fix incorrect propagation of dtype in Series normalize and other methods

This PR addresses a bug in `Series.normalize()` and other methods, where the `dtype` attribute of the output was being set incorrectly to the `dtype` of the input RDD.

After this patch, the default behavior for `apply()` and most other methods that can potentially produce output with a dtype different from the input will be to leave this attribute unset, to be lazily determined  as needed by making an implicit call to `first()` when the `dtype` attribute is requested.

For `normalize()` in particular, the output dtype will now be the smallest floating-point type that can safely store the data without over/underflow, as determined by `commons.smallest_float_type()`. This will be properly set on the output Series, so that no implicit `first()` call is needed.

@freeman-lab, if you're okay with this PR, I can take care of merging it into master from the 0.4.x branch.

Gotcha, I thought the syntax was just to avoid numpy's floored division, not to explicitly end up with a float32.