Spine charts concept in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the matplotlib library for plotting spine charts.

Matplotlib

import [1] as plt

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

Aseaborn

Bpandas

Cmatplotlib.pyplot

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the whole matplotlib instead of pyplot.

Using unrelated libraries like pandas or numpy.

We use matplotlib.pyplot as the standard module for plotting charts including spine charts.

2fill in blank

medium

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

Matplotlib

fig, ax = plt.[1]()

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Asubplots

Bshow

Cplot

Dfigure

Attempts:

3 left

💡 Hint

Common Mistakes

Using plot() which draws lines but does not create figure and axis.

Using figure() alone which returns only the figure.

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

3fill in blank

hard

Fix the error in the code to set the spine color to red.

Matplotlib

ax.spines['bottom'].set_[1]('red')

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Aposition

Bwidth

Cvisible

Dcolor

Attempts:

3 left

💡 Hint

Common Mistakes

Using set_width which changes thickness, not color.

Using set_visible which hides or shows the spine.

The set_color() method changes the spine color.

4fill in blank

hard

Fill both blanks to hide the top and right spines of the plot.

Matplotlib

ax.spines['[1]'].set_visible(False)
ax.spines['[2]'].set_visible(False)

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Atop

Bbottom

Cright

Dleft

Attempts:

3 left

💡 Hint

Common Mistakes

Hiding bottom or left spines instead of top or right.

Using set_color instead of set_visible.

To hide the top and right spines, set their visibility to False.

5fill in blank

hard

Fill all three blanks to create a spine chart with custom spine positions.

Matplotlib

ax.spines['left'].set_position(('[1]', [2]))
ax.spines['bottom'].set_position(('[3]', 0))

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

Aoutward

Baxes

Cdata

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect position types like 'left' or 'right'.

Forgetting to provide the numeric value for the position.

The left spine is moved outward by 10 points, and the bottom spine is positioned at the axes coordinate 0.

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