MAINT (API?): organize npyio.recfromcsv defaults

Organizes the default kwargs in recfromcsv. Changes two undocumented
kwargs behaviors:

*  previously, if a user set `names=None`, it was ignored and replaced
   with `names=True`
*  the `dtype` kwarg was ignored. If `update` was given, it was used as
   `dtype`, and if not, None was used. We can retain the `update` behavior
 by using `kwargs.setdefault("dtype",kwargs.get('update', None))`.
  This Closes #311 .
TST: Added regression test for recfromcsv dtype error, as suggested by @hpaulj
DOC: npyio.recfromcsv keyword argument changes

Added a note to recfromcsv about the `dtype` keyword,
as suggested by @hpaulj. Also added a note to the release notes,
about the change in the `update` keyword, as suggested by @charris.
STY: change and delete some comments in recfromcsv

Removed two irrelevant comments about code history.

P.S. my first try with Github's online editor.
BUG: Fix for issue #3213

Two changes to numpy.ma.core._check_fill_value:

1. When the fill value is a string and the array type is not one of
   'OSUV', raise TypeError instead of using the default fill value.

2. When the fill value overflows the type, raise TypeError instead
   of OverflowError.

Closes #3213.
DOC: Document changes to ma.core._check_fill_value in release notes.
Merge pull request #4565 from charris/fill_value_fix

Raise TypeError when setting inappropriate ma fill_value
DOC: sync release notes of 1.8 branch and add some missing 1.9 sections

[ci-skip]
Merge pull request #3830 from jarondl/recfromcsv

MAINT (API?): organize npyio.recfromcsv defaults
Merge pull request #4569 from juliantaylor/rn-sync

DOC: sync release notes of 1.8 branch and add some missing 1.9 sections
ENH: optimize array creation from homogenous python sequences

Instead calling PyArray_DTypeFromObjectHelper for each item in a
sequence, check if all items in the sequence are of the same scalar
type, if this is the case we only need to recurse on one item.
Also use the PySequence_Fast API to avoid expensive function calls for
the common case of lists or tuples.

np.array(range(10)) improves about 25%
np.array(range(100)) improves about 40%
ENH: use fast sequence API for setArrayFromSequence

reduces runtime of function by about 10%
ENH: use fast sequence API for discover_dimensions

reduces runtime of function by about 20%