Why Seaborn complements Matplotlib - See It in Action

Why Seaborn complements Matplotlib

📖 Scenario: You are a data analyst working with sales data. You want to create clear and attractive charts to understand sales trends better. You know Matplotlib is powerful but sometimes looks plain. You heard Seaborn can help make charts easier and prettier.

🎯 Goal: Build a simple line chart using Matplotlib, then enhance it with Seaborn to see how Seaborn complements Matplotlib by improving style and ease of use.

📋 What You'll Learn

Create a list of sales numbers for 7 days

Use Matplotlib to plot the sales data as a line chart

Add a Seaborn style to improve the chart's look

Plot the same sales data again with Seaborn's style applied

Print or show both charts to compare

💡 Why This Matters

🌍 Real World

Data analysts and scientists often need to create clear and attractive charts to communicate insights effectively.

💼 Career

Knowing how to use Matplotlib and Seaborn together helps create professional visualizations for reports and presentations.

1

Create sales data list

Create a list called sales with these exact values: [150, 200, 250, 300, 280, 320, 400]

Matplotlib

# Create the sales list with exact values
# Your code here

Need a hint?

Use square brackets and separate numbers with commas.

2

Import Matplotlib and plot sales

Import matplotlib.pyplot as plt. Use plt.plot(sales) to create a line chart of the sales list.

Matplotlib

sales = [150, 200, 250, 300, 280, 320, 400]
# Import matplotlib.pyplot as plt
# Plot sales using plt.plot(sales)
# Your code here

Need a hint?

Use import matplotlib.pyplot as plt and then plt.plot(sales).

3

Add Seaborn style to improve chart look

Import seaborn as sns. Use sns.set() before plotting to apply Seaborn's style. Then plot the sales list again using plt.plot(sales).

Matplotlib

sales = [150, 200, 250, 300, 280, 320, 400]
import matplotlib.pyplot as plt
plt.plot(sales)
# Import seaborn as sns
# Use sns.set() to apply style
# Plot sales again with plt.plot(sales)
# Your code here

Need a hint?

Import seaborn and call sns.set() before plotting.

4

Show the plot to compare styles

Use plt.show() to display the chart with Seaborn style applied.

Matplotlib

sales = [150, 200, 250, 300, 280, 320, 400]
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
plt.plot(sales)
# Show the plot
# Your code here

Need a hint?

Call plt.show() to display the chart window.