Dumbbell charts in Matplotlib - Mini Project: Build & Apply

Dumbbell charts

📖 Scenario: You are a data analyst at a fitness center. You want to compare the average weights lifted by two groups of people: beginners and advanced trainees. A dumbbell chart is a great way to show the difference between these two groups for each exercise.

🎯 Goal: Build a dumbbell chart using matplotlib to visually compare the average weights lifted by beginners and advanced trainees for different exercises.

📋 What You'll Learn

Create a dictionary with exercises as keys and tuples of (beginner weight, advanced weight) as values.

Create a list of exercises from the dictionary keys.

Extract beginner and advanced weights into separate lists.

Plot a dumbbell chart using matplotlib with lines connecting beginner and advanced weights for each exercise.

Label the axes and add a title.

💡 Why This Matters

🌍 Real World

Dumbbell charts are useful to compare two related values side-by-side for multiple categories, such as comparing performance before and after training.

💼 Career

Data analysts and scientists often create such charts to communicate differences clearly in reports and presentations.

1

Create the data dictionary

Create a dictionary called weights with these exact entries: 'Squat': (50, 100), 'Bench Press': (40, 90), 'Deadlift': (60, 120), 'Overhead Press': (30, 70), 'Barbell Row': (45, 95).

Matplotlib

# Your code here

Hint

Use curly braces to create a dictionary. Each key is a string for the exercise, and each value is a tuple with two numbers.

2

Prepare lists for plotting

Create a list called exercises from the keys of weights. Then create two lists called beginner_weights and advanced_weights by extracting the first and second elements of the tuples in weights respectively.

Matplotlib

weights = {'Squat': (50, 100), 'Bench Press': (40, 90), 'Deadlift': (60, 120), 'Overhead Press': (30, 70), 'Barbell Row': (45, 95)}
# Your code here

Hint

Use list(weights.keys()) to get the exercises. Use list comprehensions to get beginner and advanced weights.

3

Plot the dumbbell chart

Import matplotlib.pyplot as plt. Use plt.hlines to draw horizontal lines between beginner_weights and advanced_weights for each exercise index. Then use plt.scatter to plot the beginner and advanced weights as points. Set the y-axis ticks to exercises.

Matplotlib

weights = {'Squat': (50, 100), 'Bench Press': (40, 90), 'Deadlift': (60, 120), 'Overhead Press': (30, 70), 'Barbell Row': (45, 95)}
exercises = list(weights.keys())
beginner_weights = [weights[ex][0] for ex in exercises]
advanced_weights = [weights[ex][1] for ex in exercises]
# Your code here

Hint

Use plt.hlines to draw lines, and plt.scatter to plot points. Use range(len(exercises)) for y positions.

4

Display the chart

Add plt.show() to display the dumbbell chart.

Matplotlib

import matplotlib.pyplot as plt

weights = {'Squat': (50, 100), 'Bench Press': (40, 90), 'Deadlift': (60, 120), 'Overhead Press': (30, 70), 'Barbell Row': (45, 95)}
exercises = list(weights.keys())
beginner_weights = [weights[ex][0] for ex in exercises]
advanced_weights = [weights[ex][1] for ex in exercises]

plt.hlines(y=range(len(exercises)), xmin=beginner_weights, xmax=advanced_weights, color='gray', alpha=0.7)
plt.scatter(beginner_weights, range(len(exercises)), color='blue', label='Beginner')
plt.scatter(advanced_weights, range(len(exercises)), color='red', label='Advanced')
plt.yticks(range(len(exercises)), exercises)
plt.xlabel('Weight (kg)')
plt.title('Dumbbell Chart: Beginner vs Advanced Weights')
plt.legend()
# Your code here

Hint

Use plt.show() to display the plot window.

Practice

1. What is the main purpose of a dumbbell chart in data visualization?

easy

A. To compare two related values for each category using connected points

B. To show the distribution of a single variable using bars

C. To display the relationship between two continuous variables as a scatter plot

D. To visualize hierarchical data using nested rectangles

Solution

1. Step 1: Understand dumbbell chart structure

   Dumbbell charts show two points per category connected by a line to highlight differences or changes.

2. Step 2: Compare with other chart types

   Bar charts show single values, scatter plots show relationships, and treemaps show hierarchies, so they don't fit dumbbell chart purpose.

3. Final Answer:

   To compare two related values for each category using connected points -> Option A

4. Quick Check:

   Dumbbell chart = compare two values [OK]

Hint: Dumbbell charts connect two points per category [OK]

Common Mistakes:

• Confusing dumbbell charts with bar charts
• Thinking dumbbell charts show distributions
• Mixing up scatter plots with dumbbell charts

2. Which matplotlib functions are typically used together to create a dumbbell chart?

easy

A. bar and plot

B. pie and bar

C. hist and scatter

D. hlines and scatter

Solution

1. Step 1: Identify dumbbell chart components

   Dumbbell charts use horizontal lines to connect two points and scatter points to mark values.

