Customizing Seaborn plots with Matplotlib - Mini Project: Build & Apply

Customizing Seaborn plots with Matplotlib

📖 Scenario: You are analyzing sales data for a small store. You want to visualize the monthly sales using a bar plot. You will use Seaborn to create the plot and then customize it using Matplotlib to make it clearer and more attractive.

🎯 Goal: Create a Seaborn bar plot of monthly sales and customize it using Matplotlib by adding a title, axis labels, and changing the color of the bars.

📋 What You'll Learn

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

Create a list called highlight_months containing months to highlight

Use Seaborn to create a bar plot of the sales data

Use Matplotlib to add a title and axis labels

Change the color of bars for the months in highlight_months to a different color

💡 Why This Matters

🌍 Real World

Visualizing sales data helps businesses understand trends and make decisions. Customizing plots makes the information clearer and more appealing to stakeholders.

💼 Career

Data analysts and scientists often use Seaborn and Matplotlib to create and customize visualizations for reports and presentations.

1

DATA SETUP: Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 190.

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 the given month names as keys and sales numbers as values.

2

CONFIGURATION: Define months to highlight

Create a list called highlight_months containing the months 'February' and 'April' to highlight in the plot.

Matplotlib

sales_data = {'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 190}
# Create the highlight_months list with 'February' and 'April'
# Your code here

Need a hint?

Use square brackets [] to create a list with the two month names as strings.

3

CORE LOGIC: Create and customize the Seaborn bar plot

Import seaborn as sns and matplotlib.pyplot as plt. Create a bar plot using sns.barplot with months on the x-axis and sales on the y-axis from sales_data. Use a list comprehension to set the color of bars to 'orange' if the month is in highlight_months, otherwise 'skyblue'. Then add a title 'Monthly Sales' and axis labels 'Month' and 'Sales' using Matplotlib.

Matplotlib

sales_data = {'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 190}
highlight_months = ['February', 'April']
# Import seaborn and matplotlib.pyplot
# Create the bar plot with custom colors
# Add title and axis labels
# Your code here

Need a hint?

Use list() to get keys and values from the dictionary. Use a list comprehension to create the colors list. Pass palette=colors to sns.barplot. Use plt.title, plt.xlabel, and plt.ylabel to add text.

4

OUTPUT: Display the customized plot

Use plt.show() to display the plot with the custom colors, title, and axis labels.

Matplotlib

import seaborn as sns
import matplotlib.pyplot as plt

sales_data = {'January': 150, 'February': 200, 'March': 170, 'April': 220, 'May': 190}
highlight_months = ['February', 'April']

months = list(sales_data.keys())
sales = list(sales_data.values())

colors = ['orange' if month in highlight_months else 'skyblue' for month in months]

sns.barplot(x=months, y=sales, palette=colors)

plt.title('Monthly Sales')
plt.xlabel('Month')
plt.ylabel('Sales')
# Display the plot
# Your code here

Need a hint?

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