Management Science - Regional Manager of Multiple Locations!
Welcome to your new role as Regional Manager at Bean Counter!
Congratulations on another promotion!
Your standardized functions from the Assistant Manager role were so successful that the CEO has promoted you to Regional Manager! You’re now responsible for overseeing 10 Bean Counter locations across the city.
The Challenge: Managing 10 locations means tracking large amounts of data:
Using simple variables or lists isn’t enough anymore. You need a better way to organize all this information!
Your Task: Understand dictionaries - Python’s way of storing structured data with labels. Think of them as digital filing cabinets where everything has a clear label and is easy to find.
In this tutorial, we’ll learn how dictionaries help you manage complex business data across all Bean Counter locations.
Cells marked with “YOUR CODE BELOW” expect you to write your own code. Test blocks will verify your solutions.
As Regional Manager, you need to track detailed information about each Bean Counter location. Dictionaries are perfect for this - they store data with descriptive labels (keys) instead of position numbers.
# A dictionary for one Bean Counter location
downtown_store = {
"name": "Bean Counter Downtown",
"manager": "Sarah Chen",
"daily_sales": 4500.00,
"staff_count": 8,
"coffee_beans_kg": 45.5
}
# Accessing data is intuitive with keys
print(f"Location: {downtown_store['name']}")
print(f"Manager: {downtown_store['manager']}")
print(f"Today's sales: ${downtown_store['daily_sales']}")Location: Bean Counter Downtown
Manager: Sarah Chen
Today's sales: $4500.0Dictionary anatomy:
{} create a dictionarydict['key']Create a dictionary called airport_store for the Bean Counter airport location with:
# YOUR CODE BELOW
airport_store = {
# Add the key-value pairs here
}# Test your location profile
assert airport_store["name"] == "Bean Counter Airport", "Store name should be 'Bean Counter Airport'"
assert airport_store["manager"] == "James Wilson", "Manager should be 'James Wilson'"
assert airport_store["daily_sales"] == 6200.00, "Daily sales should be 6200.00"
assert airport_store["staff_count"] == 12, "Staff count should be 12"
assert airport_store["coffee_beans_kg"] == 62.0, "Coffee beans should be 62.0 kg"
print("Perfect! You've created your first location profile!")The airport store just received a delivery! Update the dictionary:
dict['key'] = new_valuedict['new_key'] = valuedict['number'] = dict['number'] + 10# Assuming airport_store exists from previous exercise
airport_store = {
"name": "Bean Counter Airport",
"manager": "James Wilson",
"daily_sales": 6200.00,
"staff_count": 12,
"coffee_beans_kg": 62.0
}
# YOUR CODE BELOW
# Update coffee beans (add 25 kg)
# Update manager
# Add last_inspection# Test your updates
assert airport_store["coffee_beans_kg"] == 87.0, "Coffee beans should now be 87.0 kg"
assert airport_store["manager"] == "Maria Garcia", "Manager should be Maria Garcia"
assert airport_store["last_inspection"] == "2024-01-15", "Should have last_inspection date"
print("Excellent inventory management! The airport store is fully updated!")Managing 10 Bean Counter locations means working with multiple dictionaries. We can organize them in a list!
# List of dictionaries - each dictionary is one store
bean_counter_locations = [
{
"id": 1,
"neighborhood": "Downtown",
"daily_sales": 4500,
"needs_restock": False
},
{
"id": 2,
"neighborhood": "Airport",
"daily_sales": 6200,
"needs_restock": True
},
{
"id": 3,
"neighborhood": "University",
"daily_sales": 3800,
"needs_restock": False
}
]
# Access specific stores and their data
print(f"First location: {bean_counter_locations[0]['neighborhood']}")
print(f"Second location sales: ${bean_counter_locations[1]['daily_sales']}")First location: Downtown
Second location sales: $6200When accessing data from a list of dictionaries, remember the two-step process:
list[index]list[index]['key']Create a list called regional_stores containing 2 Bean Counter locations. Each store dictionary should have:
# YOUR CODE BELOW
regional_stores = [
# Add 2 store dictionaries here
]# Test your regional network
assert len(regional_stores) == 2, "Should have exactly 2 stores"
assert all("location_name" in store for store in regional_stores), "Each store needs location_name"
assert all("monthly_revenue" in store for store in regional_stores), "Each store needs monthly_revenue"
assert all("manager_name" in store for store in regional_stores), "Each store needs manager_name"
assert all("flagship" in store for store in regional_stores), "Each store needs flagship status"
assert sum(store["flagship"] for store in regional_stores) >= 1, "At least one store should be flagship"
print("Great work! Your regional network is established!")As Regional Manager, you need to analyze data across all locations. Let’s loop through dictionaries to find insights!
