The paper introduces emcee, a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman and Weare (2010). The code is open-source and has been used in several astrophysics projects. The algorithm offers several advantages over traditional MCMC methods, including reduced tuning requirements and excellent performance measured by the autocorrelation time. The implementation is designed to exploit parallelism, allowing users to take advantage of multiple CPU cores without additional effort. The paper details the algorithm, its implementation, and provides guidance on installation, usage, and troubleshooting. It also discusses the benefits of using emcee for probabilistic data analysis, particularly in astrophysics and cosmology, where models are computationally expensive and have many parameters. The paper emphasizes the importance of marginalization and the efficiency of MCMC methods in handling complex posterior distributions.The paper introduces emcee, a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman and Weare (2010). The code is open-source and has been used in several astrophysics projects. The algorithm offers several advantages over traditional MCMC methods, including reduced tuning requirements and excellent performance measured by the autocorrelation time. The implementation is designed to exploit parallelism, allowing users to take advantage of multiple CPU cores without additional effort. The paper details the algorithm, its implementation, and provides guidance on installation, usage, and troubleshooting. It also discusses the benefits of using emcee for probabilistic data analysis, particularly in astrophysics and cosmology, where models are computationally expensive and have many parameters. The paper emphasizes the importance of marginalization and the efficiency of MCMC methods in handling complex posterior distributions.