This Python module uses matplotlib to visualize multidimensional samples through scatterplot matrices, revealing covariances in one- and two-dimensional projections. Originally designed for Markov Chain Monte Carlo simulations, it is versatile for various data types. Development occurs on GitHub, and the code is archived on Zenodo with a DOI. The software is released under a Creative Commons Attribution 4.0 International License. It has been widely used in astronomical research and occasionally cited as corner.py or triangle.py. A simple example of its output is shown, demonstrating a scatterplot matrix generated by corner. The module is available on GitHub and is referenced in academic literature. The software is reviewed and maintained by its author, Daniel Foreman-Mackey, a Sagan Fellow at the University of Washington. The module is designed for clarity and efficiency in visualizing high-dimensional data.This Python module uses matplotlib to visualize multidimensional samples through scatterplot matrices, revealing covariances in one- and two-dimensional projections. Originally designed for Markov Chain Monte Carlo simulations, it is versatile for various data types. Development occurs on GitHub, and the code is archived on Zenodo with a DOI. The software is released under a Creative Commons Attribution 4.0 International License. It has been widely used in astronomical research and occasionally cited as corner.py or triangle.py. A simple example of its output is shown, demonstrating a scatterplot matrix generated by corner. The module is available on GitHub and is referenced in academic literature. The software is reviewed and maintained by its author, Daniel Foreman-Mackey, a Sagan Fellow at the University of Washington. The module is designed for clarity and efficiency in visualizing high-dimensional data.