Rather than just punting to C, I'd suggest:

Make your code count. Do more with fewer executions of lines:

• Change the algorithm to a faster one. It doesn't need to be fancy to be faster in many cases.
• Use python primitives that happens to be written in C. Some things will force an interpreter dispatch where some wont. The latter is preferable
• Beware of code that first constructs a big data structure followed by its consumation. Think the difference between range and xrange. In general it is often worth thinking about memory usage of the program. Using generators can sometimes bring O(n) memory use down to O(1).
• Python is generally non-optimizing. Hoist invariant code out of loops, eliminate common subexpressions where possible in tight loops.
• If something is expensive, then precompute or memoize it. Regular expressions can be compiled for instance.
• Need to crunch numbers? You might want to check numpy out.
• Many python programs are slow because they are bound by disk I/O or database access. Make sure you have something worthwhile to do while you wait on the data to arrive rather than just blocking. A weapon could be something like the Twisted framework.
• Note that many crucial data-processing libraries have C-versions, be it XML, JSON or whatnot. They are often considerably faster than the Python interpreter.

If all of the above fails for profiled and measured code, then begin thinking about the C-rewrite path.