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The collection of gravitational waves (GWs) that are either too weak or toonumerous to be individually resolved is commonly referred to as thegravitational-wave background (GWB). A confident detection and model-drivencharacterization of such a signal will provide invaluable information about theevolution of the Universe and the population of GW sources within it. Wepresent a new, user-friendly Python--based package for gravitational-wave dataanalysis to search for an isotropic GWB in ground--based interferometer data.We employ cross-correlation spectra of GW detector pairs to construct anoptimal estimator of the Gaussian and isotropic GWB, and Bayesian parameterestimation to constrain GWB models. The modularity and clarity of the codeallow for both a shallow learning curve and flexibility in adjusting theanalysis to one's own needs. We describe the individual modules which make up{\tt pygwb}, following the traditional steps of stochastic analyses carried outwithin the LIGO, Virgo, and KAGRA Collaboration. We then describe the built-inpipeline which combines the different modules and validate it with both mockdata and real GW data from the O3 Advanced LIGO and Virgo observing run. Wesuccessfully recover all mock data injections and reproduce published results.

pygwb: Python-based library for gravitational-wave background searches