jointplot plot_marginals boxplot

Is it possible to generate a jointplot with boxplots as marginals instead of a distplot?

From the arguments it obviously does not seem possible and the error is throwing when I force it suggests that as well:

g.plot_marginals(sns.boxplot) > TypeError: boxplot() got an unexpected keyword argument 'vertical'

Is this a feasible feature request? Or is there any alternative within seaborn to achive something similar?

[EmanuelGoncalves]

I solved my problem by hacking the marginal_boxplot function and defining there the data.frame and the variables to plot.

It’s definitely not portable to other situations but in case it is useful for anyone else I leave it here the code and the outcome plot.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

plot_df = pd.read_csv('jointplot_boxplot_marginal.csv')

pal = {1: '#eb2525', 0: '#888888'}


def marginal_boxplot(a, vertical=False, **kws):
    g = sns.boxplot(x='var_z', y='var_y', orient='v', **kws) if vertical else sns.boxplot(x='var_x', y='var_z', orient='h', **kws)
    g.set_ylabel('')
    g.set_xlabel('')

sns.set(rc={'axes.linewidth': .3, 'xtick.major.width': .3, 'ytick.major.width': .3, 'xtick.major.size': 2.5, 'ytick.major.size': 2.5}, style='ticks', context='paper', font_scale=.75)

g = sns.jointplot(
    'var_x', 'var_y', plot_df, 'reg', color=pal[0], space=0, truncate=True, ratio=8,
    marginal_kws={
        'hist': False, 'rug': False, 'kde': False,
    },
    annot_kws={
        'template': 'Pearson={val:.2g}, p={p:.1e}', 'loc': 4
    },
    joint_kws={
        'scatter_kws': {'s': 20, 'edgecolor': 'w', 'linewidth': .5, 'alpha': .8},
        'line_kws': {'linewidth': .5}
    }
)

g.plot_marginals(marginal_boxplot, palette=pal, data=plot_df, linewidth=.3, fliersize=1, notch=True)

g.x = plot_df.loc[plot_df['var_z'] == 1, 'var_x']
g.y = plot_df.loc[plot_df['var_z'] == 1, 'var_y']
g.plot_joint(sns.regplot, color=pal[1], truncate=True, fit_reg=False, scatter_kws={'s': 20, 'edgecolor': 'w', 'linewidth': .5, 'alpha': .8})

g.ax_joint.axhline(0, ls='-', lw=0.3, c='black', alpha=.2)
g.ax_joint.axvline(0, ls='-', lw=0.3, c='black', alpha=.2)

plt.gcf().set_size_inches(2, 2)
plt.savefig('jointplot_boxplot_marginal.png', bbox_inches='tight', dpi=600)
plt.close('all')

[mwaskom]

But I have the impression you were suggesting something different.

Yes, I’m saying that once the JointGrid is created, you don’t have to use its methods to plot things on it. It has an attribute called ax_joint that is simply the underlying matplotlib axes and can be passed to any axes-level seaborn function (or any of its plotting methods can be called if you want to use matplotlib directly).