#polars

Pandas, Polars, Spark, and Snowpark DataFrame comparison for humans and more!

Python DataFrames as Interactive DataTables

Market data acquisition, storage, and update workflows for machine learning for trading.

Utility package for comparing polars data frames.

Open-source API for financial data. Get quotes, historical data, technical indicators, and more.

Intuitive Data Workflows

A fast excel reader for Rust and Python

Zero-config entity resolution feeding a durable identity layer: messy records from any source become stable golden entities, a Customer 360 with provenance, merge/split and audit. Fellegi-Sunter beats hand-tuned Splink. Arrow-native/Rust, 250M rows in 11.2 min. Python + edge TypeScript (WASM), SQL-native in Postgres & DuckDB, 97 MCP tools + REST.

Event-driven backtesting for quantitative strategies with configurable execution, accounting, risk, and framework-parity validation.

sportsdataverse python package

Fluent, fully-typed assertion library for Python: your type checker sees only the assertions that fit the value, and every failure carries a structured diff. Soft assertions, polling, inline snapshots, OpenAPI contracts.

Datos públicos de Chile curados, normalizados y validados: geografía, demografía, economía, salud, educación y distritos electorales. Listos para consumir en una línea de código con Polars, DuckDB, SQLite y Excel.

Datagrunt is a Python library designed to simplify the way you work with CSV, Excel, Parquet, and PDF files. It provides a streamlined approach to reading, processing, and transforming your data into various formats, making data manipulation efficient and intuitive.

High-performance Excel and CSV file generation powered by Rust (~7-9x faster)

A high-performance, PRA PS1/26 compliant Risk-Weighted Assets (RWA) calculator for credit risk, built with Python and Polars.

Native SDKs for DuckLake.

Polars R bindings