title: Bundling a library so easier to maintain?
body: <p>I am releasing a small spectroscopy library soon and I've developed it using numpy 1.8, pandas 0.14 and matplotlib 1.4.  In both pandas and matplotlib, new features/API changes in the next release are certainly going to break some functionality in my code.  In fact, the pandas API change is so drastic for our needs, that it would require me to refactor 3 core classes in my code.  But I'd have to maintain the old classes for backwards compatibility...</p>
  
  <p>I'm a PhD student and don't have much time to maintain my code for every update in these libraries in the near future.  My solution is to bundle a few modules from the current versions of the dependency libraries, and then hopefully in the future make a more compatible API.  </p>
  
  <p>This really feels like a bad solution, but I can't count on being available to keep the library up to date with every API change of the dependencies.  What advice do you have in this regard?  An alternative is to encourage users to work in a virtual env., but that's a lot to ask them to configure just to test out my library.  </p>
  
  <p>Thanks</p>