ENH: Groupby.plot enhancement

[sinhrks]

Allow to support DataFrameGroupBy.plot and SeriesGroupBy.plot covering current DataFrame.hist(by=XXX) and DataFrame.boxplot(by=XXX) usecases. Implementation is incomplete but covering most of usecases / behaviours.

• fix FIXMEs
• Split to plot_seriesgroupby and plot_dataramegroupby specifying default values.
• Refactoring plot_frame and plot_series, modify docstring. (DOC/CLN: Cleanups plotting.py #8037)
• Make line subplot to accept multiple colors (ENH/BUG: color cannot be applied to line subplots #9894)
• secondary_y
• error bars (raise NotImplementedError)
• split some cleanups required in prior to this PR (CLN: plotting cleanups for groupby plotting #10717)
• doc
• tests

Following gist contains all patterns of outputs.

• https://gist.github.com/sinhrks/ad805b6689140930d99a

Rendered doc

[jreback]

@sinhrks is this stale?

[sinhrks]

@jreback Let me revisit after #9814 which does some internal changes.

[shoyer]

@sinhrks We haven't quite finished the faceting API for xray, but basically the idea is to copy it from Seaborn. Plot methods accept arguments like row, col and col_span which control which how to create facets. We'll have a FacetGrid API for low level control, but the main user facing API will still be convenience methods like .plot.

We could copy this API for pandas. But again, Seaborn already does it -- and does it well. In theory, we might even wrap Seaborn's FacetGrid for this stuff, e.g., df.plot('x', 'y', row='foo', col='bar') -> sns.FacetGrid(df, row='foo', col='bar').map(plt.plot, 'x', 'y'). However there would pretty assuredly be some API breaks.

[sinhrks]

@shoyer I don't mean to implement full functionality like seaborn.

I think our goal is to migrate current DataFrame.hist, boxplot and scatter_plot 's grouping functionality to DataFrame.plot in a simple / easy way.

[jreback]

@sinhrks state of this?

[jreback]

change to 0.18

[jreback]

@sinhrks

so a couple of possibilities here:

1. copy seaborn API as much as possible from FacetGrid. This would be familiar and provides a nice soln.
2. simply defer to seaborn and make it an optional dep (to clarify I mean to have a reasonable pandas API that really just does the import of seaborn and pipes almost directly, with very little pandas code on the impl side).
3. like 2), but provide this via an engine='seaborn' keyword.

I actually like 3) as the start of pandas removing all plotting code and defering to matplotlib/seaborn/bokeh as the default (note that we might need a set of configuration options to ultimately control this), but for now we can simply have an engine='seaborn' kw here.

So since this is 'new' functionaility I dont' see a need to have a engine='pandas' kw to control the exiting groupby plotting behavior (unless I am missing something here).

cc @tacaswell
cc @mdboom
cc @mwaskom
cc @pzwang
cc @bryevdv

[mwaskom]

I would be in favor of option 1.

[jreback]

@sinhrks status of this?

[tacaswell]

I am also in favor of 1.

Is there anyway to tweak the API to return a dictionary mapping the groupby index -> the artist that is plotting it? That will make writing interactive functionality against these plots much easier.

[jreback]

@sinhrks status?

[jreback]

@sinhrks @TomAugspurger @jorisvandenbossche

status on this?

[jreback]

status?

[jreback]

@sinhrks status on this?

[jreback]

what's status here?

@sinhrks is this viable

[jreback]

status of this?

[jreback]

closing as stale, but @sinhrks pls reopen if you want / can fix.

[sinhrks]

Sorry for the stale. I'll re-organize the preferable spec and re-submit a PR.