We looked over the borders of Python in this chapter. Outside the Python ecosystem, programming languages such as R, C, Java, and Fortran are fairly popular. We checked out libraries that provide glue to connect Python with external code—rpy2 for R, SWIG and Boost for C, JPype for Java, and f2py for Fortran. Cloud computing aims to deliver computing power as a utility over the Internet. A brief overview of current Cloud computing services specialized in Python, including Google App Engine, PythonAnywhere, and Wakari was also given.

The next chapter, Chapter 12, Performance Tuning, Profiling, and Concurrency, gives hints on improving performance. Typically, we can speed up Python code by optimizing our code by using parallelization or rewriting parts of our code in C. We will discuss several profiling tools and concurrency APIs.