Why visualization communicates findings in Data Analysis Python - See It in Action

Why visualization communicates findings

📖 Scenario: Imagine you work in a small bakery. You have sales data for different types of bread over a week. You want to understand which bread sells the most and share this with your team clearly.

🎯 Goal: You will create a simple data structure for sales, set a threshold to highlight popular breads, use a loop to filter those breads, and finally create a bar chart to show the sales visually. This will help you see and explain the best-selling breads easily.

📋 What You'll Learn

Create a dictionary with bread types and their sales numbers

Set a sales threshold to identify popular breads

Use a for loop to find breads with sales above the threshold

Create a bar chart to visualize the popular breads and their sales

💡 Why This Matters

🌍 Real World

Visualizing sales data helps bakery teams quickly understand which products are popular and make better decisions about baking and stocking.

💼 Career

Data visualization is a key skill for data analysts and scientists to communicate insights clearly to non-technical team members and stakeholders.

1

Create sales data dictionary

Create a dictionary called bread_sales with these exact entries: 'Sourdough': 120, 'Baguette': 80, 'Rye': 45, 'Whole Wheat': 150, 'Focaccia': 60.

Data Analysis Python

# Create the bread_sales dictionary with given sales numbers
# Your code here

Hint

Use curly braces {} to create a dictionary. Each bread type is a key, and its sales number is the value.

2

Set sales threshold

Create a variable called sales_threshold and set it to 70. This will help us find breads that sold more than 70 units.

Data Analysis Python

bread_sales = {'Sourdough': 120, 'Baguette': 80, 'Rye': 45, 'Whole Wheat': 150, 'Focaccia': 60}
# Set sales_threshold to 70
# Your code here

Hint

Just create a variable and assign the number 70 to it.

3

Find popular breads

Create a new dictionary called popular_breads that contains only the breads from bread_sales with sales greater than sales_threshold. Use a for loop with variables bread and sales to iterate over bread_sales.items().

Data Analysis Python

bread_sales = {'Sourdough': 120, 'Baguette': 80, 'Rye': 45, 'Whole Wheat': 150, 'Focaccia': 60}
sales_threshold = 70
# Create popular_breads dictionary with breads selling more than sales_threshold
# Your code here

Hint

Start with an empty dictionary. Use a for loop to check each bread's sales. Add breads with sales above the threshold to the new dictionary.

4

Visualize popular breads

Import matplotlib.pyplot as plt. Create a bar chart showing the sales of breads in popular_breads. Use plt.bar() with keys as labels and values as heights. Add plt.title() with text 'Popular Bread Sales'. Finally, use plt.show() to display the chart.

Data Analysis Python

bread_sales = {'Sourdough': 120, 'Baguette': 80, 'Rye': 45, 'Whole Wheat': 150, 'Focaccia': 60}
sales_threshold = 70
popular_breads = {}
for bread, sales in bread_sales.items():
    if sales > sales_threshold:
        popular_breads[bread] = sales

# Import matplotlib.pyplot as plt
# Create a bar chart for popular_breads
# Add title 'Popular Bread Sales'
# Show the plot
# Your code here

Hint

Use plt.bar() with keys and values from popular_breads. Add a title and labels for clarity. Finally, call plt.show() to see the chart.