The speed improvements are worth it given how easy it is to use, I have seen literally a 1000x speedup on a time-series data analysis script from adding about 10 lines of static type annotations.
The functionality I like best about it are:
* It can be used transparently without any makefiles or compilation steps. Add "import pyximport; pyximport.install()" at the top of your main script, and now every imported Cython-capable module is compiled on the fly at runtime.
* You can start out with no changes to your python modules. All libraries and features still work within compiled modules. Then you can slowly start adding static types to a few variables at a time. The annotated variables become very fast native C integers/doubles/functions, instead of Python objects.
* Install it with pip, the packages in your OS distribution may be out of date,
* Rename your numeric code module's .py file to .pyx,
* Use pyximport from the main script ( http://docs.cython.org/src/userguide/source_files_and_compil...) to have it compiled at runtime without any extra build steps, and then
* Start experimenting with adding a few "cdef"s (http://docs.cython.org/src/quickstart/cythonize.html and http://docs.cython.org/src/tutorial/numpy.html)
My experience with numerical algorithms in Python has been a 10000x speedup and 10x memory reduction by using Numpy arrays and C/fortran functions. And when Numpy doesn't have a necessary function, a few extra lines of static C types and on-import compilation with Cython has still resulted in a 1000x speedup.
"Optimizing Python" needs to look at the whole existing ecosystem of C libraries - to be useful the end result has to be faster than what's possible now, not just faster than pure Python.