Basic Box Plot¶

Box plots (box-and-whisker plots) visualize the distribution of data through quartiles, providing a compact summary of central tendency, spread, and outliers.

Single Data Set¶

The simplest box plot displays one distribution using ax.boxplot().

1. Import and Setup¶

import matplotlib.pyplot as plt
import numpy as np

2. Generate Data¶

np.random.seed(42)
data = np.random.normal(100, 15, 200)

3. Create Box Plot¶

fig, ax = plt.subplots()
ax.boxplot(data)
ax.set_ylabel('Value')
ax.set_title('Basic Box Plot')
plt.show()

Multiple Data Sets¶

Compare multiple distributions side by side by passing a list of arrays.

1. Prepare Multiple Arrays¶

np.random.seed(42)
data1 = np.random.normal(100, 10, 200)
data2 = np.random.normal(90, 20, 200)
data3 = np.random.normal(110, 15, 200)

2. Pass as List¶

fig, ax = plt.subplots()
ax.boxplot([data1, data2, data3])
ax.set_xticklabels(['Group A', 'Group B', 'Group C'])
ax.set_ylabel('Value')
ax.set_title('Comparing Distributions')
plt.show()

3. Interpret Results¶

Each box represents one distribution. Boxes at different heights indicate different medians. Wider boxes (taller IQR) indicate greater variability.

Method Signature¶

The ax.boxplot() method accepts various input formats.

1. Single Array¶

ax.boxplot(data)  # One box

2. List of Arrays¶

ax.boxplot([data1, data2, data3])  # Multiple boxes

3. 2D Array¶

data_2d = np.random.randn(100, 4)
ax.boxplot(data_2d)  # Each column becomes a box