PyDistances#
Data scientists address real-world problems using multivariate and heterogeneous datasets, characterized by multiple variables of different natures. Selecting a suitable distance function between units is crucial, as many statistical techniques and machine learning algorithms depend on this concept. Traditional distances, like classical Gower’s or Euclidean, are unsuitable for mixed-type data when underlying correlation or outlying observations are present, and often lead to suboptimal results.
In the paper Grané, A., Scielzo-Ortiz, F.: PyDistances: A Python package for mixed-type
data. Submitted to SORT (2024) robust distances for mixed-type data were defined and explored, like robust
Generalized Gower’s and robust Related Metric Scaling, and this new Python package called PyDistances was developed, which enables to compute these robust proposals as well as classical ones.
The package is located in Python Package Index (PyPI), the standard repository of packages for the Python programming language: https://pypi.org/project/PyDistances/