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

Need a 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

Need a 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

Need a 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

Need a hint?

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