Axis scales (linear, log) in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of linear vs log scale plot commands

What will be the y-axis limits after running this code snippet?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(1, 10, 10)
y = x ** 2
plt.plot(x, y)
plt.yscale('log')
plt.ylim(1, 100)
plt.show()
print(plt.gca().get_ylim())

A (1.0, 100.0)

B (0.0, 100.0)

C (1.0, 1000.0)

D (1.0, 10000.0)

💡 Hint

Remember that plt.ylim sets limits directly, even if scale is log.

The plt.ylim(1, 100) sets the y-axis limits explicitly. Even with log scale, the limits remain (1.0, 100.0).

❓ data_output

intermediate

Number of ticks on log scale axis

How many major ticks will appear on the y-axis after running this code?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(1, 100, 100)
y = x
plt.plot(x, y)
plt.yscale('log')
plt.show()
print(len(plt.gca().get_yticks()))

A 11

B 6

C 10

D 3

💡 Hint

Log scale major ticks usually appear at powers of 10.

The y-axis ranges from 1 to 100, so major ticks appear at 1, 10, 100, totaling 3 major ticks.

❓ visualization

advanced

Identify the plot with log scale on y-axis

Which plot shows a straight line when y = 10^x is plotted?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 3, 100)
y = 10 ** x

plt.figure(figsize=(8, 4))
plt.subplot(1, 2, 1)
plt.plot(x, y)
plt.title('Plot 1')

plt.subplot(1, 2, 2)
plt.plot(x, y)
plt.yscale('log')
plt.title('Plot 2')
plt.show()

A Plot 2

B Both plots show straight lines

C Plot 1

D Neither plot shows a straight line

💡 Hint

Log scale turns exponential curves into straight lines.

Plot 2 uses a log scale on y-axis, so y=10^x appears as a straight line.

🧠 Conceptual

advanced

Effect of log scale on zero or negative values

What happens if you set y-axis to log scale but your data contains zero or negative values?

A Plot crashes with a SyntaxError

B Plot raises a ValueError or warning and skips those points

C Plot converts zero and negative values to positive automatically

D Plot shows zero and negative values normally on log scale

💡 Hint

Logarithm of zero or negative numbers is undefined.

Matplotlib raises a warning or error and ignores zero or negative values when using log scale.

🔧 Debug

expert

Why does this log scale plot show empty y-axis?

Given this code, why does the plot show no data on y-axis?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.yscale('log')
plt.show()

A Because x values are zero, log scale fails on x-axis

B Because plt.plot does not support log scale

C Because y contains negative values, log scale cannot display them

D Because y values are too small to plot

💡 Hint

Check the range of y values and log scale requirements.

The sine function produces negative values, which cannot be shown on a log scale y-axis.

Practice

1. What does setting the axis scale to log do in a matplotlib plot?

easy

A. It shows data spaced by powers of 10, useful for wide-ranging values.

B. It reverses the axis direction.

C. It colors the axis labels differently.

D. It removes the axis ticks.

Solution

1. Step 1: Understand axis scale types

   Linear scale spaces data evenly, while log scale spaces data by powers of 10.

2. Step 2: Effect of log scale on data display

   Log scale compresses large ranges, showing exponential growth clearly.

3. Final Answer:

   It shows data spaced by powers of 10, useful for wide-ranging values. -> Option A

4. Quick Check:

   Log scale = spaced by powers of 10 [OK]

Hint: Log scale spaces data by powers of 10, not evenly [OK]

Common Mistakes:

• Thinking log scale reverses axis
• Confusing log scale with color changes
• Assuming log scale removes ticks

2. Which of the following is the correct way to set the y-axis to a logarithmic scale in matplotlib?

easy

A. plt.yaxis('log')

B. plt.axis('log')

C. plt.set_yscale('log')

D. plt.yscale('log')

Solution

1. Step 1: Recall matplotlib axis scale syntax

   The correct function to set axis scale is plt.yscale() for y-axis.

2. Step 2: Check correct usage

   Calling plt.yscale('log') sets y-axis to logarithmic scale.

3. Final Answer:

   plt.yscale('log') -> Option D

4. Quick Check:

   Use plt.yscale('log') to set y-axis log scale [OK]

Hint: Use plt.yscale('log') to set y-axis log scale [OK]

Common Mistakes:

• Using plt.axis('log') which is invalid
• Trying plt.set_yscale which does not exist
• Using plt.yaxis('log') which is incorrect

3. What will be the y-axis tick labels when running this code?

import matplotlib.pyplot as plt
plt.plot([1, 10, 100, 1000])
plt.yscale('log')
plt.show()

medium

A. [1, 10, 100, 1000]

B. [0, 1, 2, 3]

C. [1, 2, 3, 4]

D. [10, 100, 1000, 10000]

Solution

1. Step 1: Understand the data and scale

   The data points are 1, 10, 100, 1000 and y-axis is set to log scale.

2. Step 2: Effect of log scale on ticks

   Log scale shows ticks at powers of 10 matching data values, so ticks will be 1, 10, 100, 1000.

3. Final Answer:

   [1, 10, 100, 1000] -> Option A

4. Quick Check:

   Log scale ticks = powers of 10 matching data [OK]

Hint: Log scale ticks match powers of 10 in data [OK]

Common Mistakes:

• Expecting linear spaced ticks like 0,1,2,3
• Confusing tick labels with data indices
• Assuming ticks multiply by 10 beyond data range

4. Identify the error in this code that tries to set the x-axis to logarithmic scale:

import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.xscale('logarithmic')
plt.show()

medium

A. The method name should be plt.set_xscale()

B. The scale argument should be 'log', not 'logarithmic'

C. plt.plot() cannot be used with log scale

D. Missing plt.show() call

Solution

1. Step 1: Check method and argument correctness

   The method plt.xscale() is correct, but the argument must be 'log' for logarithmic scale.

2. Step 2: Identify the wrong argument

   The code uses 'logarithmic' which is invalid and causes an error.

3. Final Answer:

   The scale argument should be 'log', not 'logarithmic' -> Option B

4. Quick Check:

   Use 'log' as argument in plt.xscale() [OK]

Hint: Use 'log' exactly as argument for log scale [OK]

Common Mistakes:

• Using 'logarithmic' instead of 'log'
• Thinking plt.set_xscale() is needed
• Believing plt.plot() can't work with log scale

5. You have data ranging from 0.001 to 1000. Which axis scale setting will best show all data clearly on a plot?

hard

A. Use linear scale on both axes

B. Use linear scale on x-axis and log scale on y-axis

C. Use log scale on both axes

D. Use log scale on x-axis and linear scale on y-axis

Solution

1. Step 1: Analyze data range

   The data spans from 0.001 to 1000, a wide range over several orders of magnitude.

2. Step 2: Choose scale to display wide range clearly

   Log scale compresses large ranges and shows small and large values clearly on the same plot.

3. Step 3: Decide axis scales

   Using log scale on both axes best displays wide-ranging data without distortion.

4. Final Answer:

   Use log scale on both axes -> Option C

5. Quick Check:

   Wide range data = log scale both axes [OK]

Hint: Wide range data needs log scale on both axes [OK]

Common Mistakes:

• Using linear scale hides small values
• Mixing linear and log scales without reason
• Assuming linear scale works for all ranges