Secondary axes in Matplotlib - Mini Project: Build & Apply

Plotting with Secondary Axes in Matplotlib

📖 Scenario: You are analyzing the sales and profit data of a small store. You want to visualize both sales and profit trends on the same plot but with different scales.

🎯 Goal: Create a line plot with sales on the primary y-axis and profit on a secondary y-axis using Matplotlib.

📋 What You'll Learn

Create a dictionary called data with keys 'Month', 'Sales', and 'Profit' and the exact values provided.

Create a variable called months that holds the list of months from the data dictionary.

Create a Matplotlib plot with sales data on the primary y-axis and profit data on a secondary y-axis.

Label both y-axes and the x-axis correctly.

Display the plot.

💡 Why This Matters

🌍 Real World

Visualizing two related but differently scaled data series on the same plot helps businesses understand relationships like sales and profit trends over time.

💼 Career

Data analysts and scientists often use secondary axes in reports and dashboards to present multiple metrics clearly and effectively.

1

Create the data dictionary

Create a dictionary called data with these exact entries: 'Month' as ["Jan", "Feb", "Mar", "Apr", "May"], 'Sales' as [250, 300, 280, 350, 400], and 'Profit' as [50, 60, 55, 70, 80].

Matplotlib

# Create the data dictionary with months, sales, and profit
# Your code here

Hint

Use a dictionary with keys 'Month', 'Sales', and 'Profit'. Assign the exact lists as values.

2

Extract the months list

Create a variable called months that holds the list of months from the data dictionary using the key 'Month'.

Matplotlib

data = {
    'Month': ["Jan", "Feb", "Mar", "Apr", "May"],
    'Sales': [250, 300, 280, 350, 400],
    'Profit': [50, 60, 55, 70, 80]
}
# Extract the months list from data
# Your code here

Hint

Use months = data['Month'] to get the list of months.

3

Create the plot with secondary y-axis

Import matplotlib.pyplot as plt. Create a figure and axis using plt.subplots(). Plot data['Sales'] against months on the primary y-axis with label 'Sales'. Create a secondary y-axis using ax.twinx(). Plot data['Profit'] against months on the secondary y-axis with label 'Profit'. Label the x-axis as 'Month'.

Matplotlib

import matplotlib.pyplot as plt

data = {
    'Month': ["Jan", "Feb", "Mar", "Apr", "May"],
    'Sales': [250, 300, 280, 350, 400],
    'Profit': [50, 60, 55, 70, 80]
}

months = data['Month']

# Create figure and axis
# Plot sales on primary y-axis
# Create secondary y-axis and plot profit
# Label x-axis
# Your code here

Hint

Use plt.subplots() to create the plot. Use ax.twinx() to create the secondary y-axis.

4

Display the plot

Add a plt.show() statement to display the plot.

Matplotlib

import matplotlib.pyplot as plt

data = {
    'Month': ["Jan", "Feb", "Mar", "Apr", "May"],
    'Sales': [250, 300, 280, 350, 400],
    'Profit': [50, 60, 55, 70, 80]
}

months = data['Month']

fig, ax = plt.subplots()
ax.plot(months, data['Sales'], color='blue', label='Sales')
ax.set_xlabel('Month')
ax.set_ylabel('Sales', color='blue')

ax2 = ax.twinx()
ax2.plot(months, data['Profit'], color='green', label='Profit')
ax2.set_ylabel('Profit', color='green')

# Display the plot
# Your code here

Hint

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

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'