Axis scales (linear, log) in Matplotlib - Mini Project: Build & Apply

Axis scales (linear, log) with matplotlib

📖 Scenario: You are analyzing sales data that grows exponentially. You want to see how the sales look on a normal scale and on a logarithmic scale to better understand the growth pattern.

🎯 Goal: Create a simple line plot of sales data using matplotlib. First, plot the data with a linear scale on the y-axis. Then, change the y-axis scale to logarithmic to compare the views.

📋 What You'll Learn

Create a list called sales with the values: 10, 100, 1000, 10000, 100000

Create a list called months with the values: 1, 2, 3, 4, 5

Create a variable called y_scale and set it to the string 'linear'

Use matplotlib to plot months vs sales with the y-axis scale set to y_scale

Change the value of y_scale to 'log' and plot the same data again

Print the plots so the output shows both linear and logarithmic scale plots

💡 Why This Matters

🌍 Real World

Scientists and business analysts often use logarithmic scales to better understand data that grows exponentially or covers a wide range.

💼 Career

Knowing how to change axis scales in plots is important for data visualization roles and helps communicate insights clearly.

1

Create the sales and months data lists

Create a list called sales with the values 10, 100, 1000, 10000, and 100000. Also create a list called months with the values 1, 2, 3, 4, and 5.

Matplotlib

# Create the sales and months lists
# Your code here

Hint

Use square brackets [] to create lists with the exact numbers given.

2

Set the initial y-axis scale to linear

Create a variable called y_scale and set it to the string 'linear'.

Matplotlib

sales = [10, 100, 1000, 10000, 100000]
months = [1, 2, 3, 4, 5]

# Set y_scale to 'linear'
# Your code here

Hint

Assign the string 'linear' to the variable y_scale.

3

Plot the sales data with the y-axis scale set to y_scale

Import matplotlib.pyplot as plt. Use plt.plot() to plot months on the x-axis and sales on the y-axis. Then set the y-axis scale using plt.yscale(y_scale). Finally, add a title 'Sales Data with Linear Scale' and call plt.show() to display the plot.

Matplotlib

sales = [10, 100, 1000, 10000, 100000]
months = [1, 2, 3, 4, 5]
y_scale = 'linear'

# Import matplotlib.pyplot as plt
# Plot months vs sales
# Set y-axis scale to y_scale
# Add title 'Sales Data with Linear Scale'
# Show the plot
# Your code here

Hint

Remember to import matplotlib.pyplot as plt. Use plt.plot() and plt.yscale() to set the scale.

4

Change y_scale to 'log' and plot again

Change the value of y_scale to the string 'log'. Then plot months vs sales again using plt.plot(), set the y-axis scale with plt.yscale(y_scale), add the title 'Sales Data with Logarithmic Scale', and call plt.show() to display the plot.

Matplotlib

import matplotlib.pyplot as plt

sales = [10, 100, 1000, 10000, 100000]
months = [1, 2, 3, 4, 5]
y_scale = 'linear'

plt.plot(months, sales)
plt.yscale(y_scale)
plt.title('Sales Data with Linear Scale')
plt.show()

# Change y_scale to 'log'
# Plot months vs sales again
# Set y-axis scale to y_scale
# Add title 'Sales Data with Logarithmic Scale'
# Show the plot
# Your code here

Hint

Assign 'log' to y_scale and repeat the plotting steps with the new scale.

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