ENH: other plotting tools via plot accessor

[rhiever]

• scatter_matrix (deprecate, redirect to seaborn)
• andrews_curve
• parallel_coordinates
• lag_plot (maybe rename to lag)
• autocorrelation_plot (maybe rename to autocorrelation?)
• bootstrap_plot (maybe rename to bootstrap?)
• radviz

I think it would be nice to allow scatter_matrix to be called directly on a DataFrame. Currently, scatter_matrix is a separate function that takes a DataFrame as a parameter, but it seems like it would be easy enough to rework to allow it to be called directly on a DataFrame as well. Effectively, the convenience function would look something like:

def scatter_matrix(self, ...):
    pandas.tools.plotting.scatter_matrix(self.data, ...)

Is this feature feasible?

[TomAugspurger]

We could probably do this as DataFrame.plot.scatter_matrix. I'm not sure why this wasn't ever folded into the df.plot(kind=) function.

Also, the standard disclaimer that seaborn does this better

[jreback]

actually prob all should be added via the plot accessor: http://pandas.pydata.org/pandas-docs/stable/visualization.html#plotting-tools

[shoyer]

Something to consider: Maybe we want to deprecate some of these more complex plot types? For example, seaborn does a better job of scatter matrix than we do...

[jreback]

absolutely let's do that. any others?

bootstrap, autocorrelation, lag for statsmodels?

cc @jseabold
cc @josef-pkt

[rhiever]

@shoyer: IMO the scatter matrix is a basic plot type that people would want to see for a DataFrame.

[jorisvandenbossche]

I agree with @shoyer. These plotting methods are rather 'neglected' lately. I think we should either choose to give them more attention, or deprecate them. And personally I think it should not be the focus of pandas.

But maybe scatter_matrix as the IMO the most generic of the plotting methods listed can be the exception?

[jreback]

why don't we defer scatter_mattix to seaborn anyhow? so seaborn becomes an optional dep)

[josef-pkt]

@jreback how well are optional circular dependencies no supported in various packagings?
I haven't kept up with it, but we get circular dependency pandas - seaborn - statsmodels.
I'm in favor of tight integration, in general.

I'm not a heavy pandas user directly, but I also find some quick plot methods convenient, instead of having to look for the appropriate function in another package.

about plots in statsmodels: plots are mostly in a minimal maintenance state. There is not much effort for a style update to make them look better, e.g. compared to seaborn, because of a lack of developers.
However, we need them in support of the models and other stats functions and still keep adding more.
For example, acf and pacf are some of our oldest plot functions and won't go away.

Trying to use seaborn as optional statsmodels dependency to upgrade plots is an idea but doesn't have a champion to look into it and work on it. For many plots the focus of seaborn is exploratory analysis which is more similar to the pandas use, but for us having a model inside the plot is inside out because we need plots inside the models, or after having estimated a model.

[jreback]

using seaborn (or sm using pandas is no problem here)
you can simply import inside the method itself