pyCARPool provides a custom class and functions to implement the Convergence Acceleration by Regression and Pooling (CARPool) method; this method exploits the correlation between simulations and surrogates to compute fast, reduced-variance statistics of large-scale structure observables without model error at the cost of only a few simulations. The method's estimates are unbiased, and achieve unbiased variance reduction factors of up to ∼10 without any further tuning. CARPool can also remove model error from ensembles of fast surrogates by combining them with a few high-accuracy simulations.