Secondary axes in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of plot with secondary y-axis

What will be the output of this code snippet that uses a secondary y-axis in matplotlib?

Matplotlib

import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y1 = [10, 20, 25, 30]
y2 = [100, 200, 250, 300]
fig, ax1 = plt.subplots()
ax1.plot(x, y1, 'g-')
ax2 = ax1.twinx()
ax2.plot(x, y2, 'b--')
plt.show()

A A plot with one x-axis, left y-axis in green line for y1, and right y-axis in blue dashed line for y2

B A plot with two x-axes and one y-axis showing both y1 and y2 overlapped

C A plot with one x-axis and one y-axis showing only y1 in green line

D A plot with one x-axis and one y-axis showing only y2 in blue dashed line

💡 Hint

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

The twinx() method creates a secondary y-axis on the right side. The first plot uses the left y-axis (green line), and the second plot uses the right y-axis (blue dashed line).

❓ data_output

intermediate

Number of ticks on secondary y-axis

Given this code, how many major ticks will the secondary y-axis have by default?

Matplotlib

import matplotlib.pyplot as plt
x = [0, 1, 2, 3]
y1 = [1, 4, 9, 16]
y2 = [10, 40, 90, 160]
fig, ax1 = plt.subplots()
ax1.plot(x, y1)
ax2 = ax1.twinx()
ax2.plot(x, y2)
print(len(ax2.get_yticks()))

A 5

B 10

C 7

D 4

💡 Hint

Check the default number of yticks matplotlib creates for a linear scale.

Matplotlib by default creates 7 major ticks on a linear axis unless customized.

❓ visualization

advanced

Identify the correct secondary x-axis transformation

Which option correctly creates a secondary x-axis that shows the square root of the primary x-axis values?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 16, 100)
y = np.sqrt(x)
fig, ax = plt.subplots()
ax.plot(x, y)
secax = ax.secondary_xaxis(1.0, functions=(np.sqrt, np.square))
secax.set_xlabel('Square root of x')
plt.show()

A secax = ax.secondary_xaxis(1.0, functions=(np.sqrt, np.square))

B secax = ax.secondary_xaxis(1.0, functions=(np.square, np.sqrt))

C secax = ax.secondary_xaxis(0.0, functions=(np.sqrt, np.square))

D secax = ax.secondary_xaxis(1.0, functions=(np.log, np.exp))

💡 Hint

The first function converts primary to secondary axis, the second converts back.

The secondary_xaxis method takes two functions: forward and inverse. To show square root on secondary axis, forward is sqrt, inverse is square.

🔧 Debug

advanced

Error when adding secondary y-axis with incompatible scales

What error will this code raise when trying to plot on a secondary y-axis with incompatible data types?

Matplotlib

import matplotlib.pyplot as plt
x = [1, 2, 3]
y1 = [10, 20, 30]
y2 = ['a', 'b', 'c']
fig, ax1 = plt.subplots()
ax1.plot(x, y1)
ax2 = ax1.twinx()
ax2.plot(x, y2)
plt.show()

A ValueError: x and y must have same first dimension

B No error, plot displays with categorical y2

C SyntaxError: invalid syntax

D TypeError: float() argument must be a string or a number, not 'str'

💡 Hint

Check if matplotlib can plot strings as y-values.

Matplotlib cannot convert string values to floats for plotting numeric axes, so it raises a TypeError.

🚀 Application

expert

Using secondary y-axis to compare different units

You have temperature data in Celsius and Fahrenheit for the same dates. Which code snippet correctly plots Celsius on the left y-axis and Fahrenheit on the right y-axis using secondary y-axis?

Matplotlib

import matplotlib.pyplot as plt
dates = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']
celsius = [0, 10, 20, 15, 5]
fahrenheit = [32, 50, 68, 59, 41]
fig, ax1 = plt.subplots()
ax1.plot(dates, celsius, 'r-', label='Celsius')
ax2 = ax1.twinx()
ax2.plot(dates, fahrenheit, 'b--', label='Fahrenheit')
ax1.set_ylabel('Celsius')
ax2.set_ylabel('Fahrenheit')
plt.show()

A The code runs but both lines overlap on the same y-axis scale

B The code runs correctly and shows Celsius on left y-axis and Fahrenheit on right y-axis

C The code raises TypeError because twinx() cannot be used with string x-axis

D The code raises ValueError because dates are strings and cannot be plotted

💡 Hint

Matplotlib can plot categorical x-axis labels as strings.

Matplotlib supports string x-axis labels and twinx() creates a secondary y-axis. The code correctly plots Celsius and Fahrenheit on separate y-axes.

Practice

1. What is the main purpose of using secondary_xaxis or secondary_yaxis in matplotlib?

easy

A. To add a new axis with a different scale on the same plot

B. To create a separate figure window

C. To change the color of the existing axis

D. To remove the original axis from the plot

Solution

1. Step 1: Understand the concept of secondary axes

   Secondary axes allow adding a new axis that shares the same plot but has a different scale or units.

2. Step 2: Identify the purpose of secondary_xaxis or secondary_yaxis

   These functions add a secondary axis to the existing plot to compare data with different units clearly.

