Dual y-axis for different scales in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of dual y-axis plot code

What will be the output of this Python code using matplotlib?

Matplotlib

import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y1 = [10, 20, 30, 40, 50]
y2 = [100, 200, 300, 400, 500]
fig, ax1 = plt.subplots()
ax1.plot(x, y1, 'g-')
ax2 = ax1.twinx()
ax2.plot(x, y2, 'b--')
plt.show()

A A single plot with two y-axes: left y-axis green solid line for y1, right y-axis blue dashed line for y2

B Two separate plots stacked vertically, one with green line and one with blue dashed line

C A plot with one y-axis showing both green and blue lines overlapping

D SyntaxError due to incorrect use of twinx()

💡 Hint

Remember that twinx() creates a second y-axis sharing the same x-axis.

The code creates one figure with two y-axes. The first axis plots y1 with a green solid line on the left y-axis. The second axis, created by twinx(), plots y2 with a blue dashed line on the right y-axis.

❓ data_output

intermediate

Number of ticks on each y-axis

Given this code snippet, how many major ticks will appear on the left and right y-axes respectively?

Matplotlib

import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y1 = [10, 20, 30, 40, 50]
y2 = [100, 200, 300, 400, 500]
fig, ax1 = plt.subplots()
ax1.plot(x, y1, 'r-')
ax2 = ax1.twinx()
ax2.plot(x, y2, 'b-')
left_ticks = len(ax1.get_yticks())
right_ticks = len(ax2.get_yticks())
print(left_ticks, right_ticks)

A 5 6

B 5 5

C 6 5

D 6 6

💡 Hint

Default matplotlib y-axis ticks usually include the min and max plus intermediate ticks.

By default, matplotlib creates 5 major ticks on each y-axis for these ranges. Both axes have 5 ticks.

🔧 Debug

advanced

Identify the error in dual y-axis code

What error will this code produce?

Matplotlib

import matplotlib.pyplot as plt
x = [1, 2, 3]
y1 = [10, 20, 30]
y2 = [100, 200, 300]
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot(x, y1, 'r-')
ax2.plot(x, y2, 'b-')
ax1.set_ylabel('Y1')
ax2.set_ylabel('Y2')
ax1.set_xlabel('X')
ax2.set_xlabel('X')
plt.show()

A No error; plot shows correctly

B AttributeError: 'AxesSubplot' object has no attribute 'set_xlabel'

C The second set_xlabel call overwrites the first, no error but confusing labels

D RuntimeWarning: Multiple x-labels set on the same axes

💡 Hint

Check if setting xlabel on both axes causes an error or just overwrites.

Both axes share the same x-axis, so setting xlabel on both does not cause an error. The last call overwrites the label, but no error occurs.

❓ visualization

advanced

Visual difference between single and dual y-axis plots

Which option best describes the visual difference between these two plots?

Plot 1: Single y-axis plotting y1 and y2 on the same scale.
Plot 2: Dual y-axis plotting y1 on left and y2 on right with different scales.

A Plot 1 shows two separate plots; Plot 2 shows one plot with one y-axis

B Plot 1 has two y-axes; Plot 2 has one y-axis

C Plot 1 and Plot 2 look identical

D Plot 1 shows overlapping lines possibly distorted; Plot 2 shows clear lines with separate scales

💡 Hint

Think about how different scales affect line visibility on one axis.

Plot 1 forces both lines on one scale, causing one line to appear flat or distorted. Plot 2 uses two y-axes to show both lines clearly.

🧠 Conceptual

expert

Reason to use dual y-axis in data visualization

Why would a data scientist choose to use a dual y-axis plot?

A To create two separate plots stacked vertically for better clarity

B To plot two variables with identical scales for simplicity

C To compare two variables with very different scales on the same x-axis clearly

D To avoid labeling axes and keep the plot minimal

💡 Hint

Think about when one y-axis scale is not enough to show both data sets clearly.

Dual y-axis plots allow comparing two variables with different units or scales on the same x-axis without distortion.

Practice

1. What is the main purpose of using a dual y-axis in a matplotlib plot?

easy

A. To compare two datasets with different scales on the same plot

B. To create two separate plots side by side

C. To add a legend to the plot

D. To change the color of the plot lines

Solution

1. Step 1: Understand the purpose of dual y-axis

   A dual y-axis allows plotting two datasets with different value ranges on the same graph for easy comparison.

2. Step 2: Identify the correct use case

   Comparing datasets with different scales requires two y-axes; other options do not describe this purpose.

