Violin Plot¶

Violin plots combine box plot statistics with kernel density estimation, showing the full distribution shape of data.

Basic Violin Plot¶

Simple Example¶

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
data = [np.random.normal(0, std, 100) for std in range(1, 5)]

fig, ax = plt.subplots()
ax.violinplot(data)
ax.set_xlabel('Dataset')
ax.set_ylabel('Value')
ax.set_title('Basic Violin Plot')
plt.show()

With Custom Positions¶

fig, ax = plt.subplots()
positions = [1, 2, 4, 5]  # Custom x positions
ax.violinplot(data, positions=positions)
ax.set_xticks(positions)
ax.set_xticklabels(['A', 'B', 'C', 'D'])
plt.show()

Anatomy of a Violin Plot¶

    ___
   /   \     ← Kernel density estimate (distribution shape)
  |     |
  |  ─  |    ← Median (if showmedians=True)
  |  │  |    ← Interquartile range
  |  ─  |    ← Q1 and Q3 markers
   \___/
     │       ← Extrema lines (min/max)

Key Parameters¶

Parameter	Description	Default
`dataset`	Data to plot	Required
`positions`	X positions	[1, 2, ...]
`widths`	Width of violins	0.5
`showmeans`	Show mean line	False
`showmedians`	Show median line	False
`showextrema`	Show min/max lines	True
`quantiles`	Quantile lines	None
`vert`	Vertical orientation	True

Displaying Statistics¶

Show Median and Mean¶

fig, ax = plt.subplots()
ax.violinplot(data, showmeans=True, showmedians=True)
plt.show()

Show Quantiles¶

fig, ax = plt.subplots()
# Show 25th, 50th, and 75th percentiles
ax.violinplot(data, quantiles=[[0.25, 0.5, 0.75]] * len(data))
plt.show()

Hide Extrema¶

fig, ax = plt.subplots()
ax.violinplot(data, showextrema=False, showmedians=True)
plt.show()

Styling Violin Plots¶

Accessing Violin Components¶

fig, ax = plt.subplots()
parts = ax.violinplot(data, showmedians=True)

# 'parts' is a dictionary with keys:
# 'bodies': list of PolyCollection (violin shapes)
# 'cmeans': LineCollection (mean lines, if shown)
# 'cmedians': LineCollection (median lines, if shown)
# 'cbars': LineCollection (center bars)
# 'cmins': LineCollection (min lines)
# 'cmaxes': LineCollection (max lines)

Custom Colors¶

fig, ax = plt.subplots()
parts = ax.violinplot(data, showmedians=True)

# Color the violin bodies
colors = ['lightblue', 'lightgreen', 'lightyellow', 'lightcoral']
for i, body in enumerate(parts['bodies']):
    body.set_facecolor(colors[i])
    body.set_edgecolor('black')
    body.set_alpha(0.7)

# Color the median lines
parts['cmedians'].set_color('red')
parts['cmedians'].set_linewidth(2)

plt.show()

Consistent Styling Function¶

def style_violins(parts, body_color='lightblue', edge_color='black', 
                  median_color='red', alpha=0.7):
    """Apply consistent styling to violin plot parts."""
    for body in parts['bodies']:
        body.set_facecolor(body_color)
        body.set_edgecolor(edge_color)
        body.set_alpha(alpha)

    if 'cmedians' in parts:
        parts['cmedians'].set_color(median_color)
        parts['cmedians'].set_linewidth(2)

    for key in ['cbars', 'cmins', 'cmaxes']:
        if key in parts:
            parts[key].set_color(edge_color)

Horizontal Violin Plots¶

fig, ax = plt.subplots()
ax.violinplot(data, vert=False, showmedians=True)
ax.set_ylabel('Dataset')
ax.set_xlabel('Value')
plt.show()

Practical Examples¶

1. Comparing Distributions¶

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)

# Different distributions
normal = np.random.normal(0, 1, 200)
uniform = np.random.uniform(-2, 2, 200)
bimodal = np.concatenate([np.random.normal(-1, 0.5, 100),
                          np.random.normal(1, 0.5, 100)])
skewed = np.random.exponential(1, 200) - 1

data = [normal, uniform, bimodal, skewed]
labels = ['Normal', 'Uniform', 'Bimodal', 'Skewed']

fig, ax = plt.subplots(figsize=(10, 6))
parts = ax.violinplot(data, showmedians=True)

# Style
for body in parts['bodies']:
    body.set_facecolor('steelblue')
    body.set_alpha(0.6)

ax.set_xticks([1, 2, 3, 4])
ax.set_xticklabels(labels)
ax.set_ylabel('Value')
ax.set_title('Comparing Different Distributions')
plt.show()

2. Split Violin (Comparison)¶

import matplotlib.pyplot as plt
import numpy as np

def half_violin(ax, data1, data2, positions, colors=['lightblue', 'lightcoral']):
    """Create split violin plot for comparison."""

