Dual y-axis for different scales in Matplotlib - Mini Project: Build & Apply

Dual y-axis for different scales

📖 Scenario: You are analyzing sales and profit data for a small store. Sales are in thousands of dollars, and profit is in hundreds of dollars. You want to show both on the same chart but with different y-axis scales.

🎯 Goal: Create a line chart with two y-axes: one for sales and one for profit, so both can be seen clearly despite different scales.

📋 What You'll Learn

Create lists for sales and profit data with exact values

Create a list for months from January to June

Create a plot with two y-axes using matplotlib

Plot sales on the left y-axis and profit on the right y-axis

Label both y-axes and the x-axis

Add a legend to distinguish sales and profit lines

💡 Why This Matters

🌍 Real World

Dual y-axis charts are useful when comparing two related data sets with different units or scales, like sales and profit, temperature and humidity, or stock price and volume.

💼 Career

Data analysts and scientists often need to visualize multiple metrics together to find insights. Knowing how to create dual y-axis plots helps communicate complex data clearly.

1

Create the data lists

Create a list called months with these exact values: 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'. Create a list called sales with these exact values: 20, 35, 30, 35, 27, 40. Create a list called profit with these exact values: 4, 7, 6, 8, 5, 9.

Matplotlib

# Create the lists months, sales, and profit with the exact values
# Your code here

Hint

Use square brackets to create lists. Separate items with commas.

2

Set up the plot and first y-axis

Import matplotlib.pyplot as plt. Create a figure and axis using fig, ax1 = plt.subplots(). Plot sales against months on ax1 with a blue line and label it 'Sales'. Set the x-axis label to 'Month' and the y-axis label of ax1 to 'Sales (thousands)'.

Matplotlib

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [20, 35, 30, 35, 27, 40]
profit = [4, 7, 6, 8, 5, 9]

# Import matplotlib.pyplot as plt
# Create fig and ax1 with plt.subplots()
# Plot sales on ax1 with blue line and label 'Sales'
# Set x-axis label to 'Month'
# Set y-axis label of ax1 to 'Sales (thousands)'
# Your code here

Hint

Use plt.subplots() to create the plot. Use ax1.plot() to plot sales.

3

Add the second y-axis and plot profit

Create a second y-axis ax2 using ax1.twinx(). Plot profit against months on ax2 with a red line and label it 'Profit'. Set the y-axis label of ax2 to 'Profit (hundreds)' with red color. Add legends for both ax1 and ax2.

Matplotlib

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [20, 35, 30, 35, 27, 40]
profit = [4, 7, 6, 8, 5, 9]

fig, ax1 = plt.subplots()
ax1.plot(months, sales, 'b-', label='Sales')
ax1.set_xlabel('Month')
ax1.set_ylabel('Sales (thousands)', color='b')

# Create ax2 as a twin of ax1
# Plot profit on ax2 with red line and label 'Profit'
# Set y-axis label of ax2 to 'Profit (hundreds)' with red color
# Add legends for ax1 and ax2
# Your code here

Hint

Use ax1.twinx() to create the second y-axis. Use different colors for clarity.

4

Display the plot

Use plt.show() to display the plot with dual y-axes.

Matplotlib

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [20, 35, 30, 35, 27, 40]
profit = [4, 7, 6, 8, 5, 9]

fig, ax1 = plt.subplots()
ax1.plot(months, sales, 'b-', label='Sales')
ax1.set_xlabel('Month')
ax1.set_ylabel('Sales (thousands)', color='b')

ax2 = ax1.twinx()
ax2.plot(months, profit, 'r-', label='Profit')
ax2.set_ylabel('Profit (hundreds)', color='r')

ax1.legend(loc='upper left')
ax2.legend(loc='upper right')

# Display the plot
# Your code here

Hint

Use plt.show() to see the plot window.

Practice

1. What is the main purpose of using a dual y-axis in a matplotlib plot?

easy

A. To compare two datasets with different scales on the same plot

B. To create two separate plots side by side

C. To add a legend to the plot

D. To change the color of the plot lines

Solution

1. Step 1: Understand the purpose of dual y-axis

   A dual y-axis allows plotting two datasets with different value ranges on the same graph for easy comparison.

2. Step 2: Identify the correct use case

   Comparing datasets with different scales requires two y-axes; other options do not describe this purpose.

