Dumbbell charts in Matplotlib - Step-by-Step Execution

Concept Flow - Dumbbell charts

Prepare data with two values per category

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Plot points for first values

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Plot points for second values

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Draw lines connecting paired points

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Customize labels, colors, and axes

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Show chart

Dumbbell charts show two values per category connected by a line, highlighting differences visually.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
categories = ['A', 'B', 'C']
val1 = [10, 20, 30]
val2 = [15, 18, 35]
plt.scatter(val1, categories)
plt.scatter(val2, categories)
plt.hlines(categories, val1, val2)
plt.show()

This code plots two sets of points per category and connects them with horizontal lines to form a dumbbell chart.

Execution Table

Step	Action	Data/Variables	Plot Elements Created	Result
1	Import matplotlib.pyplot as plt	plt module loaded	None	Ready to plot
2	Define categories	categories = ['A', 'B', 'C']	None	Categories set
3	Define first values	val1 = [10, 20, 30]	None	First values set
4	Define second values	val2 = [15, 18, 35]	None	Second values set
5	Plot first points	plt.scatter(val1, categories)	Scatter points at (10,A), (20,B), (30,C)	First points visible
6	Plot second points	plt.scatter(val2, categories)	Scatter points at (15,A), (18,B), (35,C)	Second points visible
7	Draw horizontal lines	plt.hlines(categories, val1, val2)	Lines connecting (10,A)-(15,A), (20,B)-(18,B), (30,C)-(35,C)	Lines visible connecting points
8	Show plot	plt.show()	Full dumbbell chart displayed	Chart visible on screen

💡 Plot displayed and execution ends

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
categories	undefined	['A', 'B', 'C']	['A', 'B', 'C']	['A', 'B', 'C']	['A', 'B', 'C']
val1	undefined	undefined	[10, 20, 30]	[10, 20, 30]	[10, 20, 30]
val2	undefined	undefined	undefined	[15, 18, 35]	[15, 18, 35]

Key Moments - 3 Insights

Why do we use plt.hlines with categories as y-values?

Because categories are on the y-axis, plt.hlines draws horizontal lines at each category level connecting the two x-values (val1 and val2). See execution_table step 7.

What happens if val1 and val2 have different lengths?

Matplotlib will raise an error because scatter and hlines require matching lengths for x and y data. All arrays must align as shown in execution_table steps 5-7.

Why do we plot scatter points twice?

Each scatter plots one set of values (val1 and val2) so we see both points per category. This is shown in execution_table steps 5 and 6.

Visual Quiz - 3 Questions

Look at the execution_table at step 7, what does plt.hlines do?

A Draws vertical lines connecting categories

B Draws horizontal lines connecting val1 and val2 at each category

C Plots scatter points for val1

D Shows the plot window

💡 Hint

Check the 'Plot Elements Created' column at step 7 in execution_table

According to variable_tracker, what is the value of val2 after step 4?

A [10, 20, 30]

B undefined

C [15, 18, 35]

D ['A', 'B', 'C']

💡 Hint

Look at the val2 row and After Step 4 column in variable_tracker

If val1 was [10, 20] instead of length 3, what would happen?

A Matplotlib would raise an error due to length mismatch

B The third category would be plotted with zero value

C The plot would show only two categories

D The plot would ignore extra val2 values

💡 Hint

Refer to key_moments about array length matching and execution_table steps 5-7

Concept Snapshot

Dumbbell charts show two values per category connected by lines.
Use plt.scatter() twice for points, plt.hlines() to connect.
Categories on y-axis, values on x-axis.
Ensure val1, val2, and categories have same length.
Visualize differences clearly between paired values.

Full Transcript

Dumbbell charts help compare two values per category by plotting points and connecting them with lines. First, we prepare data arrays for categories and two sets of values. Then, we plot scatter points for each value set at the categories on the y-axis. Next, horizontal lines connect each pair of points to highlight differences. Finally, we show the plot. Variables categories, val1, and val2 hold the data. Scatter plots create points, and hlines draw connecting lines. Matching array lengths are important to avoid errors. This visual method clearly shows differences between paired data points.