    # Left half
    parts1 = ax.violinplot(data1, positions=positions, showmedians=True)
    for body in parts1['bodies']:
        # Get the paths and modify to show only left half
        m = np.mean(body.get_paths()[0].vertices[:, 0])
        body.get_paths()[0].vertices[:, 0] = np.clip(
            body.get_paths()[0].vertices[:, 0], -np.inf, m)
        body.set_facecolor(colors[0])
        body.set_alpha(0.7)

    # Right half
    parts2 = ax.violinplot(data2, positions=positions, showmedians=True)
    for body in parts2['bodies']:
        m = np.mean(body.get_paths()[0].vertices[:, 0])
        body.get_paths()[0].vertices[:, 0] = np.clip(
            body.get_paths()[0].vertices[:, 0], m, np.inf)
        body.set_facecolor(colors[1])
        body.set_alpha(0.7)

    return parts1, parts2

# Example usage
np.random.seed(42)
data1 = [np.random.normal(0, std, 100) for std in [1, 1.5, 2]]
data2 = [np.random.normal(0.5, std, 100) for std in [1, 1.5, 2]]

fig, ax = plt.subplots()
half_violin(ax, data1, data2, [1, 2, 3])
ax.legend(['Group A', 'Group B'])
ax.set_xticks([1, 2, 3])
ax.set_xticklabels(['Low', 'Medium', 'High'])
plt.show()

3. Violin with Box Plot Overlay¶

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
data = [np.random.normal(0, std, 200) for std in range(1, 5)]

fig, ax = plt.subplots()

# Violin plot
parts = ax.violinplot(data, showextrema=False)
for body in parts['bodies']:
    body.set_facecolor('lightblue')
    body.set_alpha(0.5)

# Box plot overlay
bp = ax.boxplot(data, widths=0.15, patch_artist=True,
                boxprops=dict(facecolor='white', edgecolor='black'),
                medianprops=dict(color='red', linewidth=2),
                whiskerprops=dict(color='black'),
                capprops=dict(color='black'),
                flierprops=dict(marker='o', markersize=4))

ax.set_title('Violin Plot with Box Plot Overlay')
plt.show()

4. Grouped Violin Plots¶

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)

# Generate data for two groups across three categories
group1 = [np.random.normal(10, 2, 100),
          np.random.normal(15, 3, 100),
          np.random.normal(12, 2.5, 100)]

group2 = [np.random.normal(12, 2, 100),
          np.random.normal(14, 2.5, 100),
          np.random.normal(16, 3, 100)]

fig, ax = plt.subplots(figsize=(10, 6))

# Positions for each group
pos1 = [1, 3, 5]
pos2 = [1.6, 3.6, 5.6]

# Plot both groups
parts1 = ax.violinplot(group1, positions=pos1, widths=0.5, showmedians=True)
parts2 = ax.violinplot(group2, positions=pos2, widths=0.5, showmedians=True)

# Style group 1
for body in parts1['bodies']:
    body.set_facecolor('steelblue')
    body.set_alpha(0.7)

# Style group 2
for body in parts2['bodies']:
    body.set_facecolor('coral')
    body.set_alpha(0.7)

ax.set_xticks([1.3, 3.3, 5.3])
ax.set_xticklabels(['Category A', 'Category B', 'Category C'])

# Custom legend
from matplotlib.patches import Patch
legend_elements = [Patch(facecolor='steelblue', alpha=0.7, label='Group 1'),
                   Patch(facecolor='coral', alpha=0.7, label='Group 2')]
ax.legend(handles=legend_elements)

ax.set_ylabel('Value')
ax.set_title('Grouped Violin Plots')
plt.show()

5. Financial Data Distribution¶

import matplotlib.pyplot as plt
import numpy as np

# Simulated daily returns for different assets
np.random.seed(42)
stocks = np.random.normal(0.0005, 0.02, 252)  # ~12.5% annual return
bonds = np.random.normal(0.0002, 0.005, 252)  # ~5% annual return
commodities = np.random.normal(0, 0.03, 252)  # High volatility

data = [stocks * 100, bonds * 100, commodities * 100]  # Convert to percentage

fig, ax = plt.subplots(figsize=(8, 6))
parts = ax.violinplot(data, showmedians=True, showmeans=True)

colors = ['green', 'blue', 'orange']
for i, body in enumerate(parts['bodies']):
    body.set_facecolor(colors[i])
    body.set_alpha(0.6)

ax.axhline(y=0, color='black', linestyle='--', alpha=0.5)
ax.set_xticks([1, 2, 3])
ax.set_xticklabels(['Stocks', 'Bonds', 'Commodities'])
ax.set_ylabel('Daily Return (%)')
ax.set_title('Distribution of Daily Returns')
plt.show()

Violin Plot vs Box Plot¶

Feature	Box Plot	Violin Plot
Shows quartiles	✅	❌ (without overlay)
Shows distribution shape	❌	✅
Shows multimodality	❌	✅
Compact display	✅	❌
Shows outliers	✅	❌
Sample size indication	❌	Via width

Width and Scaling¶

Fixed Widths¶

ax.violinplot(data, widths=0.8)  # All same width

Variable Widths¶

widths = [0.5, 0.7, 0.9, 1.0]  # Different widths
ax.violinplot(data, widths=widths)

Scale by Sample Size¶

# Width proportional to sqrt of sample size
sample_sizes = [len(d) for d in data]
widths = [0.5 * np.sqrt(n) / np.sqrt(max(sample_sizes)) for n in sample_sizes]
ax.violinplot(data, widths=widths)

Common Pitfalls¶

1. Small Sample Sizes¶

# Violin plots need sufficient data for meaningful KDE
# Minimum recommended: 30-50 points per group
small_data = np.random.randn(10)  # Too few points
# Consider using box plot or jittered strip plot instead

2. Missing Labels¶

# violinplot doesn't automatically set x-tick labels
fig, ax = plt.subplots()
ax.violinplot(data)
# Must set labels manually
ax.set_xticks([1, 2, 3, 4])
ax.set_xticklabels(['A', 'B', 'C', 'D'])

3. Comparing with Different Scales¶

# Use same axis limits for fair comparison
ax.set_ylim(-5, 5)  # Consistent scale across subplots