Functions for Reusable Analysis

Functions group instructions into reusable blocks. They help keep data analysis code organized and reduce repetition.

Defining and Calling a Function

Use the def keyword to define a function. Parameters allow you to pass input values, while return sends a result back to the caller.

def calculate_profit(revenue, expenses):

profit = revenue - expenses
return profit
result = calculate_profit(50000, 32000)
print("Profit:", result)

The function can be reused with different inputs without rewriting the calculation.

Functions with Default Parameters

You can provide default values for parameters that are optional.

def calculate_discount(price, discount_rate=0.10):
discount = price * discount_rate
return price - discount
print(calculate_discount(1000))       # 900.0
print(calculate_discount(1000, 0.20)) # 800.0

Returning Multiple Results

A function can return multiple values, which Python packages together as a tuple.

def analyze_sales(sales):
total = sum(sales)
average = total / len(sales)
highest = max(sales)
return total, average, highest
sales = [1200, 1500, 900, 1800]
total, average, highest = analyze_sales(sales)
print("Total:", total)
print("Average:", average)
print("Highest:", highest)

When building analytical workflows, functions can separate tasks such as calculating metrics, validating inputs, and transforming data. This makes code easier to test, maintain, and reuse.