DyND is a dynamic array library for structured and semi-structured data, written with C++ as a first-class target and extended to Python with a lightweight binding. It aims to be a cross-language platform for data analysis, by bringing the popularity and flexibility of the Python data science stack to other languages, such as C++, R, and Javascript. It is inspired by NumPy, the Python array programming library at the core of the scientific Python stack, but tries to address a number of obstacles encountered by some of NumPy’s users. Examples of these are support for variable-sized strings, missing values, ragged array dimensions, and versatile tools for creating functions that apply generic patterns across arrays.

This talk will introduce the DyND library, motivating it with simple datasets that are hard to process with existing tools. We'll discuss its architecture, features, and a roadmap for the future.