When to use Seaborn vs Matplotlib - Hands-On Comparison

When to use Seaborn vs Matplotlib

📖 Scenario: You are a data analyst working with sales data. You want to create charts to understand your data better. You have two popular tools: Matplotlib and Seaborn. Each tool is good for different tasks.

🎯 Goal: Learn when to use Matplotlib and when to use Seaborn by creating simple charts with both libraries using the same data.

📋 What You'll Learn

Create a simple sales data dictionary

Set a threshold value for sales

Use Matplotlib to plot a basic bar chart

Use Seaborn to plot a styled bar chart

Print a message explaining which library is better for which task

💡 Why This Matters

🌍 Real World

Data analysts often need to choose the right plotting tool to communicate insights clearly and attractively.

💼 Career

Knowing when to use Matplotlib or Seaborn helps create effective visual reports and dashboards in data science roles.

1

Create sales data dictionary

Create a dictionary called sales_data with these exact entries: 'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 180.

Matplotlib

# Create the sales_data dictionary with the given months and sales
# Your code here

Need a hint?

Use curly braces {} to create a dictionary with month names as keys and sales numbers as values.

2

Set sales threshold

Create a variable called threshold and set it to 180.

Matplotlib

sales_data = {'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 180}
# Set the threshold variable to 180
# Your code here

Need a hint?

Just assign the number 180 to a variable named threshold.

3

Plot bar chart with Matplotlib

Import matplotlib.pyplot as plt. Use plt.bar() with sales_data.keys() and sales_data.values() to create a bar chart. Then call plt.show() to display it.

Matplotlib

sales_data = {'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 180}
threshold = 180
# Import matplotlib.pyplot as plt
# Create a bar chart using plt.bar with sales_data keys and values
# Show the plot
# Your code here

Need a hint?

Use plt.bar() to create the bar chart and plt.show() to display it.

4

Plot styled bar chart with Seaborn and explain usage

Import seaborn as sns and pandas as pd. Convert sales_data to a DataFrame with columns 'Month' and 'Sales'. Use sns.barplot() to plot the sales data. Call plt.show() to display the plot. Then print the exact message: "Use Matplotlib for simple plots and Seaborn for styled, statistical plots."

Matplotlib

import matplotlib.pyplot as plt

sales_data = {'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 180}
threshold = 180

plt.bar(sales_data.keys(), sales_data.values())
plt.title('Monthly Sales - Matplotlib')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.show()

# Import seaborn as sns and pandas as pd
# Convert sales_data to a DataFrame with columns 'Month' and 'Sales'
# Use sns.barplot to plot the data
# Show the plot
# Print the message about when to use Matplotlib vs Seaborn
# Your code here

Need a hint?

Use pd.DataFrame() to convert the dictionary to a table. Use sns.barplot() to create a nicer bar chart. Finally, print the message exactly as given.