: keep the raw frames canonical, build views (bars, metrics) as queries, and never store an aggregation that discards the flags.

Library choice is secondary to format and discipline: the strongest stack is DuckDB over Parquet with raw-frames-canonical storage, and the weakest is a JSON file you parse a dozen times a day. The ranking just makes that concrete.

A library earns its slot at the dataset's real size: the open Zenodo release alone is 897,192 snapshots, and the full archive runs to 143.5 million L2 rows and 118,251 resolved markets across 8 coins. Any library that chokes at that volume is a toy for the purpose this page ranks for.

Polars and DuckDB handle the full corpus in memory on a laptop; pandas handles it in slices; a notebook-first library without a query engine forces you to load everything. The ranked order reflects that measured reality, not preference.

• Data in DuckDB
• Parquet format
• Python guide