08-conclusion.rst

Conclusion
===============================================================================

You've reached the end of this book. I hope you've learned something while
reading it, I sure learned a lot writing it. Trying to explain something is a
generally a good exercise to test for your knowledge of this thing. Of course,
we only scratched the surface of NumPy and there are many things left to
discover. Have a look at the bibliography for books written by true experts, at
the documentation written by people making NumPy and don't hesitate to ask your
questions on the mailing lists because the NumPy community is very friendly.

If there's a single message to retain from this book it is "premature
optimization is the root of all evil". We've seen that code vectorization can
drastically improve your computation, with several orders of magnitude in some
cases. Still, problem vectorization is generally much more powerful. If you
write code vectorization too early in your design process, you won't be able to
think out-of-the-box and you'll certainly miss some really powerful alternatives
because you won't be able to identify your problem properly as we've
seen in the problem vectorization chapter. This requires some experience and
you have to be patient: experience is not an overnight process.

Finally, custom vectorization is an option worth considering once you've looked
at the alternatives to NumPy. When nothing works for you, NumPy still offers
you a clever framework to forge your own tools. And who knows, this can be the
start of an exciting adventure for you and the community as it happened to me
with the `glumpy <http://glumpy.github.io>`_ and the `vispy
<http://vispy.org>`_ packages.