Efficiency needs to count programmer time, too. From watching first-year programmers in University struggle with C, to watching seasoned programmers struggle with C, I can assure you that Python wins on programmer efficiency. I've used C longer than I've used Python (~16 years for C, ~13 years for Python), so I am definitely comfortable with both, but I now use Python for virtually all general-purpose programming.
Even when CPU cycles count, I will usually prototype in Python to get all the algorithmic details right before porting to C. Often I won't even port the whole program; a number of my recent projects have had C routines called from Python front-end code (so that the front-end can handle stuff like HTTP requests, text parsing, response formatting and the like).
Finally, libraries like NumPy and Sage are taking Python beyond mere scripting and into the realm of serious scientific programming. It is now possible to write and use complex computer vision algorithms, mathematical algorithms, and heavy-duty number crunching (like MATLAB) in Python, meaning that a good amount of scientific computing is starting to be done with Python instead of more traditional languages like Perl, MATLAB or Java.