# Analyzing multiple stores
stores = [
{
"name": "Downtown",
"sales": 4500,
"rating": 4.8
},
{
"name": "Airport",
"sales": 6200,
"rating": 4.5
},
{
"name": "University",
"sales": 3800,
"rating": 4.9
}
]
# Calculate total sales across all stores
total_sales = 0
for store in stores:
total_sales = total_sales + store["sales"]
print(f"{store['name']}: ${store['sales']} (Rating: {store['rating']}★)")
print(f"\nTotal regional sales: ${total_sales}")Downtown: $4500 (Rating: 4.8★)
Airport: $6200 (Rating: 4.5★)
University: $3800 (Rating: 4.9★)
Total regional sales: $14500When looping through a list of dictionaries:
store) represents one complete dictionarystore['key_name']Write code to analyze the performance_data list:
performance_data = [
{"store": "Downtown", "customers": 450, "satisfaction": 4.7},
{"store": "Airport", "customers": 680, "satisfaction": 4.4},
{"store": "University", "customers": 520, "satisfaction": 4.8},
{"store": "Mall", "customers": 380, "satisfaction": 4.6},
{"store": "Station", "customers": 590, "satisfaction": 4.5}
]
total_customers = 0
total_satisfaction = 0
busy_stores = 0 # Stores with >500 customers
# YOUR CODE BELOW
for store in performance_data:
# Add to total customers
# Add to satisfaction sum
# Check if this is a busy store
# Print your results
## Test your analysis
assert total_customers == 2620, f"Total customers should be 2620, got {total_customers}"
assert round(average_satisfaction, 2) == 4.60, f"Average satisfaction should be 4.60, got {round(average_satisfaction, 2)}"
assert busy_stores == 3, f"Should have 3 busy stores, got {busy_stores}"
print(f"Total customers: {total_customers}")
print(f"Average satisfaction: {average_satisfaction}")
print(f"Stores serving >500 customers: {busy_stores}")
print("Excellent analysis! You have a clear picture of regional performance!")Regional Managers often need to find stores that meet specific criteria - which ones need supplies? Which are underperforming?
# Finding stores that need attention
all_stores = [
{"name": "Downtown", "inventory_kg": 15, "daily_sales": 4500},
{"name": "Airport", "inventory_kg": 8, "daily_sales": 6200},
{"name": "University", "inventory_kg": 22, "daily_sales": 3800},
{"name": "Mall", "inventory_kg": 5, "daily_sales": 4100}
]
# Find stores with low inventory (less than 10 kg)
low_inventory_stores = []
for store in all_stores:
if store["inventory_kg"] < 10:
low_inventory_stores.append(store["name"])
print("Stores needing coffee beans:", low_inventory_stores)Stores needing coffee beans: ['Airport', 'Mall']Find stores that need immediate attention. Create two lists:
restock_needed: Store names with inventory below 20 kglow_performers: Store names with daily revenue below 1000inventory_data = [
{"name": "Plaza", "coffee_kg": 45, "milk_liters": 30, "daily_revenue": 1850},
{"name": "Station", "coffee_kg": 12, "milk_liters": 8, "daily_revenue": 920},
{"name": "Park", "coffee_kg": 18, "milk_liters": 15, "daily_revenue": 1100},
{"name": "Beach", "coffee_kg": 7, "milk_liters": 22, "daily_revenue": 780},
{"name": "Downtown", "coffee_kg": 35, "milk_liters": 18, "daily_revenue": 2200}
]
# YOUR CODE BELOW
restock_needed = []
low_performers = []
for store in inventory_data:
# Check if coffee inventory is low (below 20 kg)
# Check if daily revenue is low (below 1000)# Test your filtering
assert set(restock_needed) == {"Station", "Park", "Beach"}, f"Restock list should be Station, Park, Beach"
assert set(low_performers) == {"Station", "Beach"}, f"Low performers should be Station and Beach"
print(f"Stores needing restock: {restock_needed}")
print(f"Low performing stores: {low_performers}")
print("Perfect! You've identified the stores that need immediate attention!")Let’s combine your function skills from the Assistant Manager role with dictionaries to create powerful analysis tools!
def calculate_store_bonus(store_data):
"""Calculate performance bonus based on store metrics"""
base_bonus = 1000
# Add bonus for high sales
if store_data["monthly_sales"] > 100000:
base_bonus = base_bonus + 500
# Add bonus for good ratings
if store_data["customer_rating"] >= 4.5:
base_bonus = base_bonus + 300
return base_bonus
# Test the function
store = {
"name": "Downtown",
"monthly_sales": 120000,
"customer_rating": 4.7
}
bonus = calculate_store_bonus(store)
print(f"{store['name']} earned a ${bonus} bonus!")Downtown earned a $1800 bonus!Create a function generate_store_report that takes a store dictionary and returns a performance summary string.
The function should:
# YOUR CODE BELOW
def generate_store_report(store):
# Calculate profit margin
# Determine performance status
# Return formatted report string# Test your report generator
test_store1 = {"name": "Plaza", "revenue": 50000, "costs": 30000}
report1 = generate_store_report(test_store1)
assert "40.0%" in report1, "Plaza should have 40.0% margin"
assert "Excellent" in report1, "Plaza should be Excellent"
test_store2 = {"name": "Station", "revenue": 40000, "costs": 34000}
report2 = generate_store_report(test_store2)
assert "15.0%" in report2, "Station should have 15.0% margin"
assert "Needs Improvement" in report2, "Station needs improvement"
print("Fantastic! Your reporting system gives clear insights into store performance!")Congratulations! You’ve successfully built a regional data management system for Bean Counter!
You’ve learned:
Your Bean Counter regional management system can now:
Remember:
{} to create dictionariesdict['key']What’s Next: Your data management skills have caught the CEO’s attention! In the next tutorial, you’ll be promoted to Operations Director, where you’ll learn about sorting and optimization - finding the best options among many choices. You’ll optimize scheduling, resource allocation, and discover how to always pick the best option for Bean Counter!
You will likely find solutions to most exercises online. However, I strongly encourage you to work on these exercises independently without searching explicitly for the exact answers to the exercises. Understanding someone else’s solution is very different from developing your own. Use the lecture notes and try to solve the exercises on your own. This approach will significantly enhance your learning and problem-solving skills.
Remember, the goal is not just to complete the exercises, but to understand the concepts and improve your programming abilities. If you encounter difficulties, review the lecture materials, experiment with different approaches, and don’t hesitate to ask for clarification during class discussions.