API: remove datetime-like index ops from Series
as per discussed in #7146. may provide a `to_index()` and/or an accessor in future versions

@cpcloud @jorisvandenbossche ?
 Just added a note about the new whatsnew entry, but for the rest am OK on disabling this for now.
 @jorisvandenbossche fair comments

the issue is that it can become ambiguous, so don't want to add something w/o dealing with that.

Series.weekday wasn't documented at all so I don't think a big deal to remove.
 @jreback OK for the weekday

What do you mean with ambiguous? What can become ambiguous? `min/max/unique/nunique/value_counts` will not always act on the series values?
 not the usage of `Series.weekday` _could_ be ambiguous (and applies to all of the properties), IF the series contains a datetimeindex as well (or periodindex)

```
In [1]: s      = Series(pd.date_range('20130101',periods=3),index=pd.date_range('20130104',periods=3))

In [2]: s
Out[2]: 
2013-01-04   2013-01-01
2013-01-05   2013-01-02
2013-01-06   2013-01-03
Freq: D, dtype: datetime64[ns]

In [3]: s.index.weekday
Out[3]: array([4, 5, 6])

In [4]: pd.DatetimeIndex(s).weekday
Out[4]: array([1, 2, 3])
```
 ah yes OK, `weekday` is indeed ambiguous like the other datetime accessors. And so they are disabled again in this PR. That's all OK (but I thought you were speaking about the others)
 no, the others were added (not in this PR necessarily, though max/min were), but don't think had any notes on them
 What was actually added? (I just realize I don't fully understand this)

For example `max()` already is an index method in 0.13, so I don't fully get what `support 'max()/... on Index types'` in the release notes is supposed to mean?
 all of these were added as mixin methods that is they were moved from index/series directly to the ops mixin class (so max.min) were not added to index

but the other were added to index
 There still some occurences in the docs (that now error):

http://pandas-docs.github.io/pandas-docs-travis/timeseries.html#custom-business-days-experimental 
http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#experimental-features
