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PyTrilinos is a collection of Python modules that are useful for serial and parallel scientific computing. This collection contains modules that cover serial and parallel dense linear algebra, serial and parallel sparse linear algebra, direct and iterative linear solution techniques, domain decomposition and multilevel preconditioners, nonlinear solvers, and continuation algorithms. Also included are a variety of related utility functions and classes, including distributed I/O, coloring algorithms, and matrix generation. PyTrilinos vector objects are integrated with the popular NumPy Python module, gathering together a variety of high-level distributed computing operations with serial vector operations. PyTrilinos is a set of interfaces to existing, compiled libraries. This hybrid framework uses Python as front-end, and efficient precompiled libraries for all computationally expensive tasks. Thus, we take advantage of both the flexibility and ease of use of Python, and the efficiency of the underlying C++, C, and FORTRAN numerical kernels. Out numerical results show that, for many important problem classes, the overhead required by the Python interpreter is negligible. To run in parallel, PyTrilinos simply requires a standard Python interpreter. The fundamental MPI calls are encapsulated under an abstract layer that manages all interprocessor communications. This makes serial and parallel scripts using PyTrilinos virtually identical.