#data-science

Visualize large time series data with plotly.py

ETL / ELT / Reverse ETL Framework powered by DuckDB, designed to seamlessly integrate and process data from diverse sources. It leverages Markdown as a configuration medium, where YAML blocks define metadata for each data source, and embedded SQL blocks specify the extraction, transformation, and loading logic.

Bindings and ADO.NET Provider for DuckDB

Yet another portfolio management / asset allocation optimisation python library for quants, intitutes and retrail traders.

Advanced data visualization with Typst.

JupyDeep is an extension app for JupyterLab, your AI partner in Jupyter, powered by Pydantic AI/Agents — and beyond.

See your software development process through data. Everything runs locally. You own your data.

Numo::OpenBLAS builds and uses OpenBLAS as a background library for Numo::Linalg

Impress your boss with interactive Decision Tree visualization

Lightweight plotting to the terminal. 4x resolution via Unicode.

Serverless Cloud Optimized GeoTIFF (COG) dynamic tiling in WebAssembly. TiTiler-style XYZ tiles, no backend, built on whitebox-wasm.

Big-file data prep that never runs out of memory - an interactive shell and CLI, powered by DuckDB.

Parallel Barnes-Hut t-SNE implementation written in Rust.

CLI for fast, flexbile concatenation of tabular data using polars

GPandas is a high-performance data analysis and manipulation library for Go, inspired by Python’s popular pandas library.

Crystallize is a lightweight Python framework for rigorous, reproducible data science experiments. Define modular pipelines, apply treatments as experimental variations, and verify hypotheses with statistical tests, all with built-in caching, immutability, and parallelism for trustworthy, efficient results.

Data sets used in the book, gallery, or in examples of mlr3.

Matplot++: A C++ Graphics Library for Data Visualization 📊🗾

CLI task management & automation tool