Add colormap= argument to DataFrame plotting methods
I frequently plot DataFrames with a large number of columns and generally have difficulty distinguishing series due to the short cycle length of the default color scheme.

Especially in cases where the ordering of columns has significant information, the ideal way to color the series would be with a matplotlib colormap that uniformly spaces colors. This is pretty straightforward with pyplot, but pretty annoying to have to repeatedly do.

This patch modifies DataFrame plotting functions to take a `colormap=` argument consisting of either a `str` name of a [matplotlib colormap](http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps) or a colormap object itself.

```
df.cumsum().plot(colormap='jet', figsize=(10,5))
```

![jet_10](https://f.cloud.github.com/assets/440095/641179/ed27a674-d327-11e2-86a3-85c64f80e294.png)

KDE plot:

```
df.plot(kind='kde', colormap='jet', figsize=(10,5))
```

![kde](https://f.cloud.github.com/assets/440095/641208/1f9ec6d6-d329-11e2-9c47-5cbf88c5fe30.png)

Some colormaps don't work as well on a white background (the 0 column is white):
    df.cumsum().plot(colormap=cm.Greens, figsize=(10,5))
![greens_10](https://f.cloud.github.com/assets/440095/641183/18e0b134-d328-11e2-8436-94b26240a074.png)

But work better for other graph types:
    df.plot(kind='bar', colormap='jet', figsize=(10,5))
![greens_bar](https://f.cloud.github.com/assets/440095/641185/56d57b28-d328-11e2-9412-ab6b40559ebc.png)

Parallel coordinates on the iris dataset:

```
parallel_coordinates(iris, 'Name', colormap='gist_rainbow')
```

![iris_parallel](https://f.cloud.github.com/assets/440095/641195/a207e32e-d328-11e2-910f-36576d187ff0.png)

Andrews curves (I'd appreciate someone double checking this one; don't think I have it quite right):

```
andrews_curves(iris, 'Name', colormap='winter')
```

![andrews_winter](https://f.cloud.github.com/assets/440095/641201/e1acf85c-d328-11e2-8a8c-263d9abc12f9.png)

I've included some test coverage and unified all the color creation code into one method `_get_standard_colors()`. I started adding to the documentation but ran into a weird issue with the sphinx plot output. When adding this to `visualization.rst`:

```
.. ipython:: python

   from matplotlib import cm

   df = DataFrame(randn(1000, 4), index=ts.index, columns=list('ABCD'))
   df = df.cumsum()

   plt.figure()

   @savefig greens.png width=6in
   df.plot(colormap=cm.Greens)
```

I get this output (the lines should be white->green):
![greens](https://f.cloud.github.com/assets/440095/641261/d7243762-d32b-11e2-9bb0-e135ae25546b.png)

My first thought was that it was the `options.display.mpl_style = 'default'`, but plots render fine in IPython with this setting.  My guess is something in `@savefig`, but is anyone familiar with what might be happening here?

I like this:+1: on the idea of extending plotting. That said, I'm hoping long-term we follow the matplotlib convention and pass all extra kwargs to the OOP interface of matplotlib (in other words, if you get `cmap=xyz`, then you run `set_cmap(xyz)` on the axes).  We'd need to keep a list of aliases around too (e.g., `colormap: cmap`).

Btw - What should happen when you pass an invalid colormap?
 That said, I think this is a great addition!
 @jtratner My impression is that `set_cmap()` only applies to luminosities, not plots; see https://github.com/matplotlib/matplotlib/issues/484#issuecomment-2318515 . I'd love to be wrong.

As for what happens when you pass a string that does not correspond to a matplotlib colormap, you just get matplotlib's error:

```
ValueError: Colormap foo is not recognized
```
 @qwhelan Thanks for clarifying on this :), didn't realize that this wasn't a straight to mpl case. Can you add a test case for the failure condition?
 @jtratner Done.

I've worked out my Sphinx issue, but it'll be a few days before I can get back to it. I can do that in a separate pull if that's more convenient.
 @qwhelan thanks for adding that test in. in regards to your question - probably easier to just include the changes with this pull request. 
 @jreback @cpcloud thoughts on this? Don't want this to get lost in the shuffle...looks like it could be useful (though I don't know if there are more changes to plotting that would make it so we'd want to wait on this). Maybe could be experimental or something?
 I think it's natural to pass any option for is not specifically picked by pandas directly to matplotlob
doesn't that solve the problem and make this very simple?

of course pandas can be smarter and intercept certain arguments 

but that is a different issue
 @jreback, no, as @qwehelan points out, you have to manipulate the axis directly to do it. I do think it makes the graphs far more visually appealing.
 we can push to 0.12 though I don't see any harm in adding now
 your call - might be better to push to 0.12, in case there are other plotting changes...
 @jtratner I've added some documentation to the visualization.rst page. I don't have any further changes planned.
 Thanks... I meant more along the lines of broader changes to plotting.
Anyways, @jreback knows _way_ better than me.
On Jun 23, 2013 11:15 PM, "Christopher Whelan" notifications@github.com
wrote:

> @jtratner https://github.com/jtratner I've added some documentation to
> the visualization.rst page. I don't have any further changes planned.
> 
> —
> Reply to this email directly or view it on GitHubhttps://github.com/pydata/pandas/pull/3860#issuecomment-19887246
> .
 @jtratner Sorry for the confusion, I wasn't responding to your previous post. I was following up on the comment I made several days ago regarding additional changes to this pull request.
 @qwhelan 

I am fine with this...there might be other changes to plotting in 0.12, but this is 'independent' AFAIK...

can you rebase on current master?
 is this compatible with [mpltools](https://github.com/tonysyu/mpltools)?
 why not call it `cmap`?
 @cpcloud Should be compatible, colors are being generated the exact same way. The only difference is that mpltools calls `set_color_cycle()` on the axis, which will give different results if you add more series to the plot (mpltools will automatically cycle, this won't; not sure which is better).

As for `colormap` vs `cmap`, I have no preference. I usually just tab-complete in IPython anyway.
 @qwhelan can u rebase?
 @jreback @cpcloud Rebased.
 @qwhelan can you hook up to travis? (see contributing in main pandas dir)

pls also add a release notes entry and in v0.11.1 (will be changed to 0.12 - as thats going to be the release version, but not merged yet)

and then squash down commits to a few

thanks
 @qwhelan trying to close out issues for 0.12...status of the above?
 alternatively we could push to 0.13...
 this just needs release notes and entry in 0.12 and test on travis...
 i'm running the branch on travis
 ok...perfect
 Thanks. I can take care of the squash/release notes by 8pm PST.
On Jun 26, 2013 12:32 PM, "Phillip Cloud" notifications@github.com wrote:

> i'm running the branch on travis
> 
> —
> Reply to this email directly or view it on GitHubhttps://github.com/pydata/pandas/pull/3860#issuecomment-20073799
> .
 @qwhelan great...

you will need to rebase to master first (and see if you can setup travis for the future in any event)
 @jreback Added release notes, squashed, and travis is passing.

Should be good to go.
 looks great
thanks!
