Box Plot Keywords¶

The ax.boxplot() method accepts numerous keyword arguments to control appearance and behavior.

Labels¶

The labels keyword assigns names to each box on the x-axis.

1. Basic Labels¶

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(42)
data_a = np.random.normal(100, 10, 200)
data_b = np.random.normal(90, 20, 200)
data_c = np.random.normal(110, 15, 200)

fig, ax = plt.subplots()
ax.boxplot([data_a, data_b, data_c], labels=['Group A', 'Group B', 'Group C'])
plt.show()

2. LaTeX Labels¶

ax.boxplot([data_a, data_b, data_c], 
           labels=[r'$10^4$', r'$5 \cdot 10^4$', r'$10^5$'])

3. Numeric Labels¶

ax.boxplot([data_a, data_b, data_c], labels=['N=100', 'N=500', 'N=1000'])

Widths¶

The widths keyword controls the width of each box.

1. Uniform Width¶

ax.boxplot([data_a, data_b, data_c], widths=0.5)  # All boxes same width

2. Variable Widths¶

ax.boxplot([data_a, data_b, data_c], widths=[0.2, 0.4, 0.6])

3. Proportional to Sample Size¶

sizes = [100, 500, 1000]
widths = [np.sqrt(s) / np.sqrt(max(sizes)) * 0.8 for s in sizes]
ax.boxplot([data_a, data_b, data_c], widths=widths)

Orientation¶

The vert keyword controls vertical or horizontal orientation.

1. Vertical (Default)¶

ax.boxplot(data, vert=True)

2. Horizontal¶

fig, ax = plt.subplots()
ax.boxplot([data_a, data_b, data_c], vert=False)
ax.set_yticklabels(['A', 'B', 'C'])
ax.set_xlabel('Value')
plt.show()

3. Use Case¶

Horizontal orientation works well when labels are long or when comparing many groups.

Notch¶

The notch keyword adds confidence interval notches around the median.

1. Enable Notches¶

ax.boxplot(data, notch=True)

2. Interpretation¶

Non-overlapping notches between two boxes suggest the medians differ significantly at approximately the 95% confidence level.

3. Notch Calculation¶

# Notch extent approximation
notch_extent = 1.57 * IQR / np.sqrt(n)

Patch Artist¶

The patch_artist keyword enables filled boxes for color customization.

1. Enable Patch Artist¶

bp = ax.boxplot(data, patch_artist=True)

2. Access Box Patches¶

for patch in bp['boxes']:
    patch.set_facecolor('lightblue')

3. Required for Fill Colors¶

Without patch_artist=True, boxes are drawn as lines only and cannot be filled.

Show Statistics¶

Keywords control which statistical markers appear.

1. Show Mean¶

ax.boxplot(data, showmeans=True)

2. Show Fliers (Outliers)¶

ax.boxplot(data, showfliers=True)   # Default
ax.boxplot(data, showfliers=False)  # Hide outliers

3. Mean Line Instead of Point¶

ax.boxplot(data, showmeans=True, meanline=True)

Whisker Range¶

The whis keyword controls whisker extent.

1. Default (1.5 IQR)¶

ax.boxplot(data, whis=1.5)

2. Custom Multiplier¶

ax.boxplot(data, whis=2.0)  # Fewer outliers shown

3. Percentile Range¶

ax.boxplot(data, whis=[5, 95])  # Whiskers at 5th and 95th percentiles