Spine charts concept in Matplotlib - Mini Project: Build & Apply

Create a Spine Chart with Matplotlib

📖 Scenario: You work as a data analyst for a company that wants to visualize the sales performance of different products over several months. Your manager asks you to create a spine chart to compare the sales trends clearly.

🎯 Goal: Build a spine chart using matplotlib to show sales data for three products over four months. You will create the data, configure the plot, draw the spine chart, and display the result.

📋 What You'll Learn

Create a dictionary called sales_data with sales numbers for three products over four months.

Create a list called months with the names of the four months.

Use matplotlib to plot a spine chart comparing the sales trends.

Label the x-axis with the months and add a legend for the products.

Display the final spine chart.

💡 Why This Matters

🌍 Real World

Spine charts help compare multiple categories over time or other continuous variables, making trends easy to spot for business decisions.

💼 Career

Data analysts and scientists often create spine charts to visualize and communicate trends clearly to stakeholders.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Product A': [150, 200, 250, 300], 'Product B': [180, 210, 230, 280], and 'Product C': [100, 150, 200, 250].

Matplotlib

# Create the sales_data dictionary with exact values
# Your code here

Need a hint?

Use curly braces to create a dictionary. Each product name is a key, and the sales numbers are lists as values.

2

Create the months list

Create a list called months with these exact strings: 'Jan', 'Feb', 'Mar', and 'Apr'.

Matplotlib

sales_data = {
    'Product A': [150, 200, 250, 300],
    'Product B': [180, 210, 230, 280],
    'Product C': [100, 150, 200, 250]
}
# Create the months list with exact month names
# Your code here

Need a hint?

Use square brackets to create a list with the exact month names as strings.

3

Plot the spine chart

Import matplotlib.pyplot as plt. Use a for loop with variables product and sales to iterate over sales_data.items(). Inside the loop, plot the sales data using plt.plot(months, sales, marker='o', label=product). Then add a legend with plt.legend().

Matplotlib

sales_data = {
    'Product A': [150, 200, 250, 300],
    'Product B': [180, 210, 230, 280],
    'Product C': [100, 150, 200, 250]
}

months = ['Jan', 'Feb', 'Mar', 'Apr']

# Import matplotlib.pyplot as plt
# Use a for loop with product, sales in sales_data.items()
# Plot each product's sales with plt.plot and add a legend
# Your code here

Need a hint?

Remember to import matplotlib.pyplot as plt first. Use a for loop to plot each product's sales with markers and labels.

4

Display the spine chart

Add plt.xlabel('Months') and plt.ylabel('Sales') to label the axes. Then use plt.title('Sales Trends Spine Chart') to add a title. Finally, display the plot with plt.show().

Matplotlib

import matplotlib.pyplot as plt

sales_data = {
    'Product A': [150, 200, 250, 300],
    'Product B': [180, 210, 230, 280],
    'Product C': [100, 150, 200, 250]
}

months = ['Jan', 'Feb', 'Mar', 'Apr']

for product, sales in sales_data.items():
    plt.plot(months, sales, marker='o', label=product)

plt.legend()

# Add x-axis label, y-axis label, title, and show the plot
# Your code here

Need a hint?

Use plt.xlabel, plt.ylabel, and plt.title to add labels and title. Use plt.show() to display the chart.