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We now know that a large number of stars are born in multiple systems. Additionally, more than 70% of massive stars are found in close binary systems, meaning that they will interact over the course of their lifetime. This has strong implications for their evolution as well as the transients (e.g supernovae) and the potential gravitational wave progenitors they produce. Therefore, in order to understand and correctly interpret astronomical observations of stellar populations, we must use theoretical models able to account for the effects of binary stars. This is the case of the Binary Population and Spectral Synthesis code (BPASS), which has been a staple of the field for over 10 years. As is the case for most other theoretical models, the data products of BPASS are large, varied and complex. As a result, their use requires a level of expertise that is not immediately accessible to a wider community that may hold key observational data. The goal of hoki is to bridge the gap between observation and theory, by providing a set of tools to make BPASS data easily accessible and facilitate analysis. The use of Python is deliberate as it is a ubiquitous language within Astronomy. This allows BPASS results to be used naturally within the pre-existing workflow of most astronomers.