What if you have a list of 10,000 sales transactions? You won’t write code for each one. This is where loops come in. Loops allow you to perform the same action on every item in a collection, automating what would otherwise be an impossibly tedious task.
for loop: The most common loop. It iterates over a sequence (like a list or dictionary) and executes a block of code for each item.
# Calculate total revenue from a list of sales
total_revenue = 0
for sale in monthly_sales:
total_revenue += sale
while loop: Runs as long as a certain condition is True. It’s used when you don’t know beforehand how many times you need to loop, such as in a simulation.The real power comes from combining loops with if statements. This allows you to perform an action on only the items that meet a certain criteria, which is the foundation of filtering and transforming data.
# Find all transactions over a certain amount
large_transactions = []
for amount in transactions:
if amount > 500:
large_transactions.append(amount)
Before you begin, ensure you have followed the setup instructions in the main README.md to set up your virtual environment and install the required libraries.
The script for this lesson, loops.py, has been refactored to place each looping task into its own testable function.
Day_10_Loops/loops.py. Notice the functions calculate_total_from_list(), filter_high_value_customers(), and simulate_investment_growth().Coding-For-MBA), run the script to see the functions in action:
python Day_10_Loops/loops.py
pytest tests/test_day_10.py
Calculate Average Employee Age:
my_solutions_10.py), create a list of employee dictionaries: employees = [{"name": "Alice", "age": 30}, {"name": "Bob", "age": 45}, {"name": "Charlie", "age": 35}].calculate_average_age(employee_list) that takes a list of employee dictionaries.for loop to get the sum of all ages.total_age / number_of_employees).Filter High-Priority Customers:
loops.py for an example).filter_high_value_customers function from the lesson script.Inventory Stock Alert:
inventory = {"Laptops": 15, "Mice": 150, "Keyboards": 45}.check_inventory_levels function.🎉 Incredible! The combination of loops and conditionals is the foundation of almost all data processing and analysis tasks. You’ve completed the core structures of Python!
Run this lesson’s code interactively in your browser:
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???+ example “loops.py” View on GitHub
```python title="loops.py"
"""
Day 10: Automating Tasks with Loops (Refactored)
This script demonstrates how to use for and while loops to
process collections of business data automatically. This version
is refactored into functions for better organization and testability.
"""
def calculate_total_from_list(numbers_list):
"""Calculates the sum of all numbers in a list using a for loop."""
total = 0
for number in numbers_list:
total += number
return total
def filter_high_value_customers(customers_list, threshold=2000):
"""
Filters a list of customer dictionaries to find those who spent above a threshold.
Returns a list of their names.
"""
high_priority = []
for customer in customers_list:
if customer.get("total_spent", 0) > threshold:
high_priority.append(customer.get("name"))
return high_priority
def check_inventory_levels(inventory_dict, threshold=50):
"""
Checks an inventory dictionary and returns a list of products
that are below a specified stock threshold.
"""
low_stock_alerts = []
for product, count in inventory_dict.items():
if count < threshold:
low_stock_alerts.append(product)
return low_stock_alerts
def simulate_investment_growth(initial_investment, target_amount, interest_rate):
"""
Simulates the number of years it takes for an investment to reach a target.
Returns the number of years.
"""
if initial_investment <= 0 or interest_rate <= 0:
return -1 # Indicate invalid input
investment = initial_investment
years = 0
while investment < target_amount:
investment *= 1 + interest_rate
years += 1
return years
if __name__ == "__main__":
# --- Using a for loop to aggregate data ---
print("--- Calculating Total Monthly Revenue ---")
sales_data = [2340.50, 3100.25, 2900.00, 4500.75]
total_rev = calculate_total_from_list(sales_data)
print(f"Total Revenue for the month: ${total_rev:.2f}")
print("-" * 20)
# --- Using a for loop with a conditional to filter data ---
print("--- Filtering High-Value Customers ---")
customer_data = [
{"name": "InnovateCorp", "total_spent": 5500},
{"name": "DataDriven Inc.", "total_spent": 1200},
{"name": "Analytics LLC", "total_spent": 2100},
{"name": "Global Solutions", "total_spent": 850},
]
priority_customers = filter_high_value_customers(customer_data)
print(f"High-priority customers to contact: {priority_customers}")
print("-" * 20)
# --- Looping through a dictionary for inventory alerts ---
print("--- Inventory Stock Level Alerts ---")
inventory_levels = {"Laptops": 15, "Mice": 150, "Keyboards": 45, "Monitors": 25}
low_stock_items = check_inventory_levels(inventory_levels)
for item in low_stock_items:
print(
f"ALERT: {item} are low on stock ({inventory_levels[item]} units remaining)."
)
print("-" * 20)
# --- Using a while loop for financial simulation ---
print("--- Investment Growth Simulation ---")
initial = 10000
target_val = 20000
rate = 0.07
years_to_double = simulate_investment_growth(initial, target_val, rate)
print(
f"It will take {years_to_double} years for the initial investment of ${initial} to double at a {rate * 100}% interest rate."
)
print("-" * 20)
```
???+ example “solutions.py” View on GitHub
```python title="solutions.py"
"""
Day 10: Solutions to Exercises
"""
# --- Exercise 1: Calculate Average Employee Age ---
print("--- Solution to Exercise 1 ---")
employees = [
{"name": "Alice", "age": 30},
{"name": "Bob", "age": 45},
{"name": "Charlie", "age": 35},
{"name": "Diana", "age": 28},
]
total_age = 0
for employee in employees:
total_age += employee["age"]
# Ensure we don't divide by zero if the list is empty
if len(employees) > 0:
average_age = total_age / len(employees)
print(f"The average age of employees is: {average_age:.1f} years")
else:
print("No employees in the list.")
print("-" * 20)
# --- Exercise 2: Filter High-Priority Customers ---
print("--- Solution to Exercise 2 ---")
customers = [
{"name": "InnovateCorp", "total_spent": 5500},
{"name": "DataDriven Inc.", "total_spent": 1200},
{"name": "Analytics LLC", "total_spent": 2100},
{"name": "Key Insights", "total_spent": 1800},
]
high_priority_customers = []
for customer in customers:
if customer["total_spent"] > 2000:
high_priority_customers.append(customer["name"])
print(f"High-priority customers (spent > $2000): {high_priority_customers}")
print("-" * 20)
# --- Exercise 3: Inventory Stock Alert ---
print("--- Solution to Exercise 3 ---")
inventory = {"Laptops": 15, "Mice": 150, "Keyboards": 45, "Monitors": 25}
low_stock_threshold = 50
print("Checking inventory levels...")
for product, count in inventory.items():
if count < low_stock_threshold:
print(f" - ALERT: {product} are low on stock ({count} units remaining).")
print("-" * 20)
```