Dumbbell charts in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the matplotlib plotting library.

Matplotlib

import [1] as plt

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Aseaborn

Bmatplotlib.pyplot

Cpandas

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong library like numpy or pandas instead of matplotlib.pyplot.

Forgetting to use 'as plt' for shorthand.

We import matplotlib.pyplot as plt to create plots.

2fill in blank

medium

Complete the code to create a figure and axis for plotting.

Matplotlib

fig, ax = plt.[1]()

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Asubplots

Bfigure

Cplot

Dshow

Attempts:

3 left

💡 Hint

Common Mistakes

Using plt.plot() which only plots data but does not create axes.

Using plt.figure() which creates a figure but not axes.

The subplots() function creates a figure and axes for plotting.

3fill in blank

hard

Fix the error in the code to plot two points connected by a line for a dumbbell chart.

Matplotlib

ax.plot([1, 2], [[1], 5], marker='o', color='blue')

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A7

B5

C1

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same y-value twice, which plots a horizontal line.

Using a single number instead of a list of two numbers.

The y-values must be two numbers to plot points connected by a line. Using 3 and 5 creates a line between points at y=3 and y=5.

4fill in blank

hard

Fill both blanks to create a dumbbell chart connecting two sets of values for categories.

Matplotlib

ax.hlines(y=[1], xmin=[2], xmax=[1], color='gray', linewidth=2)

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A[1, 2, 3, 4]

B[5, 6, 7, 8]

C[0, 1, 2, 3]

D[4, 5, 6, 7]

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same list for y and xmin, which results in zero-length lines.

Swapping xmin and xmax values.

The horizontal lines are drawn at y positions from the categories list (e.g., [1, 2, 3, 4]). The xmin values are the smaller values (e.g., [0, 1, 2, 3]) to connect to xmax.

5fill in blank

hard

Fill all three blanks to complete the dumbbell chart code with points and lines.

Matplotlib

ax.scatter([1], [2], color='red', label='Start')
ax.scatter([3], [2], color='blue', label='End')
ax.hlines(y=[2], xmin=[1], xmax=[3], color='gray')

Drag options to blanks, or click blank then click option'

A[1, 2, 3, 4]

B[5, 6, 7, 8]

D[0, 1, 2, 3]

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up xmin and xmax values.

Using different y values for scatter and lines.

The y positions are the category indices [1, 2, 3, 4]. The start points are at xmin values [0, 1, 2, 3], and the end points are at xmax values [5, 6, 7, 8].

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