3. Final Answer:

   To compare two datasets with different scales on the same plot -> Option A

4. Quick Check:

   Dual y-axis = compare different scales [OK]

Hint: Dual y-axis means two y-scales on one graph [OK]

Common Mistakes:

• Thinking dual y-axis creates two separate plots
• Confusing dual y-axis with adding legends
• Assuming it changes line colors only

2. Which matplotlib function is used to create a second y-axis sharing the same x-axis?

easy

A. plt.subplots()

B. ax.twiny()

C. ax.twinx()

D. plt.plot()

Solution

1. Step 1: Recall matplotlib methods for axes

   The method twinx() creates a second y-axis sharing the x-axis.

2. Step 2: Differentiate from similar methods

   twiny() creates a second x-axis, not y-axis; subplots() creates figure and axes; plot() draws lines.

3. Final Answer:

   ax.twinx() -> Option C

4. Quick Check:

   Second y-axis = twinx() [OK]

Hint: Use ax.twinx() for second y-axis, twiny() is for x-axis [OK]

Common Mistakes:

• Using plt.subplots() to create second y-axis
• Confusing twinx() with twiny()
• Trying to use plt.plot() to add axes

3. What will the following code output?

import matplotlib.pyplot as plt
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
ax1.plot([1, 2, 3], [10, 20, 30], 'g-')
ax2.plot([1, 2, 3], [100, 200, 300], 'b-')
print(len(fig.axes))

medium

A. 2

B. 1

C. 3

D. 4

Solution

1. Step 1: Understand axes creation

   plt.subplots() creates one axes (ax1), then ax1.twinx() adds a second y-axis (ax2), so total axes are 2.

2. Step 2: Check figure axes count

   fig.axes is a list of all axes in the figure; after adding ax2, length is 2.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Two axes after twinx() = 2 [OK]

Hint: twinx() adds one more axes, so total axes count increases by 1 [OK]

Common Mistakes:

• Assuming twinx() replaces the original axes
• Thinking twinx() adds multiple axes
• Confusing axes count with number of plots

4. Identify the error in this code snippet for creating dual y-axis:

import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax2 = plt.twinx()
ax.plot([1,2,3], [10,20,30])
ax2.plot([1,2,3], [100,200,300])
plt.show()

medium

A. plt.show() is missing

B. plt.subplots() should be plt.figure()

C. ax2.plot() should be ax.plot()

D. plt.twinx() should be ax.twinx()

Solution

1. Step 1: Check method usage for second y-axis

   twinx() is a method of an axes object, not plt module. So plt.twinx() is invalid.

2. Step 2: Correct method call

   Use ax.twinx() to create the second y-axis sharing the x-axis.

3. Final Answer:

   plt.twinx() should be ax.twinx() -> Option D

4. Quick Check:

   twinx() is an axes method, not plt function [OK]

Hint: twinx() must be called on axes object, not plt [OK]

Common Mistakes:

• Calling twinx() on plt instead of axes
• Confusing figure and axes creation methods
• Missing plt.show() (though present here)

5. You want to plot temperature (°C) and rainfall (mm) over months on the same plot with dual y-axes. Which of these code snippets correctly sets different y-axis labels and colors for clarity?

hard

A. ax1.set_xlabel('Temperature (°C)', color='red')\nax2.set_xlabel('Rainfall (mm)', color='blue')

B. ax1.set_ylabel('Temperature (°C)', color='red')\nax2.set_ylabel('Rainfall (mm)', color='blue')

C. ax1.set_ylabel('Rainfall (mm)', color='blue')\nax2.set_ylabel('Temperature (°C)', color='red')

D. ax1.set_title('Temperature and Rainfall')\nax2.set_title('Temperature and Rainfall')

Solution

1. Step 1: Assign correct y-axis labels

   Temperature and rainfall are y-axis data, so use set_ylabel() on each axis with matching labels.

2. Step 2: Use colors to distinguish axes

   Setting label colors helps visually separate the two y-axes for clarity.

3. Final Answer:

   ax1.set_ylabel('Temperature (°C)', color='red')\nax2.set_ylabel('Rainfall (mm)', color='blue') -> Option B

4. Quick Check:

   Correct y-labels and colors = ax1.set_ylabel('Temperature (°C)', color='red')\nax2.set_ylabel('Rainfall (mm)', color='blue') [OK]

Hint: Label y-axes with units and colors for clear dual-axis plots [OK]

Common Mistakes:

• Setting x-axis labels instead of y-axis
• Swapping labels between axes
• Using titles instead of axis labels