Yes, I have. I wrote a row-count program for a binary (length-prefixed rather than delimited) bcp output file once and ended up having to redo it in C because the python one was too slow. This program was quite small (it only took a couple of days to re-write it in C), so I didn’t bother to try and build a hybrid application (python glue with central routines written in C) but this would also have been a viable route.
A larger application with performance critical bits can be written in a combination of C and a higher level language. You can write the performance-critical parts in C with an interface to Python for the rest of the system. SWIG, Pyrex or Boost.Python (if you’re using C++) all provide good mechanisms to do the plumbing for your Python interface. The C API for python is more complex than that for Tcl or Lua, but isn’t infeasible to build by hand. For an example of a hand-built Python/C API, check out cx_Oracle.
This approach has been used on quite a number of successful applications going back as far as the 1970s (that I am aware of). Mozilla was substantially written in Javascript around a core engine written in C. Several CAD packages, Interleaf (a technical document publishing system) and of course EMACS are substantially written in LISP with a central C, assembly language or other core. Quite a few commercial and open-source applications (e.g. Chandler or Sungard Front Arena) use embedded Python interpreters and implement substantial parts of the application in Python.
EDIT: In rsponse to Dutch Masters’ comment, keeping someone with C or C++ programming skills on the team for a Python project gives you the option of writing some of the application for speed. The areas where you can expect to get a significant performance gain are where the application does something highly iterative over a large data structure or large volume of data. In the case of the row-counter above it had to inhale a series of files totalling several gigabytes and go through a process where it read a varying length prefix and used that to determine the length of the data field. Most of the fields were short (just a few bytes long). This was somewhat bit-twiddly and very low level and iterative, which made it a natural fit for C.
Many of the python libraries such as numpy, cElementTree or cStringIO make use of an optimised C or FORTRAN core with a python API that facilitates working with data in aggregate. For example, numpy has matrix data structures and operations written in C which do all the hard work and a Python API that provides services at the aggregate level.