BUG: legend behaves inconsistently when plotting to the same axes 
There seems to be some inconsistencies related to `DataFrame.plot` and `Series.plot` legend behaviors.

_Problems:_
- When `DataFrame.plot` or `Series.plot` plots data on the same axes repeatedly:
  - If the target axes already has a legend, line plot always appends its legend to existing one ignoring `legend` kw and existing legend will be drawn as line artist regardless of actual artist type. Also, legend cannot be `reverse`ed if the axes already has a legend.
  - Bar/BarH plot deletes the existing legend and overwrites with latest one.
  - KDE plot appends its legend to the existing one, and will apply `reverse` to all artists including the existing one.
- When `subplots` is enabled, line plot draws legends on each axes but bar plot doesn't.
- Scatter plot does not draw legend even if `label` keyword is passed.

_Fix:_
I've prepared a fix based on following concept, except `hexbin` which doesn't use legend.
- Legend should be drawn by the order of the artists drawn.
- Each legend should be drawn according to the passed `legend` value.
  - If df1 plots with `legend=True` and df2 with `legend=False`, only df1's legend should appear.
  - If df2 plots with `legend='reverse'`d, only df2's legend should be reversed.
- When `subplots=True` and `legend=True`, each subplot axes should have its own legend (standardize current line plot behavior). 

_Example Code_

```
df1 = DataFrame(randn(6, 3), index=range(6), columns=['a', 'b', 'c'])
df2 = DataFrame(randn(6, 3), index=range(6), columns=['d', 'e', 'f'])
df3 = DataFrame(randn(6, 3), index=range(6), columns=['x', 'y', 'z'])

fig, axes = plt.subplots(1, 5, figsize=(14, 3))
for i, (legend, df) in enumerate(zip([True, False, 'reverse'], [df1, df2, df3])):
    df.plot(ax=axes[0],  legend=legend, title='line')
    df.plot(kind='bar', ax=axes[1], legend=legend, title='bar')
    df.plot(kind='barh', ax=axes[2],  legend=legend, title='barh')
    df.plot(kind='kde', ax=axes[3],  legend=legend, title='kde')
    df.plot(kind='scatter', ax=axes[4], x=df.columns[0], y=df.columns[1], label=i, 
        legend=legend, title='scatter')
plt.show()
```

_Output using current repository_
- For line, bar, kde plot, expected legend is `a, b, c, z, y x`. Because df2 was plot by `legend=False`, and df3 was plot by `legend='reverse'`.
- For scatter plot, `0, 2` is expected because df2 was plot by `legend=False`.

![figure_legend_current](https://f.cloud.github.com/assets/1696302/2480801/ba3c694a-b0c9-11e3-8f5b-d2ebec0798e3.png)

_Output after fix_
![figure_fixed](https://f.cloud.github.com/assets/1696302/2480773/b1bd8f70-b0c8-11e3-9654-3e3c2b20157b.png)

If there is anything should be considered, please let me know. Thanks.

@TomAugspurger can you take a look at this?
 @sinhrks - you're going to need to rebase this before we can merge it.
 ok, rebased.
 This will technically cause the test suite fail if someone runs the slow tests on py2.6 since the `assertIsNone` and `assertIsNotNone` methods were added to [`unittest`](https://docs.python.org/2/library/unittest.html#assert-methods) in python 2.7. Our build matrix doesn't catch that since 2.6 is run with `not slow`. Is this a problem @jreback? The tests could be changed to use `self.assertTrue(x is not None)` or `self.assertTrue(x is None)`
 Other than that, this should be fine.

@sinhrks I'll get to your area plot PR soon.
 hmmm....could enable slow ops on 2.6 (as it skips other things)....let me see
 OK. @sinhrks do you mind changing the asserts in `_check_legend_labels` to use `assertIsTrue` or `assertIsFalse`?

@sinhrks I didn't realize it was already broken before your PR . Could you change the one at the bottom of `test_subplots` in `test_graphics.py` also? It would be nice to make sure that the full test suite passes on all the supported versions of python if possible.
 hmm..current plotting DOES seem ok on 2.6

see here: https://travis-ci.org/jreback/pandas/jobs/22479235 (this with matplotlib 1.3.1).

am thinking about putting this in master - ownly drawback as this now takes a fair amount of time more than the other runs (master is about 50% faster than these anyhow because of caching).

worth it?
 I'm not sure if it's worth it for this specific issue. The actual code is valid python 2.6+, it's just the tests that rely on 2.7+ methods. But if you have other reasons for running the slow ones on 2.6 then go for it.
 @TomAugspurger no...going to pass on the 2.6 slow for now...doesn't seem worth it....

go ahead on this PR when you are ready
 Sure, I've modified condition formula and remove `assertIsNone`. 

Also, followings are changed:
- `test_subplots` to cover bar plots tests
- adding legend handle and label operation is refactored to `_add_legend_handle`
 @sinhrks reviewing this today. Sorry it took so long. Would you mind rebasing on top of master? (last time, I promise) I'll get this one merged, then you can rebase your Area one on top of this and we can merge that too.

Could you also clean  up the docs now that the legend API is so much better?
In `doc/source/visualization.rst`:

`plt.figure(); ts.plot(style='k--', label='Series'); plt.legend()`

You can pass `legend=True` in the `plot` call and remove the `plt.legend()`

I think you can remove the `plt.legend()` in the next one too:
`plt.figure(); df.plot(); plt.legend(loc='best')`

since `loc=best` is the default right?

The rest should be good. You could add a reversed example too if you want,
the docstring is pretty clear about that though, so no need.
 @TomAugspurger Sure. Modified docs and rebased. I agree that reversed example in the doc is not necessary.
 Thanks! Ahh I may have been a bit quick on merging. I'll keep my eye on what Travis says https://travis-ci.org/pydata/pandas/builds/23507773
 Travis is all green: https://travis-ci.org/pydata/pandas/builds/23507773
