pyMOR is a modern, object-oriented software library for building advanced model order reduction applications with the Python programming language. The main goal of pyMOR is to ease the integration of model order reduction algorithms with external high-dimensional solvers by expressing each such algorithm via operations on simple, application agnostic interface classes. Highlights of this release are: The introduction of the vector space concept for even simpler integration with external solvers. Addition of a generic Newton algorithm. Support for Jacobian evaluation of empirically interpolated operators. Greatly improved performance of the EI-Greedy algorithm. Addition of the DEIM algorithm. A new algorithm for residual operator projection and a new, numerically stable a posteriori error estimator for stationary coercive problems based on this algorithm. (Cf. A. Buhr, C. Engwer, M. Ohlberger, S. Rave, 'A numerically stable a posteriori error estimator for reduced basis approximations of elliptic equations', proceedings of WCCM 2014, Barcelona, 2014.) A new, easy to use mechanism for setting and accessing default values. Serialization via the pickle module is now possible for each class in pyMOR. (See the new 'analyze_pickle' demo.) Addition of generic iterative linear solvers which can be used in conjunction with any operator satisfying pyMOR's operator interface. Support for least squares solvers and PyAMG. An improved SQLite-based cache backend. Improvements to the built-in discretizations: support for bilinear finite elements and addition of a finite volume diffusion operator. Test coverage has been raised from 46% to 75%. Distribution packages for Ubuntu Linux can be obtained from our pyMOR PPA. pyMOR is also available at the Python Package Index an can be installed via pip. Further information can be found in the project's README file. Development of pyMOR has been supported by the German Federal Ministry of Education and Research (BMBF) under contract number 05M13PMA.