  We introduce a stable, well tested Python implementation of the
affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed
by Goodman & Weare (2010). The code is open source and has already been used in
several published projects in the astrophysics literature. The algorithm behind
emcee has several advantages over traditional MCMC sampling methods and it has
excellent performance as measured by the autocorrelation time (or function
calls per independent sample). One major advantage of the algorithm is that it
requires hand-tuning of only 1 or 2 parameters compared to $\sim N^2$ for a
traditional algorithm in an N-dimensional parameter space. In this document, we
describe the algorithm and the details of our implementation and API.
Exploiting the parallelism of the ensemble method, emcee permits any user to
take advantage of multiple CPU cores without extra effort. The code is
available online at http://dan.iel.fm/emcee under the MIT License.
