# FDApy: a Python package for functional data

arXiv: https://arxiv.org/abs/2101.11003 · HTML: https://mlsanity.com/paper/2101.11003/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains](https://mlsanity.com/paper/1509.02029/) (2015)
- [Clustering multivariate functional data using unsupervised binary trees](https://mlsanity.com/paper/2012.05973/) (2020)

## Built on by (most cited first, 2 of 2)

- [scikit-fda: A Python Package for Functional Data Analysis](https://mlsanity.com/paper/2211.02566/) (2022)
- [Hierarchical Probabilistic Principal Component Analysis of Longitudinal Data](https://mlsanity.com/paper/2604.22015/) (2026)

## Abstract (arXiv)

We introduce FDApy, an open-source Python package for the analysis of functional data. The package provides tools for the representation of (multivariate) functional data defined on different dimensional domains and for functional data that is irregularly sampled. Additionally, dimension reduction techniques are implemented for multivariate and/or multidimensional functional data that are regularly or irregularly sampled. A toolbox for generating functional datasets is also provided. The documentation includes installation and usage instructions, examples on simulated and real datasets and a complete description of the API. FDApy is released under the MIT license. The code and documentation are available at https://github.com/StevenGolovkine/FDApy.