Generally it’s an awful way to write code, and does not guarantee that it will be any faster. Things which are simple and fast in one language can be complex and slow in another. You’re better off either learning how to write fast Python code or learning C++ directly than fighting with a translator and figuring out how to make the generated code run acceptably.
If you want C++, use C++. Note, however that PyPy have a bunch of benchmarks showing that they can be much faster than C; and with NumPy, which uses compiled extensions, numerical work becomes much faster and easier.
If you want to programme in something statically compiled, and a bit like Python, there’s RPython.
Finally, you can do what NumPy does: use extensions written in C or C++ for most of your heavy computational lifting, where that appears to be appropriate, either because profiling shows a hotspot, or because you need an extension to more easily do something involving python’s internals. Note that this will tie your code to a particular implementation.
Similar to what was already stated, C++ may be faster in some areas and slower in others. Python is exactly the same. In the end, any language will be converted into machine code. It is really up to the compiler in the end to make it as efficient as it knows how to do. That said, it is better to pick one language and learn how to write fast and efficient code to do what you want.