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.