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    <p>Adding my $0.02 for the record.</p> <p>My work involves developing numeric models that run over 100's of gigabytes of data. The hard problems are in coming up with a revenue-generating solution quickly (i.e. time-to-market). To be commercially successful the solution also has to <em>execute</em> quickly (compute the solution in minimal amounts of time).</p> <p>For us Python has proven to be an excellent choice to develop solutions for the reasons commonly cited: fast development time, language expressiveness, rich libraries, etc. But to meet the execution speed needs we've adopted the 'Hybrid' approach that several responses have already mentioned. </p> <ol> <li>Using numpy for computationally intense parts. We get within 1.1x to 2.5x the speed of a 'native' C++ solution with numpy with less code, fewer bugs, and shorter development times.</li> <li>Pickling (Python's object serialization) intermediate results to minimize re-computation. The nature of our system requires multiple steps over the same data, so we 'memorize' the results and re-use them where possible.</li> <li>Profiling and choosing better algorithms. It's been said in other responses, but I'll repeat it: we whip-out cProfile and try to replace hot-spots with a better algorithm. Not applicable in all cases.</li> <li>Going to C++. If the above fails then we call a C++ library. We use <a href="http://code.google.com/p/pybindgen/" rel="noreferrer" title="PyBindGen">PyBindGen</a> to write our Python/C++ wrappers. We found it far superior to SWIG, SIP, and Boost.Python as it produces direct Python C API code without an intermediate layer. </li> </ol> <p>Reading this list you might think "What a lot of re-work! I'll just do it in [C/C++/Java/assembler] the first time around and be done with it." </p> <p>Let me put it into perspective. Using Python we were able to produce a working revenue-generating application in 5 weeks that, in other languages, had previously required 3 months for projects of similar scope. This includes the time needed to optimize the Python parts we found to be slow.</p>
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