Spine charts concept in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a basic spine chart plot

What will be the output of the following Python code using matplotlib to create a spine chart?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

labels = ['A', 'B', 'C', 'D']
values = [10, 15, 7, 12]

fig, ax = plt.subplots()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('outward', 10))
ax.plot(labels, values, marker='o')
plt.show()

A A line plot with markers but all spines visible in default positions.

B A bar chart with bars for A, B, C, D and all four spines visible in default positions.

C A scatter plot with points at A, B, C, D and all spines hidden.

D A line plot with markers connecting points A, B, C, D and only left and bottom spines visible, shifted outward by 10 points.

💡 Hint

Look at which spines are set visible and their positions.

The code hides the top and right spines, and moves the left and bottom spines outward by 10 points. The plot is a line plot with markers connecting the points labeled A to D.

❓ data_output

intermediate

Number of visible spines after customization

After running this code, how many spines are visible on the plot?

Matplotlib

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
plt.show()

A 2

B 3

C 4

D 1

💡 Hint

Check which spines are set to visible True or False.

Only the left and bottom spines are visible; top and right are hidden.

🔧 Debug

advanced

Identify the error in spine position setting

What error will this code raise when trying to set spine position?

Matplotlib

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.spines['left'].set_position('outward', 10)
plt.show()

A No error, the code runs successfully

B TypeError: set_position() takes 2 positional arguments but 3 were given

C ValueError: spine position must be a tuple or string

D AttributeError: 'Spine' object has no attribute 'set_position'

💡 Hint

Check the method signature for set_position in matplotlib spines.

The set_position method expects a single argument which can be a string or a tuple. Passing two separate arguments causes a TypeError.

❓ visualization

advanced

Effect of spine position on plot appearance

Which option best describes the visual effect of setting the bottom spine position to ('axes', 0.5)?

Matplotlib

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.spines['bottom'].set_position(('axes', 0.5))
ax.plot([0, 1], [0, 1])
plt.show()

A The bottom spine moves to the middle of the plot area horizontally at half the axes height.

B The bottom spine moves outside the plot area by 0.5 inches.

C The bottom spine stays at the default position at the bottom edge.

D The bottom spine disappears from the plot.

💡 Hint

The 'axes' coordinate system ranges from 0 (bottom) to 1 (top) vertically.

Setting position to ('axes', 0.5) moves the spine to the vertical center of the axes area.

🧠 Conceptual

expert

Purpose of spine customization in data visualization

Why would a data scientist customize spines in a plot? Choose the best reason.

A To change the data values shown on the plot axes.

B To increase the plot size automatically based on data range.

C To improve plot readability by emphasizing important axes and reducing clutter.

D To add interactive features like zoom and pan to the plot.

💡 Hint

Think about how visual design affects understanding of data.

Customizing spines helps highlight key axes and remove unnecessary lines, making plots clearer and easier to read.

Practice

1. What is the main purpose of a spine chart in matplotlib?

easy

A. To connect data points with lines to compare two groups

B. To display data as a pie chart

C. To create 3D surface plots

D. To show only individual data points without connections

Solution

1. Step 1: Understand spine chart basics

   A spine chart connects data points with lines to compare two groups visually.

2. Step 2: Compare with other chart types

   Pie charts and 3D plots serve different purposes, not spine charts.

3. Final Answer:

   To connect data points with lines to compare two groups -> Option A

4. Quick Check:

   Spine chart = line connection for comparison [OK]

Hint: Spine charts connect points to compare groups visually [OK]

Common Mistakes:

• Confusing spine charts with pie charts
• Thinking spine charts show 3D data
• Assuming spine charts show unconnected points

2. Which of the following is the correct way to create a spine chart using matplotlib?

easy

A. plt.bar(x, y)

B. plt.scatter(x, y)

C. plt.plot(x, y, marker='o', linestyle='-')

D. plt.pie(y)

Solution

1. Step 1: Identify spine chart syntax

   Spine charts use lines connecting points, so plt.plot with markers and line style is correct.

