Dashboard layout patterns in Matplotlib - Mini Project: Build & Apply

Dashboard layout patterns

📖 Scenario: You work as a data analyst for a small company. Your manager wants a simple dashboard to see sales data and customer feedback side by side. You will create a dashboard layout using matplotlib to show two charts in one window.

🎯 Goal: Create a dashboard layout with two charts side by side using matplotlib. The first chart will show sales data as a bar chart. The second chart will show customer feedback scores as a line chart.

📋 What You'll Learn

Create a dictionary called sales_data with product names as keys and sales numbers as values

Create a list called feedback_scores with 5 numeric values representing customer feedback

Use matplotlib.pyplot.subplots() to create a figure with two side-by-side plots

Plot a bar chart of sales_data on the left subplot

Plot a line chart of feedback_scores on the right subplot

Add titles to each subplot: 'Sales Data' and 'Customer Feedback'

Display the dashboard using plt.show()

💡 Why This Matters

🌍 Real World

Dashboards are used in many companies to show important data clearly and quickly. This project shows how to arrange multiple charts in one window for easy comparison.

💼 Career

Data analysts and scientists often create dashboards to communicate insights to managers and teams. Knowing how to layout multiple charts is a key skill.

1

Create sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Apples': 30, 'Bananas': 45, 'Cherries': 15, 'Dates': 10, 'Elderberries': 5.

Matplotlib

# Create the sales_data dictionary with product names and sales numbers
# Your code here

Need a hint?

Use curly braces {} to create a dictionary. Separate keys and values with colons.

2

Create customer feedback scores list

Create a list called feedback_scores with these exact numeric values: 4, 5, 3, 4, 5.

Matplotlib

sales_data = {'Apples': 30, 'Bananas': 45, 'Cherries': 15, 'Dates': 10, 'Elderberries': 5}
# Create the feedback_scores list with 5 numeric values
# Your code here

Need a hint?

Use square brackets [] to create a list of numbers separated by commas.

3

Create dashboard layout with two subplots

Import matplotlib.pyplot as plt. Use plt.subplots() to create a figure and two side-by-side axes stored in variables fig and axes. Then plot a bar chart of sales_data on axes[0] and a line chart of feedback_scores on axes[1]. Add the titles 'Sales Data' and 'Customer Feedback' to the respective subplots.

Matplotlib

sales_data = {'Apples': 30, 'Bananas': 45, 'Cherries': 15, 'Dates': 10, 'Elderberries': 5}
feedback_scores = [4, 5, 3, 4, 5]
# Import matplotlib.pyplot as plt
# Create figure and axes with plt.subplots() for 1 row and 2 columns
# Plot bar chart on axes[0] using sales_data keys and values
# Plot line chart on axes[1] using feedback_scores
# Add titles to both subplots
# Your code here

Need a hint?

Use plt.subplots(1, 2) to create two plots side by side. Use axes[0].bar() for bar chart and axes[1].plot() for line chart. Use set_title() to add titles.

4

Display the dashboard

Add a line to display the dashboard window using plt.show().

Matplotlib

import matplotlib.pyplot as plt

sales_data = {'Apples': 30, 'Bananas': 45, 'Cherries': 15, 'Dates': 10, 'Elderberries': 5}
feedback_scores = [4, 5, 3, 4, 5]

fig, axes = plt.subplots(1, 2, figsize=(10, 4))

axes[0].bar(sales_data.keys(), sales_data.values())
axes[0].set_title('Sales Data')

axes[1].plot(feedback_scores, marker='o')
axes[1].set_title('Customer Feedback')
# Display the dashboard window
# Your code here

Need a hint?

Use plt.show() to open the window with your dashboard charts.