Axis scales (linear, log) in Matplotlib - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does a linear axis scale represent in a plot?

A linear axis scale shows data points spaced evenly according to their actual values. For example, the distance between 1 and 2 is the same as between 4 and 5.

beginner

What is a logarithmic (log) axis scale?

A log axis scale spaces data points based on the logarithm of their values. This means equal distances represent equal ratios, not equal differences. It helps show data that changes exponentially.

beginner

How do you set a logarithmic scale on the y-axis in matplotlib?

Use the command plt.yscale('log') after plotting your data to change the y-axis to a logarithmic scale.

intermediate

Why might you choose a log scale over a linear scale?

You choose a log scale when data covers a wide range or grows exponentially. It makes it easier to see patterns and compare values that differ by large amounts.

intermediate

What happens if you try to use a log scale with zero or negative values?

Log scales cannot display zero or negative values because logarithms of these numbers are undefined. You must remove or adjust such data before using a log scale.

Which matplotlib command sets the x-axis to a logarithmic scale?

A plt.yscale('log')

B plt.xscale('log')

C plt.axis('log')

D plt.scale('log')

Use plt.xscale('log') to set the x-axis to logarithmic scale.

What kind of data is best visualized with a log scale?

A Data that grows exponentially or covers many orders of magnitude

B Data with negative values

C Data with only zero values

D Data with small range and linear growth

Log scales help visualize data that grows exponentially or spans large ranges.

What will happen if you plot zero values on a log scale axis?

A They will appear at zero

B They will be shown as negative values

C They will cause an error or not display

D They will be converted to one

Logarithms of zero are undefined, so zero values cause errors or are not shown on log scales.

Which axis scale spaces values evenly by their actual difference?

A Linear scale

B Logarithmic scale

C Exponential scale

D Categorical scale

Linear scales space values evenly by their actual difference.

How do you switch back to a linear scale on the y-axis in matplotlib?

A plt.scale('linear')

B plt.yaxis('linear')

C plt.axis('linear')

D plt.yscale('linear')

Use plt.yscale('linear') to set the y-axis back to linear scale.

Explain the difference between linear and logarithmic axis scales and when to use each.

Think about how distances between numbers appear on each scale.

Describe how to set a logarithmic scale on an axis using matplotlib and what to watch out for.

Recall the matplotlib commands and data requirements.

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