Remove gaps in seaborn violin plots when hue categories are missing: overlay subsets and sync colors

Learn why seaborn reserves hue slots and creates whitespace in violin plots, and apply a simple overlay-and-legend method to remove gaps and keep colors aligned

When you split a violin plot by two categories, you expect each x-axis group to display only the violins that exist in your data. In practice, seaborn reserves space for every potential hue inside each x-position. If some category combinations aren’t present, the plot shows empty gaps. In R this can be visually tightened with position_dodge; in seaborn there isn’t a built-in shortcut that removes those voids. The effect is especially visible when some hues are missing for particular x-groups.

Minimal example that reproduces the whitespace

The snippet below generates synthetic data with incomplete combinations of Depth and Hydraulic_Conductivity and renders a standard seaborn violinplot. You’ll see unused space where a hue doesn’t exist for a given Depth bin.

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# categories
depth_bins = ["<0.64", "0.64-0.82", "0.82-0.90", ">0.9"]
k_bins = ["<0.2", "0.2-2.2", "2.2-15.5", ">15.5"]
# reproducible values
np.random.seed(42)
vals_hsi = np.random.uniform(low=0, high=35, size=30)
# random assignments with some combinations missing
vals_depth = np.random.choice(depth_bins, size=30)
vals_k = np.random.choice(k_bins, size=30)
for i in range(5):
    vals_depth[i] = depth_bins[i % len(depth_bins)]
    vals_k[i] = k_bins[(i + 1) % len(k_bins)]
# assemble dataframe
df_mock = pd.DataFrame({
    'HSI': vals_hsi,
    'Depth': vals_depth,
    'Hydraulic_Conductivity': vals_k
})
# basic seaborn violin with hue, leaving whitespace for missing hues
palette_set = sns.color_palette('Set1')
plt.figure(figsize=(12, 6))
sns.violinplot(
    x='Depth', y='HSI', hue='Hydraulic_Conductivity', data=df_mock,
    palette=palette_set,
    density_norm='count',
    cut=0,
    gap=0.1,
    linewidth=0.5,
    common_norm=False,
    dodge=True
)
plt.xlabel("DDDD")
plt.ylabel("XXX")
plt.title("Violin plot of XXX by YYYY and DDDD")
plt.ylim(-5, 35)
plt.legend(title='DDDD', loc='upper right')
plt.show()

Why those gaps appear

Seaborn positions hue-split violins by preallocating slots within each x-category. If a hue level is absent for that x-position, the slot remains empty. The result is visible whitespace, even though you would prefer the remaining violins to be centered and occupy the available width. There isn’t a built-in option that auto-compacts the violins when hue levels are missing.

Practical fix: overlay per-group violins and synchronize colors

One reliable way to eliminate the gaps is to draw multiple violinplots on the same axes, each time only for the subset of data that shares the same count of hues within an x-group. This overlays the violins without reserving space for non-existent hue levels. To keep the visual story intact, the hue colors must be synchronized across overlays, and the legend created manually.

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
# starting from the same df_mock as above
# palette and column names
palette_set = sns.color_palette('Set1')
col_hue = 'Hydraulic_Conductivity'
col_x = 'Depth'
col_y = 'HSI'
# order hues using the same parsing logic
order_hue = sorted(
    df_mock[col_hue].unique(),
    key=lambda s: (not s.startswith('<'), float(s.strip('<>').partition('-')[0]))
)
# map hue to consistent colors
map_colors = dict(zip(order_hue, palette_set))
# custom x-order to fix the category sequence
order_x = ['<0.64', '0.64-0.82', '0.82-0.90', '>0.9']
# group rows by the number of hue levels per x-bin
grp_size = df_mock.groupby(col_x)[col_hue].transform('nunique')
fig, ax = plt.subplots(figsize=(12, 6))
for _, subdf in df_mock.groupby(grp_size):
    present = set(subdf[col_hue])
    these_hues = [h for h in order_hue if h in present]
    sns.violinplot(
        x=col_x, y=col_y, hue=col_hue, data=subdf,
        order=order_x,
        hue_order=these_hues,
        palette=map_colors,
        density_norm='count',
        cut=0,
        gap=0.1,
        linewidth=0.5,
        common_norm=False,
        dodge=True,
        ax=ax,
        legend=False
    )
# manual legend using the same color map
ax.legend(
    handles=[mpatches.Patch(color=c, label=l) for l, c in map_colors.items()],
    title='DDDD', loc='upper right'
)
ax.set_xlabel('DDDD')
ax.set_ylabel('XXX')
ax.set_title('Violin plot of XXX by YYYY and DDDD')
ax.set_ylim(-5, 35)
plt.show()

This approach draws only the existing hue levels for each x-category, so there is no reserved space for missing combinations. In sparse categories with a single data point, you might see a thin vertical line instead of a filled shape; that is expected and becomes a non-issue as sample sizes grow.

Why it’s worth knowing

When you rely on hue to encode a second categorical dimension, the layout choices of the plotting library directly affect readability. Empty slots exaggerate distances and dilute comparisons within and across x-groups. Understanding that seaborn preallocates dodge positions empowers you to take control of the rendering and remove unintended whitespace without changing the underlying data.

Takeaways

If some hue levels are missing per x-category, seaborn’s default behavior creates gaps. Overlaying multiple violinplots per subset, while keeping a fixed hue order and a consistent color map, produces compact, interpretable figures. The manual legend ties the story together and preserves the meaning of colors across overlays.