This paper details a computationally efficient and versatile Python package (BinMod1D v1.0.10) that explicitly evolves spectral bin distributions and corresponding polarimetric radar variables for rain or snow according to atmospheric collisional coalescence and breakup processes. BinMod1D can be executed as a box model, a 1D steady-state model in height, or a full (time and height) 1D column model utilizing multiple particle categories, each of which can have their own densities, aspect ratios, and fall speeds. Forward simulations of polarimetric radar variables are implemented using standard Rayleigh analytic scattering equations. Two-moment (mass and number) or one-moment (mass only) particle interaction calculations follow a source-based approach but with parallelizable just-in-time (JIT) compilation for high performance. BinMod1D box model solutions are validated using analytic solutions of collision-coalescence using a variety of kernels as well as for breakup and the steady-state balance of coalescence with breakup. BinMod1D capabilities are demonstrated through steady-state simulations of rainfall and snow signatures, as well as vertical profiles of diverse meteorological scenarios. Convergence and timing tests are provided for the meteorological scenario of a cloud to rain transition using a realistic collision kernel and fragment distribution. BinMod1D is intended to enable cloud microphysics and weather radar researchers to efficiently simulate vertical profiles of complex weather events. Such a tool can be used to provide reference solutions for training machine learning models and validating various retrieval methodologies.