Sparse data handling in Data Analysis Python - Mini Project: Build & Apply

Handling Sparse Data in Python

📖 Scenario: Imagine you work for a company that collects customer feedback scores for many products. Most customers don't rate every product, so the data has many missing or zero values. This is called sparse data.Handling sparse data well helps us analyze only the important information without wasting time on empty values.

🎯 Goal: You will create a dictionary of product ratings, set a threshold to find popular products, filter the dictionary to keep only products with ratings above the threshold, and finally print the filtered results.

📋 What You'll Learn

Create a dictionary with product names as keys and their ratings as values.

Create a threshold variable to select products with ratings above this value.

Use dictionary comprehension to filter products with ratings above the threshold.

Print the filtered dictionary showing popular products.

💡 Why This Matters

🌍 Real World

Companies often collect data with many missing or zero values. Filtering sparse data helps focus on meaningful information for better decisions.

💼 Career

Data analysts and scientists frequently clean and filter sparse datasets to prepare for analysis and reporting.

1

Create the product ratings dictionary

Create a dictionary called product_ratings with these exact entries: 'Laptop': 4, 'Smartphone': 0, 'Tablet': 3, 'Headphones': 0, 'Smartwatch': 5

Data Analysis Python

# Create the product_ratings dictionary with given values
# Your code here

Hint

Use curly braces {} to create a dictionary with the exact product names and ratings.

2

Set the rating threshold

Create a variable called rating_threshold and set it to 2 to select products with ratings above this value.

Data Analysis Python

product_ratings = {'Laptop': 4, 'Smartphone': 0, 'Tablet': 3, 'Headphones': 0, 'Smartwatch': 5}
# Create rating_threshold variable and set it to 2
# Your code here

Hint

Just create a variable and assign the number 2 to it.

3

Filter products with ratings above threshold

Use dictionary comprehension to create a new dictionary called popular_products that includes only products from product_ratings with ratings greater than rating_threshold. Use for product, rating in product_ratings.items() in your comprehension.

Data Analysis Python

product_ratings = {'Laptop': 4, 'Smartphone': 0, 'Tablet': 3, 'Headphones': 0, 'Smartwatch': 5}
rating_threshold = 2
# Create popular_products dictionary using comprehension to filter ratings > rating_threshold
# Your code here

Hint

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

4

Print the filtered popular products

Write a print statement to display the popular_products dictionary.

Data Analysis Python

product_ratings = {'Laptop': 4, 'Smartphone': 0, 'Tablet': 3, 'Headphones': 0, 'Smartwatch': 5}
rating_threshold = 2
popular_products = {product: rating for product, rating in product_ratings.items() if rating > rating_threshold}
# Print the popular_products dictionary
# Your code here

Hint

Use print(popular_products) to show the filtered dictionary.