Conditions, Loops, and Functions

Why are these useful?

Example

def revenue_after_discount(price, discount_rate):
"""Return the price after applying a discount rate (0.10 = 10%)."""
return price * (1 - discount_rate)
print(revenue_after_discount(1000, 0.10)) # 900.0

How it works

• def defines the function. price and discount_rate are parameters.
• return sends the result back to whoever called the function.
• The function doesn't print anything by itself. print() displays the returned value.
• A discount rate of 0.10 represents 10%.
• The text in triple quotes is a docstring: a short description of what the function does.

Conditions and loops

Conditions:

order_value = 6200
if order_value >= 5000:
label = 'Large'
elif order_value >= 1000:
label = 'Medium'
else:
label = 'Small'
print(label) # Large

Loops:

files = ['jan.csv', 'feb.csv', 'mar.csv']
for f in files:
print(f"Processing {f}")

In pandas, prefer vectorised operations over loops:

Loops are still useful for general tasks, such as processing a list of files.

Common mistakes

• Unclear function names: calc() vs calculate_net_revenue()
• Printing instead of returning: later calculations can't use a printed value
• Not testing edge cases: zero, negative values, missing values (None), empty lists
• Division by zero: guard with if orders > 0:
• Using = instead of == in conditions

Try it: Write a function that labels an order as 'Large' when its value is at least 5,000 and 'Standard' otherwise.