title: Python with Numpy/Scipy vs. Pure C++ for Big Data Analysis
body: <p>Doing Python on relatively small projects makes me appreciate the dynamically typed nature of this language (no need for declaration code to keep track of types), which often makes for a quicker and less painful development process along the way. However, I feel that in much larger projects this may actually be a hindrance, as the code would run slower than say, its equivalent in C++.  But then again, using Numpy and/or Scipy with Python may get your code to run just as fast as a native C++ program (where the code in C++ would sometimes take longer to develop). </p>
  
  <p>I post this question after reading Justin Peel's comment on the thread "<a href="https://stackoverflow.com/questions/801657/is-python-faster-and-lighter-than-c">Is Python faster and lighter than C++?</a>" where he states:
  "Also, people who speak of Python being slow for serious number crunching haven't used the Numpy and Scipy modules. Python is really taking off in scientific computing these days. Of course, the speed comes from using modules written in C or libraries written in Fortran, but that's the beauty of a scripting language in my opinion."  Or as S. Lott writes on the same thread regarding Python: "...Since it manages memory for me, I don't have to do any memory management, saving hours of chasing down core leaks."
  I also inspected a Python/Numpy/C++ related performance question on "<a href="https://stackoverflow.com/questions/7596612/benchmarking-python-vs-c-using-blas-and-numpy">Benchmarking (python vs. c++ using BLAS) and (numpy)</a>" where J.F. Sebastian writes "...There is no difference between C++ and numpy on my machine."  </p>
  
  <p>Both of these threads got me to wondering whether there is any real advantage conferred to knowing C++ for a Python programmer that uses Numpy/Scipy for producing software to analyze 'big data' where performance is obviously of great importance (but also code readability and development speed are a must)? </p>
  
  <p>Note: I'm especially interested in handling huge text files. Text files on the order of 100K-800K lines with multiple columns, where Python could take a good five minutes to analyze a file "only" 200K lines long.</p>