sequimini

Sequimini is an open‑source software library for the Python programming language that provides utilities for processing and analysing sequential data such as time series, DNA strings, and event logs. The project was initiated in 2016 by a group of data scientists seeking a lightweight alternative to existing toolkits that combined functional programming concepts with efficient vectorised operations. The first public release, version 0.1.0, was hosted on GitHub under the Apache 2.0 license and quickly attracted contributions from developers in academia and industry.

The core of Sequimini consists of three modules: a sequence abstraction layer that normalises heterogeneous data formats; a collection of transformation pipelines for tasks such as smoothing, resampling, and feature extraction; and a set of statistical tests and visualisation helpers tailored for ordered data. The library is built on top of NumPy and Pandas, leveraging their performance while exposing a higher‑level API that encourages readable, chainable code. Compatibility with Python 3.8 and later is maintained, and optional extensions provide integration with machine‑learning frameworks such as scikit‑learn and PyTorch.

Sequimini is used in research on climate modelling, bioinformatics, and financial analytics, where analysts require reproducible preprocessing of large‑scale sequential datasets. Documentation includes tutorials, an API reference, and example notebooks. The development community follows a standard pull‑request workflow, and releases are versioned according to semantic‑versioning principles. As of 2024, the library has over 1 500 stars on GitHub and is included in several university curricula for data‑science courses.