Box plot vs violin plot comparison in Matplotlib - Hands-On Comparison

Box plot vs violin plot comparison

📖 Scenario: You work as a data analyst for a company that wants to understand the distribution of sales data across different regions. You will use two common plots: box plot and violin plot, to compare the sales data visually.

🎯 Goal: Create a Python program using matplotlib to plot both a box plot and a violin plot side by side for the sales data of three regions. This will help you see how these plots show data distribution differently.

📋 What You'll Learn

Create a dictionary called sales_data with three keys: 'North', 'South', and 'East' and their sales values as lists of numbers.

Create a list called regions containing the region names in order: 'North', 'South', 'East'.

Use matplotlib.pyplot to create a figure with two subplots side by side.

Plot a box plot of the sales data on the first subplot using ax1.boxplot().

Plot a violin plot of the sales data on the second subplot using ax2.violinplot().

Set the x-axis tick labels to the region names for both plots.

Add titles to each subplot: 'Box Plot' and 'Violin Plot'.

Display the plots using plt.show().

💡 Why This Matters

🌍 Real World

Data analysts often compare different visualizations to understand data distribution and variability across groups or regions.

💼 Career

Knowing how to create and interpret box plots and violin plots is essential for data scientists and analysts to communicate insights effectively.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'North': [250, 270, 300, 280, 260], 'South': [220, 210, 230, 240, 225], and 'East': [300, 320, 310, 305, 315].

Matplotlib

# Create the sales_data dictionary with exact values
# Your code here

Hint

Use curly braces {} to create the dictionary and colons : to assign lists to each region key.

2

Create the regions list

Create a list called regions containing the strings 'North', 'South', and 'East' in this exact order.

Matplotlib

sales_data = {
    'North': [250, 270, 300, 280, 260],
    'South': [220, 210, 230, 240, 225],
    'East': [300, 320, 310, 305, 315]
}
# Create the regions list with exact order
# Your code here

Hint

Use square brackets [] to create the list and separate the region names with commas.

3

Plot box plot and violin plot side by side

Import matplotlib.pyplot as plt. Create a figure and two subplots side by side using plt.subplots(1, 2, figsize=(10, 5)). On the first subplot ax1, plot a box plot of the sales data values using ax1.boxplot(). On the second subplot ax2, plot a violin plot of the sales data values using ax2.violinplot(). Set the x-axis tick labels to regions for both plots. Add titles 'Box Plot' and 'Violin Plot' to ax1 and ax2 respectively.

Matplotlib

sales_data = {
    'North': [250, 270, 300, 280, 260],
    'South': [220, 210, 230, 240, 225],
    'East': [300, 320, 310, 305, 315]
}

regions = ['North', 'South', 'East']

# Import matplotlib.pyplot as plt and create figure with 2 subplots
# Plot boxplot on ax1 and violinplot on ax2 with correct labels and titles
# Your code here

Hint

Use list comprehension to get the sales values in order for plotting. Use set_xticklabels to label x-axis ticks. Remember violin plot x-ticks start at 1.

4

Display the plots

Add a line to display the plots using plt.show().

Matplotlib

import matplotlib.pyplot as plt

sales_data = {
    'North': [250, 270, 300, 280, 260],
    'South': [220, 210, 230, 240, 225],
    'East': [300, 320, 310, 305, 315]
}

regions = ['North', 'South', 'East']

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))

# Prepare data list for plotting
data_values = [sales_data[region] for region in regions]

ax1.boxplot(data_values)
ax1.set_xticklabels(regions)
ax1.set_title('Box Plot')

ax2.violinplot(data_values)
ax2.set_xticks([1, 2, 3])
ax2.set_xticklabels(regions)
ax2.set_title('Violin Plot')

# Display the plots
# Your code here

Hint

Use plt.show() to display the figure with both plots.

Practice

1. Which of the following best describes the main difference between a box plot and a violin plot?

easy

A. Box plots show the full data distribution shape, while violin plots only show median and quartiles.

B. Box plots show summary statistics like median and quartiles, while violin plots show the full data distribution shape.

C. Both plots show only the mean and standard deviation of the data.

D. Violin plots are used only for categorical data, box plots only for numerical data.

Solution

1. Step 1: Understand box plot features

   Box plots display median, quartiles, and outliers, summarizing key statistics.

