ENH/VIS: Pass DataFrame column to size argument in DataFrame.scatter
Originally discussed in #8244. Currently this only uses the column from the dataframe if the `s` argument is a string, supporting int column names is ambiguous and liable to break existing code, but if people can think of a good way to do this I'll try to add it.

Example output:

``` python
import pandas as pd
import numpy as np
import random

df = pd.DataFrame({
    'x': np.linspace(0, 50, 6),
    'y': np.linspace(0, 20, 6),
    'cat_column': random.sample('abcdabcdabcd', 6)
})
df['cat_column'] = pd.Categorical(df['cat_column'])

# Numeric column: linear scaling of point sizes
df.plot(kind='scatter', x='x', y='y', s='x')
```

![image](https://cloud.githubusercontent.com/assets/1460294/5163679/70a09820-7422-11e4-8bf3-7a15fb072f71.png)

``` python
# Categorical columns: fixed set of sizes
df.plot(kind='scatter', x='x', y='y', s='cat_column')
```

![image](https://cloud.githubusercontent.com/assets/1460294/5163691/91671f16-7422-11e4-92fd-18b7f67b5b41.png)

The minimum and maximum point sizes we scale have a default range of  `(50, 1000)`, and
can be adjusted with the `size_range` arg:

``` python
# Smaller range of sizes than default
df.plot(kind='scatter', x='x', y='y', s='x',
        size_range=(200, 800))
```

![image](https://cloud.githubusercontent.com/assets/1460294/5163729/04e4f44a-7423-11e4-9e85-902d9dd1caaa.png)

Picking good defaults for the range of sizes is pretty hard as these sizes are absolute- matplotlib
sets the size in terms of points. The ability to tweak up and down with the `size_range` arg is
probably going to be needed, but open to suggestions on good defaults.
. 

is `s` really the argument name passed thru to matplotlib?
 Yeah, that's what the argument is [in pyplot](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.scatter). Other parts of the api might accept something different but pyplot doesn't even accept `size` as an alias.
 @onesandzeroes are you scaling the radius as proportional to value or the area as proportional to value?  I think the later is generally more sensible for numeric (non-negative) columns, but perhaps this should be another option...
 The `s` argument to `plt.scatter()` changes the area, so that's what it's scaling on here. There's a bit of discussion on SO [here](http://stackoverflow.com/questions/14827650/pyplot-scatter-plot-marker-size).
 @onesandzeroes Sounds good -- let's document that!

Also, I think the default scaling (at least for strictly positive variables) should probably be not be offset by the minimum value, but be absolute (relative to some scale). Right now it looks like that would be hard to achieve.
 > Also, I think the default scaling (at least for strictly positive variables) should probably be not be offset by the minimum value, but be absolute (relative to some scale). Right now it looks like that would be hard to achieve.

@shoyer not sure if I understand what you mean here. So, for strictly positive values, have the size of points range between 0 and the max size, with the minimum data value falling at some point within that range? Like if we have data values  of `[50, 75, 100]` and a max size of 200pt, the 50 val will be 100pt in size because it falls halfway between 0-100?

That probably works pretty well actually. I guess the reason I originally had a min size set was that I didn't want small values to completely disappear, but as long as the min size can still be bumped up to prevent that, should be OK. I'll try to test that out later today.

I think part of the reason I went with a minimum size, with the minimum observed value assigned that size, is because that's what ggplot seems to do, from what I can make of [their source](https://github.com/hadley/ggplot2/blob/master/R/scale-size.r).
 looks good. pls add a release note and squash.

@TomAugspurger ?
 Re referencing int column names, what to people think about making a `Column` class (or maybe reuse Index) to disambiguate.

`df.plot(..., s=pd.Column(0))`

then we can pick out the column labeled 0 (which may not be the position 0 column). Doesn't need to go in this PR of course.

I'll look at the code tonight hopefully.

I think what @shoyer is saying is that for

``` python
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4], 's1': [10, 20], 's2': [100, 200]})
```

then `df.plot(kind='scatter', x='A', y='B', s='s1')`, and `df.plot(kind='scatter', x='A', y='B', s='s2')`

will look identical. I think this is ok since users can adjust size_range if they want them to look different.
 @onesandzeroes Yes, if we have data values of [50, 75, 100] and a max size of 200pt, they should have corresponding sizes of [100, 150, 200].

We could do a similar thing for colormaps as well, using a diverging or ascending colormap based on similar heuristics.

It's worth looking at how Seaborn [handles this for heatmap](http://stanford.edu/~mwaskom/software/seaborn/tutorial/dataset_exploration.html). Actually, I would encourage you to consider getting some version of this patch into Seaborn as well, which could really use a scatterplot function: https://github.com/mwaskom/seaborn/issues/315
 @onesandzeroes pls add a release note, rebase and squash.

@shoyer @TomAugspurger @jorisvandenbossche ok on this?
 @onesandzeroes Can you add to the test the cases where:
- `s` is an int
- `s` is a column (but not a string, the column itself as `df['col']`)
- `s` is a numpy array (this last one broke for `c`, see PR https://github.com/pydata/pandas/pull/8929 that fixed it). I don't think in this case it will be a problem, as you check whether it is a string, but we should test it.

Further, I was wondering, it seems a bit strange / inconsistent that `size_range` is not applied when you provide a column / array but only for string. I know it is because you can't modify the values through the string directly, but still.
 @onesandzeroes ? trying to get 0.15.2 finished....can you see comments above
 I think we should be sure we're happy with the size scaling behavior before merging this, so it may not make it into 0.15.2.

From his earlier comments, it sounds like @onesandzeroes was going to test out my proposal for scaling based on absolute values rather than subtracting the minimum value.
 bumped
 closing if/when updated pls reopen