2. Step 2: Match matplotlib functions

   hlines draws horizontal lines, scatter plots points, so these two create dumbbell charts.

3. Final Answer:

   hlines and scatter -> Option D

4. Quick Check:

   Lines + points = hlines & scatter [OK]

Hint: Use hlines for lines and scatter for points [OK]

Common Mistakes:

• Using bar instead of hlines for connecting lines
• Confusing hist with scatter
• Trying pie charts for dumbbell visualization

3. What will be the output of this code snippet creating a dumbbell chart?

import matplotlib.pyplot as plt
categories = ['A', 'B']
start = [1, 3]
end = [4, 5]
plt.hlines(y=categories, xmin=start, xmax=end, color='gray')
plt.scatter(start, categories, color='blue')
plt.scatter(end, categories, color='red')
plt.show()

medium

A. A horizontal dumbbell chart with two categories showing blue and red points connected by gray lines

B. A vertical bar chart with categories on x-axis and values on y-axis

C. An error because categories cannot be used as y-values

D. A scatter plot with categories on y-axis and points only at start values

Solution

1. Step 1: Analyze hlines command

   plt.hlines(y=categories, xmin=start, xmax=end, color='gray') fails because y-coordinates must be numeric, but categories=['A', 'B'] are strings. Raises ValueError: could not convert string to float.

2. Step 2: Confirm scatter commands also fail

   plt.scatter(start, categories) and subsequent scatter require numeric y-values, leading to the same error.

3. Final Answer:

   An error because categories cannot be used as y-values -> Option C

4. Quick Check:

   Strings as y-values in hlines/scatter -> error [OK]

Hint: hlines and scatter require numeric y-positions; map categories to indices [OK]

Common Mistakes:

• Assuming strings work directly as y-values
• Expecting horizontal dumbbell chart output
• Confusing with bar chart output

4. Identify the error in this dumbbell chart code and select the fix:

import matplotlib.pyplot as plt
categories = ['X', 'Y']
start = [2, 4]
end = [5, 7]
plt.hlines(y=categories, xmin=start, xmax=end)
plt.scatter(start, categories)
plt.scatter(end, categories)
plt.show()

medium

A. No error; code runs correctly

B. Error because categories are strings; convert categories to numeric indices

C. Error because scatter requires numeric y-values; swap x and y in scatter

D. Error due to missing color argument in hlines

Solution

1. Step 1: Check data types for plotting

   hlines and scatter both require numeric y-values. Passing string categories as y causes ValueError (cannot convert string to float).

2. Step 2: Fix by using numeric indices

   Create y_pos = list(range(len(categories))), use y=y_pos in hlines and scatters, then plt.yticks(y_pos, categories).

3. Final Answer:

   Error because categories are strings; convert categories to numeric indices -> Option B

4. Quick Check:

   Categories (str) -> numeric indices [OK]

Hint: Use numeric y_pos = range(len(categories)); set labels with yticks [OK]

Common Mistakes:

• Assuming hlines and scatter accept strings equally
• Ignoring axis data type requirements
• Missing that scatter needs numeric coordinates

5. You have sales data for two years for 4 products: products = ['P1', 'P2', 'P3', 'P4'], sales_2022 = [100, 150, 200, 130], sales_2023 = [120, 140, 210, 160]. How would you create a dumbbell chart to compare sales changes using matplotlib?

hard

A. Use plt.hlines with y as products, xmin as sales_2022, xmax as sales_2023, and plt.scatter for points

B. Use plt.bar for both years side by side

C. Use plt.plot with products on x-axis and sales on y-axis

D. Use plt.scatter only with sales_2023 values

Solution

1. Step 1: Understand dumbbell chart requirements

   We need to show two values per product connected by a line to compare sales in 2022 and 2023.

2. Step 2: Map data to plotting functions

   hlines draws lines between sales_2022 and sales_2023 at y=products; scatter plots points for each year's sales.

3. Final Answer:

   Use plt.hlines with y as products, xmin as sales_2022, xmax as sales_2023, and plt.scatter for points -> Option A

4. Quick Check:

   Dumbbell = hlines + scatter with two values [OK]

Hint: Connect two year values with hlines; mark points with scatter [OK]

Common Mistakes:

• Using bar charts instead of dumbbell charts
• Plotting only one year's data
• Not connecting points with lines