2. Step 2: Understand violin plot features

   Violin plots show the full distribution shape using a density curve, revealing data spread and modality.

3. Final Answer:

   Box plots show summary statistics like median and quartiles, while violin plots show the full data distribution shape. -> Option B

4. Quick Check:

   Box = summary stats, Violin = distribution shape [OK]

Hint: Box = stats summary, Violin = full distribution shape [OK]

Common Mistakes:

• Confusing violin plots as only showing summary stats
• Thinking box plots show full distribution shape
• Assuming violin plots are for categorical data only

2. Which of the following is the correct way to create a violin plot using matplotlib for a list of values data?

easy

A. plt.plot.violin(data)

B. plt.boxplot(data)

C. plt.violin(data)

D. plt.violinplot(data)

Solution

1. Step 1: Recall matplotlib violin plot syntax

   The correct function to create a violin plot is plt.violinplot().

2. Step 2: Check other options for correctness

   plt.boxplot() creates box plots, plt.violin() and plt.plot.violin() are invalid functions.

3. Final Answer:

   plt.violinplot(data) -> Option D

4. Quick Check:

   Use plt.violinplot() for violin plots [OK]

Hint: Use plt.violinplot() to create violin plots [OK]

Common Mistakes:

• Using plt.boxplot() instead of plt.violinplot()
• Trying plt.violin() which does not exist
• Using plt.plot.violin() which is invalid

3. Given the following code, what will the output plot show?

import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
data = np.random.normal(0, 1, 100)
plt.boxplot(data)
plt.show()

medium

A. A plot showing the median, quartiles, and outliers of the data.

B. A plot showing the full distribution shape with density curves.

C. A scatter plot of all data points.

D. A line plot of the sorted data values.

Solution

1. Step 1: Identify the plot function used

   The code uses plt.boxplot(data), which creates a box plot.

2. Step 2: Understand box plot output

   Box plots display median, quartiles, and outliers, summarizing data distribution.

3. Final Answer:

   A plot showing the median, quartiles, and outliers of the data. -> Option A

4. Quick Check:

   plt.boxplot() = median, quartiles, outliers [OK]

Hint: plt.boxplot() shows median and quartiles [OK]

Common Mistakes:

• Confusing boxplot with violin plot output
• Expecting density curves from boxplot
• Thinking it shows scatter or line plot

4. The following code is intended to plot a violin plot but produces an error. What is the error?

import matplotlib.pyplot as plt
data = [1, 2, 3, 4, 5]
plt.violin(data)
plt.show()

medium

A. The function plt.violin() does not exist in matplotlib.

B. The data list must be converted to a numpy array first.

C. plt.show() must be called before plt.violin().

D. The data list is too short to plot a violin plot.

Solution

1. Step 1: Check the function name used

   The code calls plt.violin(), which is not a valid matplotlib function.

2. Step 2: Identify correct function for violin plots

   The correct function is plt.violinplot(), not plt.violin().

3. Final Answer:

   The function plt.violin() does not exist in matplotlib. -> Option A

4. Quick Check:

   Use plt.violinplot(), plt.violin() causes error [OK]

Hint: Use plt.violinplot(), not plt.violin() [OK]

Common Mistakes:

• Using plt.violin() instead of plt.violinplot()
• Thinking data type causes error
• Calling plt.show() too early

5. You have two groups of data and want to compare their distributions clearly. Which approach best uses both box and violin plots together in matplotlib?

hard

A. Plot box plots side by side for each group only.

B. Plot violin plots stacked vertically without box plots.

C. Overlay violin plots with box plots for each group side by side.

D. Plot only histograms for each group.

Solution

1. Step 1: Understand the goal of comparing distributions

   Using both box and violin plots together helps see summary stats and full distribution shape.

2. Step 2: Identify best plotting method

   Overlaying violin plots with box plots side by side for each group shows both views clearly for comparison.

3. Final Answer:

   Overlay violin plots with box plots for each group side by side. -> Option C

4. Quick Check:

   Combine box + violin side by side for best comparison [OK]

Hint: Overlay box and violin plots side by side for clear comparison [OK]

Common Mistakes:

• Using only box or only violin plots alone
• Stacking plots vertically without overlay
• Using histograms which show less detail