Combining Seaborn and Matplotlib - Mini Project: Build & Apply

Combining Seaborn and Matplotlib

📖 Scenario: You are analyzing sales data for a small store. You want to visualize the sales amounts for different products using a bar chart. You will use Seaborn to create the bar chart and Matplotlib to add a title and labels.

🎯 Goal: Create a bar chart using Seaborn to show sales amounts for products, then use Matplotlib to add a title and axis labels.

📋 What You'll Learn

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

Create a list called products containing the product names from sales_data.

Create a list called sales containing the sales amounts from sales_data.

Use Seaborn's barplot function with products and sales to create the bar chart.

Use Matplotlib's plt.title, plt.xlabel, and plt.ylabel to add a title and axis labels.

Use plt.show() to display the final plot.

💡 Why This Matters

🌍 Real World

Combining Seaborn and Matplotlib is common when you want to create attractive plots with Seaborn and customize them further with Matplotlib's features.

💼 Career

Data scientists and analysts often need to create clear visualizations for reports and presentations, using multiple libraries together to get the best results.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Apples': 50, 'Bananas': 30, 'Cherries': 20, 'Dates': 15, 'Elderberries': 10.

Matplotlib

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

Need a hint?

Use curly braces {} to create a dictionary with the given keys and values.

2

Create lists for products and sales

Create a list called products containing the keys from sales_data. Create a list called sales containing the values from sales_data.

Matplotlib

sales_data = {'Apples': 50, 'Bananas': 30, 'Cherries': 20, 'Dates': 15, 'Elderberries': 10}
# Create the products list from sales_data keys
# Create the sales list from sales_data values
# Your code here

Need a hint?

Use list() with sales_data.keys() and sales_data.values() to create the lists.

3

Create the bar chart with Seaborn

Import seaborn as sns and matplotlib.pyplot as plt. Use sns.barplot with x=products and y=sales to create the bar chart.

Matplotlib

sales_data = {'Apples': 50, 'Bananas': 30, 'Cherries': 20, 'Dates': 15, 'Elderberries': 10}
products = list(sales_data.keys())
sales = list(sales_data.values())

# Import seaborn as sns and matplotlib.pyplot as plt
# Create a barplot with x=products and y=sales
# Your code here

Need a hint?

Use import seaborn as sns and import matplotlib.pyplot as plt. Then call sns.barplot(x=products, y=sales).

4

Add title and labels with Matplotlib and show plot

Use plt.title to add the title 'Sales Amounts by Product'. Use plt.xlabel with 'Product' and plt.ylabel with 'Sales'. Finally, use plt.show() to display the plot.

Matplotlib

import seaborn as sns
import matplotlib.pyplot as plt

sales_data = {'Apples': 50, 'Bananas': 30, 'Cherries': 20, 'Dates': 15, 'Elderberries': 10}
products = list(sales_data.keys())
sales = list(sales_data.values())

sns.barplot(x=products, y=sales)

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

Need a hint?

Use plt.title('Sales Amounts by Product'), plt.xlabel('Product'), plt.ylabel('Sales'), and plt.show().