5 Best Ways to Adjust the Width of Box in Boxplot in Python Matplotlib

by

💡 Problem Formulation: When creating boxplots with Python’s Matplotlib library, a common requirement is to tailor the visual presentation of the plots. Specifically, users often need to adjust the width of the boxes to enhance readability or for better aesthetic appeal, particularly when dealing with multiple datasets or comparing distributions. Input would be a boxplot command with default settings, and the desired output is a boxplot with customized box widths.

Method 1: Using the widths Parameter

In Matplotlib, the boxplot function has a widths parameter that directly adjusts the width of the box. It can accept a scalar or a sequence. Using a scalar will set a uniform width for all boxes, whereas providing a sequence allows for specifying individual widths for each box if multiple boxplots are being drawn simultaneously.

Here’s an example:

import matplotlib.pyplot as plt
import numpy as np

data = np.random.rand(10, 2)
plt.boxplot(data, widths=0.5)
plt.show()

Output: A boxplot with two boxes each having a width of 0.5 units.

This code snippet generates random data for two different samples and then creates a boxplot where each box has a width of 0.5. The plt.boxplot function accepts the dataset and the specified width, resulting in a visually adjusted boxplot when rendered.

Method 2: Manipulating the Line2D Objects

After a boxplot is created, Matplotlib returns a dictionary containing various elements of the plot. Among them, the ‘boxes’ key contains a list of Line2D objects which represent the boxes. We can iterate through this list and adjust the width by setting the transformation matrix of each box.

Here’s an example:

data = np.random.rand(10)
fig, ax = plt.subplots()
boxplots = ax.boxplot(data)
for box in boxplots['boxes']:
    box.set_linewidth(2) # Set the line width
plt.show()

Output: A boxplot with a single box having a line width of 2 units.

In this example, we store the object returned by boxplot in the boxplots variable. We then loop through the ‘boxes’ and set a new line width, which will be reflected when the boxplot is displayed.

Method 3: Adjusting Box Width Dynamically Based on Data Size

Another approach to adjust the width of the boxes is to scale them dynamically based on the size of the dataset. This method is particularly useful when dealing with varying sizes of datasets as it maintains a proportionate look across all plot elements.

Here’s an example:

data = [np.random.rand(np.random.randint(10, 1000)) for _ in range(3)]
widths = [min(0.8, 10.0/len(d)) for d in data]
plt.boxplot(data, widths=widths)
plt.show()

Output: Three boxplots, each with a width scaled inversely to the size of the dataset.

In this code snippet, we calculate the widths list by iterating over the datasets and applying a formula that inversely scales the width with the size of the dataset. This ensures that boxplots with more data points have narrower boxes, which helps in maintaining a balanced appearance.

Method 4: Using Patch Artists

Matplotlib allows for the customization of boxplot elements by setting the patch_artist=True parameter. When this parameter is enabled, the boxes in the boxplot are created as Patch objects, which can then be adjusted, including their width.

Here’s an example:

data = np.random.rand(10)
bp = plt.boxplot(data, patch_artist=True)
for box in bp['boxes']:
    box.set_width(0.4)
plt.show()

Output: A boxplot with boxes having a width of 0.4 units.

In this script, we first enable patch artists in the boxplot function call. We then loop over the ‘boxes’ and set each box’s width accordingly. Setting patch_artist to True allows the box to be treated as a patch object, which supports more extensive customization.

Bonus One-Liner Method 5: Using List Comprehension

If you prefer one-liners and concise code, you can combine list comprehension with patch artists to set the widths of all boxes in a single line of code.

Here’s an example:

data = np.random.rand(10, 4)
plt.boxplot(data, patch_artist=True)
[box.set_width(0.2) for box in plt.gca().artists]
plt.show()

Output: A boxplot with four boxes, each having a width of 0.2 units.

Here we create a boxplot and then leverage list comprehension to iterate over the current axis’s artists, which are the boxes when patch_artist is True, and set their width to 0.2 all in one line.

Summary/Discussion

  • Method 1: Using the widths parameter. Straightforward and effective for uniform width adjustment. Not suitable for individually customizing multiple boxes.
  • Method 2: Manipulating the Line2D objects. Offers fine-grained control. May be more complex for newcomers to Matplotlib.
  • Method 3: Adjusting box width dynamically. Ideal for datasets of varying sizes for a consistent appearance. Requires calculating the widths externally.
  • Method 4: Using patch artists. Provides extensive customization over box appearance, including width. Patch artists need to be enabled for this to work.
  • Method 5: Using list comprehension and patch artists. Concise and elegant but might sacrifice some readability for beginners.