3. Final Answer:

   To compare two datasets with different scales on the same plot -> Option A

4. Quick Check:

   Dual y-axis = compare different scales [OK]

Hint: Dual y-axis means two y-scales on one graph [OK]

Common Mistakes:

• Thinking dual y-axis creates two separate plots
• Confusing dual y-axis with adding legends
• Assuming it changes line colors only

2. Which matplotlib function is used to create a second y-axis sharing the same x-axis?

easy

A. plt.subplots()

B. ax.twiny()

C. ax.twinx()

D. plt.plot()

Solution

1. Step 1: Recall matplotlib methods for axes

   The method twinx() creates a second y-axis sharing the x-axis.

2. Step 2: Differentiate from similar methods

   twiny() creates a second x-axis, not y-axis; subplots() creates figure and axes; plot() draws lines.

3. Final Answer:

   ax.twinx() -> Option C

4. Quick Check:

   Second y-axis = twinx() [OK]

Hint: Use ax.twinx() for second y-axis, twiny() is for x-axis [OK]

Common Mistakes:

• Using plt.subplots() to create second y-axis
• Confusing twinx() with twiny()
• Trying to use plt.plot() to add axes

3. What will the following code output?

import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot([1, 2, 3], [10, 20, 30], 'g-')
ax2.plot([1, 2, 3], [100, 200, 300], 'b-')
print(len(fig.axes))

medium

A. 2

B. 1

C. 3

D. 4

Solution

1. Step 1: Understand axes creation

   plt.subplots() creates one axes (ax1), then ax1.twinx() adds a second y-axis (ax2), so total axes are 2.

2. Step 2: Check figure axes count

   fig.axes is a list of all axes in the figure; after adding ax2, length is 2.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Two axes after twinx() = 2 [OK]

Hint: twinx() adds one more axes, so total axes count increases by 1 [OK]

Common Mistakes:

• Assuming twinx() replaces the original axes
• Thinking twinx() adds multiple axes
• Confusing axes count with number of plots

4. Identify the error in this code snippet for creating dual y-axis:

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax2 = plt.twinx()
ax.plot([1,2,3], [10,20,30])
ax2.plot([1,2,3], [100,200,300])
plt.show()

medium

A. plt.show() is missing

B. plt.subplots() should be plt.figure()

C. ax2.plot() should be ax.plot()

D. plt.twinx() should be ax.twinx()

Solution

1. Step 1: Check method usage for second y-axis

   twinx() is a method of an axes object, not plt module. So plt.twinx() is invalid.

2. Step 2: Correct method call

   Use ax.twinx() to create the second y-axis sharing the x-axis.

3. Final Answer:

   plt.twinx() should be ax.twinx() -> Option D

4. Quick Check:

   twinx() is an axes method, not plt function [OK]

Hint: twinx() must be called on axes object, not plt [OK]

Common Mistakes:

• Calling twinx() on plt instead of axes
• Confusing figure and axes creation methods
• Missing plt.show() (though present here)

5. You want to plot temperature (°C) and rainfall (mm) over months on the same plot with dual y-axes. Which of these code snippets correctly sets different y-axis labels and colors for clarity?

hard

A. ax1.set_xlabel('Temperature (°C)', color='red')\nax2.set_xlabel('Rainfall (mm)', color='blue')

B. ax1.set_ylabel('Temperature (°C)', color='red')\nax2.set_ylabel('Rainfall (mm)', color='blue')

C. ax1.set_ylabel('Rainfall (mm)', color='blue')\nax2.set_ylabel('Temperature (°C)', color='red')

D. ax1.set_title('Temperature and Rainfall')\nax2.set_title('Temperature and Rainfall')

Solution

1. Step 1: Assign correct y-axis labels

   Temperature and rainfall are y-axis data, so use set_ylabel() on each axis with matching labels.

2. Step 2: Use colors to distinguish axes

   Setting label colors helps visually separate the two y-axes for clarity.

3. Final Answer:

   ax1.set_ylabel('Temperature (°C)', color='red')\nax2.set_ylabel('Rainfall (mm)', color='blue') -> Option B

4. Quick Check:

   Correct y-labels and colors = ax1.set_ylabel('Temperature (°C)', color='red')\nax2.set_ylabel('Rainfall (mm)', color='blue') [OK]

Hint: Label y-axes with units and colors for clear dual-axis plots [OK]

Common Mistakes:

• Setting x-axis labels instead of y-axis
• Swapping labels between axes
• Using titles instead of axis labels