Abstract
We present the software package binary_c-python which provides a convenient and easy-to-use interface to the binary_c framework, allowing the user to rapidly evolve individual systems and populations of stars. binary_c-python is available on Pip and on GitLab. binary_c-python contains many useful features to control and process the output of binary_c, like by providing binary_c-python with logging statements that are dynamically compiled and loaded into binary_c. Moreover, we have recently added standardised output of events like Roche-lobe overflow or double compact-object formation to binary_c, and automatic parsing and managing of that output in binary_c-python. binary_c-python uses multiprocessing to utilise all the cores on a particular machine, and can run populations with HPC cluster workload managers like HTCondor and Slurm, allowing the user to run simulations on large computing clusters. We provide documentation that is automatically generated based on docstrings and a suite of Jupyter notebooks. These notebooks consist of technical tutorials on how to use binary_c-python and use-case scenarios aimed at doing science. Much of binary_c-python is covered by unit tests to ensure reliability and correctness, and the test coverage is continually increased as the package is improved.