Python Pandas - Draw a violin plot and set quartiles as horizontal lines with Seaborn

A violin plot combines a box plot and kernel density estimation to show the distribution of data. In Seaborn, you can draw violin plots with quartiles displayed as horizontal lines using the inner="quartile" parameter.

What is a Violin Plot?

A violin plot displays the probability density of data at different values, similar to a box plot but with a rotated kernel density plot on each side. The quartiles help identify the median and interquartile range within the distribution.

Basic Violin Plot with Sample Data

Let's create a violin plot using sample data to demonstrate the quartile lines ?

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

# Create sample data
np.random.seed(42)
data = {
    'Role': ['Batsman'] * 50 + ['Bowler'] * 50,
    'Age': np.concatenate([
        np.random.normal(28, 4, 50),  # Batsman ages
        np.random.normal(26, 3, 50)   # Bowler ages
    ])
}

df = pd.DataFrame(data)
print(df.head())

      Role        Age
0  Batsman  30.967309
1  Batsman  26.793585
2  Batsman  33.297968
3  Batsman  29.613614
4  Batsman  29.949619

Creating Violin Plot with Quartiles

Use the inner="quartile" parameter to display quartile lines horizontally across the violin ?

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

# Create sample data
np.random.seed(42)
data = {
    'Role': ['Batsman'] * 50 + ['Bowler'] * 50,
    'Age': np.concatenate([
        np.random.normal(28, 4, 50),  # Batsman ages
        np.random.normal(26, 3, 50)   # Bowler ages
    ])
}

df = pd.DataFrame(data)

# Create violin plot with quartiles
plt.figure(figsize=(8, 6))
sns.violinplot(x='Role', y='Age', data=df, inner="quartile", order=["Batsman", "Bowler"])
plt.title('Age Distribution by Role with Quartiles')
plt.show()

[A violin plot showing age distribution for Batsman and Bowler roles with horizontal quartile lines]

Parameters Explanation

Different Inner Representations

Compare different inner parameter options to see various ways of displaying data within violins ?

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

# Create sample data
np.random.seed(42)
data = {
    'Role': ['Batsman'] * 30 + ['Bowler'] * 30,
    'Age': np.concatenate([
        np.random.normal(28, 4, 30),
        np.random.normal(26, 3, 30)
    ])
}

df = pd.DataFrame(data)

# Create subplots for different inner styles
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
fig.suptitle('Violin Plots with Different Inner Representations')

inner_styles = ["quartile", "box", "point", "stick"]
for i, style in enumerate(inner_styles):
    ax = axes[i//2, i%2]
    sns.violinplot(x='Role', y='Age', data=df, inner=style, ax=ax)
    ax.set_title(f'inner="{style}"')

plt.tight_layout()
plt.show()

[Four violin plots showing different inner representations: quartile lines, box plots, individual points, and stick representations]

Conclusion

Violin plots with inner="quartile" effectively show both data distribution and quartile statistics. Use the order parameter to control category sequence and combine with other Seaborn styling options for better visualization.