Rework functions to use ufuncs?

The implementation of e.g. nper:

https://github.com/numpy/numpy-financial/blob/master/numpy_financial/_financial.py#L229

requires the usual “split the array up using boolean indexes and then do different things to each hunk” stuff that you have to do when writing a function with conditional logic that operates on an entire array. This tends to lead to convoluted, hard-to-maintain code. (And nper still has more edge cases that need to be handled, so there will need to be more boolean arrays!)

One solution to this problem is to write ufuncs so that you can work with scalars and just use conditional logic. The numpy_financial functions can’t be full ufuncs because they have default arguments, but most of them can be very thin wrappers around a ufunc.

The disadvantage of introducing ufuncs is that you introduce compiled code, but perhaps it is worth it? Code for generating the loops from scalar kernels can be grabbed from SciPy (though it can be stripped down for numpy_financial since less languages need to be supported).

Start in numpy_financial/_financial.py around nper and compare its boolean-indexing implementation with the scalar-kernel and loop-generation approach referenced from SciPy. Review the other financial functions and the edge cases mentioned in the issue before assessing scope. Done requires an agreed approach, affected functions, and edge-case coverage.