3. Final Answer:

   To add a new axis with a different scale on the same plot -> Option A

4. Quick Check:

   Secondary axes = different scale on same plot [OK]

Hint: Secondary axes add a new scale on the same plot [OK]

Common Mistakes:

• Thinking it creates a new figure
• Confusing axis color change with adding axes
• Assuming it removes original axes

2. Which of the following is the correct way to add a secondary y-axis in matplotlib?

easy

A. ax.add_secondary_yaxis('right', functions=(forward, inverse))

B. ax.add_secondary_axis('right', functions=(forward, inverse))

C. ax.secondary_yaxis('right', functions=(forward, inverse))

D. ax.secondary_axis('right', functions=(forward, inverse))

Solution

1. Step 1: Recall the matplotlib method for secondary y-axis

   The correct method is secondary_yaxis called on the axes object.

2. Step 2: Match the exact syntax

   The method is ax.secondary_yaxis('right', functions=(forward, inverse)) where 'right' is the position and functions are conversion functions.

3. Final Answer:

   ax.secondary_yaxis('right', functions=(forward, inverse)) -> Option C

4. Quick Check:

   Correct method name = secondary_yaxis [OK]

Hint: Use ax.secondary_yaxis with conversion functions [OK]

Common Mistakes:

• Using incorrect method names like add_secondary_axis
• Missing the functions parameter
• Using secondary_axis instead of secondary_yaxis

3. What will be the output of this code snippet?

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([0, 1, 2], [0, 10, 20])

forward = lambda x: x / 10
inverse = lambda x: x * 10
secax = ax.secondary_yaxis('right', functions=(forward, inverse))
print(secax.get_ylim())

medium

A. [0.0, 2.0]

B. [0, 20]

C. [0.0, 200.0]

D. [0, 10]

Solution

1. Step 1: Understand the original y-axis limits

   The original y-axis data ranges from 0 to 20, so default limits are approximately (0, 20).

2. Step 2: Apply the forward function to original limits for secondary axis

   The forward function divides by 10, so 0 becomes 0.0 and 20 becomes 2.0 on the secondary axis.

3. Final Answer:

   [0.0, 2.0] -> Option A

4. Quick Check:

   Secondary axis limits = original limits / 10 [OK]

Hint: Secondary axis limits = apply forward function to original limits [OK]

Common Mistakes:

• Confusing forward and inverse functions
• Expecting original limits instead of transformed
• Assuming inverse function applies to limits

4. Identify the error in this code that tries to add a secondary x-axis:

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.plot([0, 1, 2], [0, 1, 4])

forward = lambda x: x * 2
inverse = lambda x: x / 2
secax = ax.secondary_xaxis('top', functions=(inverse, forward))

medium

A. The lambda functions should not be used

B. The order of functions in the tuple is reversed

C. The position 'top' is invalid for secondary_xaxis

D. The method name should be secondary_yaxis instead

Solution

1. Step 1: Check the order of conversion functions

   The first function should convert from primary to secondary axis (forward), the second from secondary to primary (inverse).

2. Step 2: Identify the mistake in the code

   The code uses (inverse, forward) instead of (forward, inverse), so the order is reversed causing incorrect scaling.

3. Final Answer:

   The order of functions in the tuple is reversed -> Option B

4. Quick Check:

   Functions order = (forward, inverse) [OK]

Hint: First function forward, second inverse in tuple [OK]

Common Mistakes:

• Swapping forward and inverse functions
• Using invalid axis position
• Confusing secondary_xaxis with secondary_yaxis

5. You have temperature data in Celsius and want to add a secondary y-axis in Fahrenheit on the right side. Which of these code snippets correctly creates the secondary axis with proper conversion?

hard

A. forward = lambda f: (f - 32) * 5/9
inverse = lambda c: (c * 9/5) + 32
ax.secondary_yaxis('right', functions=(inverse, forward))

B. forward = lambda c: (c * 5/9) + 32
inverse = lambda f: (f - 32) * 9/5
ax.secondary_yaxis('right', functions=(forward, inverse))

C. forward = lambda c: (c - 32) * 5/9
inverse = lambda f: (f * 9/5) + 32
ax.secondary_yaxis('left', functions=(forward, inverse))

D. forward = lambda c: (c * 9/5) + 32
inverse = lambda f: (f - 32) * 5/9
ax.secondary_yaxis('right', functions=(forward, inverse))

Solution

1. Step 1: Recall Celsius to Fahrenheit conversion formulas

   Celsius to Fahrenheit: F = (C * 9/5) + 32; Fahrenheit to Celsius: C = (F - 32) * 5/9.

2. Step 2: Match the forward and inverse functions correctly

   Forward converts primary (C) to secondary (F), inverse converts secondary (F) back to primary (C). Position 'right' is correct for secondary y-axis.

3. Final Answer:

   forward = lambda c: (c * 9/5) + 32 inverse = lambda f: (f - 32) * 5/9 ax.secondary_yaxis('right', functions=(forward, inverse)) -> Option D

4. Quick Check:

   Correct conversion and axis position = forward = lambda c: (c * 9/5) + 32 inverse = lambda f: (f - 32) * 5/9 ax.secondary_yaxis('right', functions=(forward, inverse)) [OK]

Hint: Forward: C to F; Inverse: F to C; position 'right' for y-axis [OK]

Common Mistakes:

• Swapping forward and inverse functions
• Using wrong conversion formulas
• Placing secondary axis on 'left' instead of 'right'