Sales data analysis pattern in Data Analysis Python - Mini Project: Build & Apply

Sales Data Analysis Pattern

📖 Scenario: You work in a small shop that sells three products. Every day, you record how many units of each product you sell. Now, you want to analyze this sales data to find out which products sold more than a certain number of units.

🎯 Goal: Build a simple program that stores daily sales data in a dictionary, sets a sales threshold, finds products that sold more than the threshold, and prints those products with their sales.

📋 What You'll Learn

Create a dictionary called sales with exact product names and sales numbers

Create a variable called threshold to set the minimum sales number

Use a dictionary comprehension to find products with sales greater than threshold

Print the resulting dictionary of filtered products and their sales

💡 Why This Matters

🌍 Real World

Analyzing sales data helps businesses understand which products are popular and make better stock decisions.

💼 Career

Data analysts often filter and summarize sales data to create reports and support business decisions.

1

Create the sales data dictionary

Create a dictionary called sales with these exact entries: 'Apples': 120, 'Bananas': 90, 'Cherries': 150, 'Dates': 60, 'Elderberries': 30.

Data Analysis Python

# Create the sales dictionary with exact product names and sales numbers
# Your code here

Hint

Use curly braces {} to create a dictionary. Separate keys and values with a colon :. Separate items with commas.

2

Set the sales threshold

Create a variable called threshold and set it to 80.

Data Analysis Python

sales = {'Apples': 120, 'Bananas': 90, 'Cherries': 150, 'Dates': 60, 'Elderberries': 30}
# Set the sales threshold to 80
# Your code here

Hint

Just assign the number 80 to a variable named threshold.

3

Filter products with sales above threshold

Use a dictionary comprehension to create a new dictionary called top_sellers that contains only products from sales with sales greater than threshold. Use for product, count in sales.items() in your comprehension.

Data Analysis Python

sales = {'Apples': 120, 'Bananas': 90, 'Cherries': 150, 'Dates': 60, 'Elderberries': 30}
threshold = 80
# Create top_sellers dictionary with products having sales > threshold
# Your code here

Hint

Use the syntax {key: value for key, value in dict.items() if condition} to filter dictionary items.

4

Print the filtered top sellers

Print the top_sellers dictionary.

Data Analysis Python

sales = {'Apples': 120, 'Bananas': 90, 'Cherries': 150, 'Dates': 60, 'Elderberries': 30}
threshold = 80
top_sellers = {product: count for product, count in sales.items() if count > threshold}
# Print the top_sellers dictionary
# Your code here

Hint

Use print(top_sellers) to display the filtered dictionary.