2. Step 2: Eliminate other plot types

   Bar, scatter, and pie charts do not connect points with lines.

3. Final Answer:

   plt.plot(x, y, marker='o', linestyle='-') -> Option C

4. Quick Check:

   Line plot with markers = spine chart [OK]

Hint: Use plt.plot with markers and lines for spine charts [OK]

Common Mistakes:

• Using plt.bar or plt.pie instead of plt.plot
• Forgetting to add markers or lines
• Confusing scatter plots with spine charts

3. What will be the output of the following code?

import matplotlib.pyplot as plt
x = [1, 2, 3]
y1 = [4, 5, 6]
y2 = [7, 8, 9]
plt.plot(x, y1, marker='o', label='Group 1')
plt.plot(x, y2, marker='s', label='Group 2')
plt.legend()
plt.show()

medium

A. A scatter plot without lines

B. A bar chart comparing Group 1 and Group 2

C. An error due to missing plot commands

D. Two lines with different markers connecting points for Group 1 and Group 2

Solution

1. Step 1: Analyze the plot commands

   Two plt.plot calls with markers 'o' and 's' create two lines with different markers.

2. Step 2: Understand legend and show

   Legend labels groups, plt.show() displays the plot with connected lines.

3. Final Answer:

   Two lines with different markers connecting points for Group 1 and Group 2 -> Option D

4. Quick Check:

   Multiple plt.plot with markers = multiple spine lines [OK]

Hint: Multiple plt.plot with markers create spine chart lines [OK]

Common Mistakes:

• Expecting bar or scatter plot output
• Thinking plt.plot without plt.show() shows plot
• Confusing markers with bar heights

4. Identify the error in this spine chart code:

import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [4, 5, 6]
plt.plot(x, y, marker='o')
plt.plot(x, y, marker='s')
plt.show()

medium

A. No error; code runs and shows two overlapping lines

B. Missing linestyle argument causes error

C. Markers must be the same for all plots

D. x and y lists must be numpy arrays

Solution

1. Step 1: Check syntax and parameters

   plt.plot accepts marker without linestyle; default line style is solid.

2. Step 2: Understand plot behavior

   Two plots with same data but different markers overlap; no error occurs.

3. Final Answer:

   No error; code runs and shows two overlapping lines -> Option A

4. Quick Check:

   Different markers allowed; no error [OK]

Hint: Different markers allowed; no linestyle needed [OK]

Common Mistakes:

• Assuming missing linestyle causes error
• Thinking markers must match
• Believing lists must be numpy arrays

5. You want to create a spine chart comparing sales of two products over 4 quarters with different colors and markers. Which code snippet correctly achieves this?

hard

A. plt.bar(quarters, sales1, color='blue')
plt.bar(quarters, sales2, color='red')
plt.show()

B. plt.plot(quarters, sales1, marker='o', color='blue', label='Product A')
plt.plot(quarters, sales2, marker='s', color='red', label='Product B')
plt.legend()
plt.show()

C. plt.scatter(quarters, sales1, color='blue')
plt.scatter(quarters, sales2, color='red')
plt.show()

D. plt.plot(sales1, quarters, marker='o')
plt.plot(sales2, quarters, marker='s')
plt.show()

Solution

1. Step 1: Check correct spine chart syntax

   Using plt.plot with markers, colors, and labels matches the requirement.

2. Step 2: Verify other options

   Bar and scatter plots do not connect points with lines; swapping axes reverses data meaning.

3. Final Answer:

   plt.plot(quarters, sales1, marker='o', color='blue', label='Product A') plt.plot(quarters, sales2, marker='s', color='red', label='Product B') plt.legend() plt.show() -> Option B

4. Quick Check:

   Line plots with markers and colors = spine chart [OK]

Hint: Use plt.plot with markers, colors, and labels for spine charts [OK]

Common Mistakes:

• Using bar or scatter plots instead of line plots
• Swapping x and y axes incorrectly
• Omitting legend for group labels