Shared axes between subplots in Matplotlib - Mini Project: Build & Apply

Shared axes between subplots

📖 Scenario: You are analyzing sales data for two different products over a week. You want to compare their daily sales side by side using charts that share the same vertical scale to make comparison easier.

🎯 Goal: Create two line charts side by side using matplotlib that share the same y-axis. This will help you visually compare the sales trends of the two products on the same scale.

📋 What You'll Learn

Create a dictionary called sales_data with daily sales for two products.

Create a variable called days listing the days of the week.

Create two subplots side by side that share the y-axis.

Plot sales for each product on its own subplot.

Add titles to each subplot and label the x-axis with days.

💡 Why This Matters

🌍 Real World

Sharing axes between subplots is useful when comparing related data sets visually, such as sales trends, temperature changes, or stock prices over time.

💼 Career

Data scientists and analysts often create shared-axis plots to make comparisons clearer and to present data insights effectively to stakeholders.

1

Create the sales data and days list

Create a dictionary called sales_data with these exact entries: 'Product A': [10, 15, 13, 17, 20, 22, 19] and 'Product B': [12, 14, 16, 15, 18, 21, 20]. Also create a list called days with these exact values: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'].

Matplotlib

# Create sales_data dictionary and days list
# Your code here

Need a hint?

Use a dictionary for sales_data with two keys and lists of numbers as values. Use a list for days.

2

Create subplots with shared y-axis

Import matplotlib.pyplot as plt. Then create two subplots side by side using plt.subplots with nrows=1, ncols=2, and sharey=True. Store the result in variables fig and axes.

Matplotlib

sales_data = {'Product A': [10, 15, 13, 17, 20, 22, 19], 'Product B': [12, 14, 16, 15, 18, 21, 20]}
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
# Import matplotlib.pyplot as plt and create two subplots side by side sharing y-axis
# Your code here

Need a hint?

Use plt.subplots(nrows=1, ncols=2, sharey=True) to create the subplots.

3

Plot sales data on each subplot

Use axes[0].plot(days, sales_data['Product A']) to plot Product A sales on the first subplot. Use axes[1].plot(days, sales_data['Product B']) to plot Product B sales on the second subplot. Add titles to each subplot with axes[0].set_title('Product A Sales') and axes[1].set_title('Product B Sales'). Label the x-axis on both subplots with axes[0].set_xlabel('Day') and axes[1].set_xlabel('Day').

Matplotlib

import matplotlib.pyplot as plt

sales_data = {'Product A': [10, 15, 13, 17, 20, 22, 19], 'Product B': [12, 14, 16, 15, 18, 21, 20]}
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

fig, axes = plt.subplots(nrows=1, ncols=2, sharey=True)
# Plot sales for Product A on axes[0] and Product B on axes[1]
# Add titles and x-axis labels
# Your code here

Need a hint?

Use axes[0] and axes[1] to plot and label each subplot separately.

4

Display the plot

Use plt.show() to display the figure with the two subplots.

Matplotlib

import matplotlib.pyplot as plt

sales_data = {'Product A': [10, 15, 13, 17, 20, 22, 19], 'Product B': [12, 14, 16, 15, 18, 21, 20]}
days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

fig, axes = plt.subplots(nrows=1, ncols=2, sharey=True)

axes[0].plot(days, sales_data['Product A'])
axes[0].set_title('Product A Sales')
axes[0].set_xlabel('Day')

axes[1].plot(days, sales_data['Product B'])
axes[1].set_title('Product B Sales')
axes[1].set_xlabel('Day')
# Display the plot
# Your code here

Need a hint?

Call plt.show() to display the figure.