There are about 200 open "Numpy Enhancement Proposal" issues on Github, https://github.com/numpy/numpy/labels/01%20-%20Enhancement

Many things could be improved in NumPy:

  * make it easier to implement and deploy custom dtypes, fix the time-related dtype
  * support for ragged arrays
  * consolidate internals, especially around ufuncs

I also think some non trivial part of pandas lowest levels belong to NumPy, though I have not thought very deeply about that one: support for missing value, some kind of indexing, etc...

Yes, I agree. I would love to see parts of what is in Pandas actually supported better in NumPy.

That's not to say there aren't things NumPy doesn't do that it could. How about lazy evaluation or even just matrix chain multiplication? Either could save a lot of computation.

Matrix chain multiplication was added to NumPy recently, see numpy.linalg.multi_dot: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/...

Hooray! Still, it's not exactly transparent in a way that you could get with lazy array evaluation.

Dynamic arrays anyone?