  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.
