BUG/VIS: groupby.hist/plot() should pass group keys as labels

[TomAugspurger]

At least wherever possible.

In [2]: df = pd.DataFrame(np.random.randn(30, 2), columns=['A', 'B'])

In [3]: df['C'] = 15 * ['a'] + 15 * ['b']

In [4]: ax = df.groupby('C')['A'].hist()

Ideally the group keys would be the legend for the plot.
I can take this one.

[TomAugspurger]

I'm still working on this. The interface between the groupby and plotting methods is a bit messy.

Interestingly enough, df.groupby('C')['A'].hist() and df['A'].hist(by=df['C']) follow two completely different code paths, and produce different results.

>>>df.groupby('C')['A'].hist()
C
a    Axes(0.552174,0.15;0.347826x0.75)
b    Axes(0.552174,0.15;0.347826x0.75)
dtype: object

and

>>>df['A'].hist(by=df['C'])
array([<matplotlib.axes.AxesSubplot object at 0x1111bba10>,
       <matplotlib.axes.AxesSubplot object at 0x1111ef710>], dtype=object)

[fonnesbeck]

Is there currently a hack to get a legend for plots like this (i.e. the first two plots where histograms are on the same axis)? At present I have no way of knowing which histogram belongs to which series.

[TomAugspurger]

Sorry, haven't gotten around to fixing this. My current workaround is to do the groupby and then iterate over the groups:

groups = df.groupby("age_bin")['Impressions']

fig, ax = plt.subplots()

for k, v in groups:
    v.hist(label=k, alpha=.75, ax=ax)

ax.legend()

That will give you something like

[jreback]

@TomAugspurger fixable for 0.14? push? wont-fix?

[TomAugspurger]

@jreback pushing

[jreback]

ok......(of course if you do fix by release time, then can pull forward)