Add cdfplot
WIP for now. Closes #2669

I'm using statsmodels' KDE implementation right now; it has a `cdf` method, but scipy's gaussian_kde doesn't. I need to check the math, but I think something like doing a cumsum on the density and normalizing by the sum should be the same.

I'm also going to add kwargs for things like the inverse cdf. 

![cdf](https://f.cloud.github.com/assets/1312546/1749251/0287778c-6505-11e3-9dae-cc9e9c37a1de.png)

@TomAugspurger not saying the dict thing is required to get this merged, but if you wanted to add a second commit that changed this all to two dicts (one for Series, one for DataFrame), rather than if/elif statements, that'd be a complementary addition to this PR.
 FWIW, in statsmodels (and places in scipy.stats), we compute the cdf from the pdf using `scipy.integrate.quad`.

https://github.com/statsmodels/statsmodels/blob/master/statsmodels/nonparametric/kde.py#L161
 Okay this one can sit until 0.14 has started and I can add a release notes entry. I got on a bit of DRYness run, so I ended up combining the `KdePlot` and `CDFPlot` into a `DistributionPlot` class.

I've only used statsmodels for the actual fitting of the cdf. Scipy doesn't provide a cdf on their kde object, so we'd have to duplicate the code that @jseabold linked to above. May as well just use what they've done.

And of course if this has fallen out of scope for pandas then close without merging!
 We can't have a dependency on statsmodels, except for testing.
I remember `kde` plots were a bit suspect in terms of scope too,
expanding further is therefore suspect too.

Let's call this out of scope.
