How to widen boxes in Seaborn boxplot?

Question

I'm trying to make a grouped boxplot using Seaborn (Reference), and the boxes are all incredibly narrow -- too narrow to see the grouping colors.

g = seaborn.factorplot("project_code",y="num_mutations",hue="organ",
        data=grouped_donor, kind="box", aspect=3)

If I zoom in, or stretch the graphic several times the width of my screen, I can see the boxes, but obviously this isn't useful as a standard graphic.

This appears to be a function of my amount of data; if I plot only the first 500 points (of 6000), I get visible-but-small boxes. It might specifically be a function of the high variance of my data; according to the matplotlib boxplot documentation,

The default [width] is 0.5, or 0.15x(distance between extreme positions) if that is smaller.

Regardless of the reason, there's plenty of room on the graph itself for wider boxes, if I could just widen them.

Unfortunately, the boxplot keyword widths which controls the box width isn't a valid factorplot keyword, and I can't find a matplotlib function that'll change the width of a bar or box outside of the plotting function itself. I can't even find anyone discussing this; the closest I found was boxplot line width. Any suggestions?

Answer 1

When sns.boxplot is used adding dodge=False will solve this problem as of version 0.9.

sns.factorplot() has been deprecated since version 0.9, and has been replaced with catplot() which also has the dodge parameter.

Answer 2

For future reference, here are the relevant bits of code that make the correct figure with legend: (obviously this is missing important things and won't actually run as-is, but hopefully it shows the tricky parts)

import matplotlib.pylab as pyp
import seaborn as sns

def custom_legend(colors,labels, legend_location = 'upper left', legend_boundary = (1,1)):
    # Create custom legend for colors
    recs = []
    for i in range(0,len(colors)):
        recs.append(mpatches.Rectangle((0,0),1,1,fc=colors[i]))
    pyp.legend(recs,labels,loc=legend_location, bbox_to_anchor=legend_boundary)

# Color boxplots by organ
organ_list = sorted(df_unique(grouped_samples,'type'))
colors = sns.color_palette("Paired", len(organ_list))
color_dict = dict(zip(organ_list, colors))
organ_palette = grouped_samples.drop_duplicates('id')['type'].map(color_dict)

# Plot grouped boxplot
g = sns.factorplot("id","num_mutations",data=grouped_samples, order=id_list, kind="box", size=7, aspect=3, palette=organ_palette)
sns.despine(left=True)
plot_setup_pre()
pyp.yscale('log')
custom_legend(colors,organ_list)