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.
Speed nowdays its no longer an issue for python  since ctypes and cython emerged. Whats brilliant about cython is that your write python code and it generates c code without requiring from you to know a single line of c and then compiles to a library or you could even create a stanalone. Ctypes also is similar though abit slower. From the tests I have conducted cython code is as fast as c code and that make sense since cython code is translated to c code. Ctypes is abit slower.
So in the end its a question of profiling , see what is slow in python and move it to cython, or you could wrap your existing c libraries for python with cython. Its quite easy to achieve c speeds this way. 
So I will recommend not to waste the effort you invested creating these c libraries , wrap them with cython and do the rest with python. Or you could do all of it with cython if you wish as cython is python bar some limitations. And even allows you to mix c code as well. So you could do part of it in c and part of it python/cython. Depending what makes you feel more comfortable.
Numpy ans SciPy could be used as well for saving more time and providing ready to use solutions to your problems / needs.You should certainly check them out. Numpy has even has weaver a tool that let you inline c code inside your python code, just like you can inline assembly code inside your c code. But i think you would prefer to use cython . Remember because cython is both c and python at the same time it allows you to use directly c and python libraries.
Python vs. C++ for an application that does sparse linear algebra