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

Need a 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

Need a 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

Need a 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

Need a hint?

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