Data type optimization in Data Analysis Python - Mini Project: Build & Apply

Data type optimization

📖 Scenario: You work as a data analyst for a retail company. You have a dataset of product sales with product IDs, quantities sold, and prices. The dataset is large, and you want to optimize the data types to save memory and improve performance.

🎯 Goal: You will create a dictionary with product sales data, add a configuration for the maximum quantity threshold, optimize the data types by converting values to smaller types where possible, and finally print the optimized data.

📋 What You'll Learn

Create a dictionary called sales_data with exact entries for product IDs as strings and their quantities and prices as integers and floats.

Create a variable called max_quantity set to 1000.

Use a dictionary comprehension to create a new dictionary called optimized_sales where quantities greater than max_quantity are converted to float and others to int, and prices are converted to float with two decimals.

Print the optimized_sales dictionary.

💡 Why This Matters

🌍 Real World

Optimizing data types helps reduce memory use and speeds up data processing in real-world data analysis tasks.

💼 Career

Data analysts and scientists often optimize data types to handle large datasets efficiently and improve performance.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'P001': {'quantity': 500, 'price': 19.99}, 'P002': {'quantity': 1500, 'price': 29.95}, 'P003': {'quantity': 300, 'price': 9.99}.

Data Analysis Python

# Create the sales_data dictionary with exact entries
# Your code here

Hint

Use a dictionary with product IDs as keys and dictionaries as values containing 'quantity' and 'price'.

2

Set the maximum quantity threshold

Create a variable called max_quantity and set it to 1000.

Data Analysis Python

sales_data = {
    'P001': {'quantity': 500, 'price': 19.99},
    'P002': {'quantity': 1500, 'price': 29.95},
    'P003': {'quantity': 300, 'price': 9.99}
}
# Create max_quantity variable and set it to 1000
# Your code here

Hint

Just assign the number 1000 to the variable max_quantity.

3

Optimize data types with dictionary comprehension

Use a dictionary comprehension to create a new dictionary called optimized_sales. For each product in sales_data, convert quantity to float if it is greater than max_quantity, otherwise keep it as int. Convert price to float rounded to two decimals.

Data Analysis Python

sales_data = {
    'P001': {'quantity': 500, 'price': 19.99},
    'P002': {'quantity': 1500, 'price': 29.95},
    'P003': {'quantity': 300, 'price': 9.99}
}
max_quantity = 1000
# Create optimized_sales dictionary using dictionary comprehension
# Your code here

Hint

Use a dictionary comprehension with for product, info in sales_data.items(). Use a conditional expression to convert quantity.

4

Print the optimized sales data

Print the optimized_sales dictionary.

Data Analysis Python

sales_data = {
    'P001': {'quantity': 500, 'price': 19.99},
    'P002': {'quantity': 1500, 'price': 29.95},
    'P003': {'quantity': 300, 'price': 9.99}
}
max_quantity = 1000
optimized_sales = {
    product: {
        'quantity': float(info['quantity']) if info['quantity'] > max_quantity else int(info['quantity']),
        'price': round(float(info['price']), 2)
    }
    for product, info in sales_data.items()
}
# Print optimized_sales dictionary
# Your code here

Hint

Use print(optimized_sales) to display the dictionary.