  In recent years, tree tensor network methods have proven capable of
simulating quantum many-body and other high-dimensional systems. This work is a
user guide to our Python library PyTreeNet. It includes code examples and
exercises to introduce the library's functions and familiarise the reader with
the concepts and methods surrounding tree tensor networks. PyTreeNet implements
all the tools required to implement general tree tensor network methods, such
as tensor decompositions and arbitrary tree structures. The main focus is on
the time evolution of quantum systems. This includes an introduction to tree
tensor network states and operators and the time-evolving block decimation and
time-dependent variational principle. The library's capabilities are showcased
with the example of a modified transverse field Ising model on tree structures
that go far beyond the ability of common state vector methods.
