Plotting of DatetimeIndex directly with matplotlib no longer gives datetime formatted axis (0.15)
From http://stackoverflow.com/questions/26526230/plotting-datetimeindex-on-x-axis-with-matplotlib-creates-wrong-ticks-in-pandas-0

```
df = pd.DataFrame({'RandomValues': np.random.randint(1, 50, 60)},
                  index=pd.date_range("2012-01-01", periods=60))
```

Plotting of course works:

```
In [79]: df.plot()
Out[79]: <matplotlib.axes._subplots.AxesSubplot at 0xf571550>
```

But plotting with matplotlib's `plot` functions gives no longer a datetime formatted x-axis, but just ints:

```
In [80]: plt.plot(df.index, df.RandomValues)
Out[80]: [<matplotlib.lines.Line2D at 0xfb5f748>]
```

**Reason**: matplotlibs `plot` calls `np.atleast_1d` on the input (https://github.com/matplotlib/matplotlib/blob/v1.4.0/lib/matplotlib/axes/_base.py#L220). Previously this returned an Index, now it returns a array of `datetime64` values, which matplotlib can't handle.

Issues:
- This is actually a problem of matplotlib (they should support datetime64), but of course: a lot of users rely now on the behaviour that it did work with a pandas `Index` also.
- Is there a way to restore this behaviour? I don't see directly a solution?
- We should update our docs, as this behaviour is also mentioned there briefly: http://pandas.pydata.org/pandas-docs/stable/visualization.html#plotting-directly-with-matplotlib + figure out why this didn't fail in the doc build (the figure seems not updated with the 0.15 doc build)

It seems there was some discussion about this in the Index-no-subclass PR: https://github.com/pydata/pandas/pull/7891#issuecomment-50936333 and https://github.com/pydata/pandas/pull/7891#discussion_r15862386 (I commented there about exactly this issue, but I don't know why we didn't look further in it then)
