  Machine learning techniques have been paramount throughout the last years,
being applied in a wide range of tasks, such as classification, object
recognition, person identification, and image segmentation. Nevertheless,
conventional classification algorithms, e.g., Logistic Regression, Decision
Trees, and Bayesian classifiers, might lack complexity and diversity, not
suitable when dealing with real-world data. A recent graph-inspired classifier,
known as the Optimum-Path Forest, has proven to be a state-of-the-art
technique, comparable to Support Vector Machines and even surpassing it in some
tasks. This paper proposes a Python-based Optimum-Path Forest framework,
denoted as OPFython, where all of its functions and classes are based upon the
original C language implementation. Additionally, as OPFython is a Python-based
library, it provides a more friendly environment and a faster prototyping
workspace than the C language.
