I'm writing an application where quite a bit of the computational time will be devoted to performing basic linear algebra operations (add, multiply, multiply by vector, multiply by scalar, etc.) on sparse matrices and vectors. Up to this point, we've built a prototype using C++ and the Boost matrix library. 
I'm considering switching to Python, to ease of coding the application itself, since it seems the Boost library (the easy C++ linear algebra library) isn't particularly fast anyway. This is a research/proof of concept application, so some reduction of run time speed is acceptable (as I assume C++ will almost always outperform Python) so long as coding time is also significantly decreased.
Basically, I'm looking for general advice from people who have used these libraries before. But specifically:
1) I've found scipy.sparse and and pySparse. Are these (or other libraries) recommended?
2) What libraries beyond Boost are recommended for C++? I've seen a variety of libraries with C interfaces, but again I'm looking to do something with low complexity, if I can get relatively good performance.
3) Ultimately, will Python be somewhat comparable to C++ in terms of run time speed for the linear algebra operations? I will need to do many, many linear algebra operations and if the slowdown is significant then I probably shouldn't even try to make this switch.
Thank you in advance for any help and previous experience you can relate.
I don't have directly applicable experience, but the scipy/numpy operations are almost all implemented in C.  As long as most of what you need to do is expressed in terms of scipy/numpy functions, then your code shouldn't be much slower than equivalent C/C++.
Python vs. C++ for an application that does sparse linear algebra