beginner⏱60 minLección 7 de 10

Functions

Master Python functions: definition, parameters, return values, scope, docstrings, and practical function patterns.

Functions

Functions are reusable blocks of code that perform specific tasks. They help organize code, avoid repetition, and make programs easier to understand and maintain.

What is a Function?

A function is a named block of code that can be called (invoked) to perform a task. Think of it as a recipe: you define it once, then use it whenever needed.

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Defining Functions

Basic Syntax

python

def function_name(parameters):
    """Docstring: describes what the function does."""
    # Function body
    result = ...
    return result

Simple Function Example

python

def greet(name):
    """Return a greeting message for the given name."""
    return f"Hello, {name}! Welcome to Python."

# Calling the function
message = greet("Alice")
print(message)  # Hello, Alice! Welcome to Python.

message = greet("Bob")
print(message)  # Hello, Bob! Welcome to Python.

Functions Without Return Value

python

def print_separator(char="-", length=30):
    """Print a separator line."""
    print(char * length)

print_separator()           # ------------------------------
print_separator("=", 20)    # ====================
print_separator("*", 10)    # **********

ℹ️Note

Functions without a return statement implicitly return None. This is Python's way of representing "nothing."

Parameters and Arguments

Parameters are variables listed in the function definition. Arguments are the actual values passed when calling the function.

Types of Parameters

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Positional Parameters

python

def calculate_area(length, width):
    """Calculate the area of a rectangle."""
    return length * width

# Arguments matched by position
area = calculate_area(10, 5)
print(f"Area: {area}")  # 50

# Order matters!
print(f"10×5 = {calculate_area(10, 5)}")  # 50
print(f"5×10 = {calculate_area(5, 10)}")  # 50 (same result, but different meaning)

Default Parameters

python

def create_profile(name, age, country="Brazil", language="Python"):
    """Create a user profile with default values."""
    return {
        "name": name,
        "age": age,
        "country": country,
        "language": language
    }

# Using defaults
profile1 = create_profile("Alice", 25)
print(profile1)
# {'name': 'Alice', 'age': 25, 'country': 'Brazil', 'language': 'Python'}

# Overriding defaults
profile2 = create_profile("Bob", 30, "USA", "JavaScript")
print(profile2)
# {'name': 'Bob', 'age': 30, 'country': 'USA', 'language': 'JavaScript'}

⚠️Warning

Default parameter values are evaluated only once, when the function is defined. Never use mutable defaults (like lists or dicts):

python

# BAD - shared list across calls!
def add_item(item, items=[]):
    items.append(item)
    return items
# GOOD - create new list each time
def add_item(item, items=None):
    if items is None:
        items = []
    items.append(item)
    return items

Keyword Arguments

python

def create_email(to, subject, body, priority="normal"):
    """Create an email message."""
    return f"To: {to}\nSubject: {subject}\nPriority: {priority}\n\n{body}"

# Using keyword arguments (order doesn't matter)
email = create_email(
    body="Please review the attached document.",
    to="manager@company.com",
    priority="high",
    subject="Document Review"
)
print(email)

Output:

To: manager@company.com
Subject: Document Review
Priority: high

Please review the attached document.

*args - Variable Positional Arguments

python

def calculate_sum(*args):
    """Sum any number of arguments."""
    total = 0
    for num in args:
        total += num
    return total

print(f"Sum of 1, 2, 3: {calculate_sum(1, 2, 3)}")         # 6
print(f"Sum of 10, 20, 30, 40: {calculate_sum(10, 20, 30, 40)}")  # 100
print(f"Sum of nothing: {calculate_sum()}")                  # 0

**kwargs - Variable Keyword Arguments

python

def create_student_record(name, **kwargs):
    """Create a student record with optional fields."""
    record = {"name": name}
    record.update(kwargs)
    return record

student = create_student_record(
    "Maria",
    age=22,
    major="Computer Science",
    gpa=3.8,
    enrolled=True
)
print(student)
# {'name': 'Maria', 'age': 22, 'major': 'Computer Science', 'gpa': 3.8, 'enrolled': True}

Return Values

Functions can return values using the return statement.

Single Return Value

python

def square(number):
    """Return the square of a number."""
    return number ** 2

result = square(5)
print(f"5² = {result}")  # 25

Multiple Return Values

python

def divide_with_remain(dividend, divisor):
    """Return quotient and remainder."""
    quotient = dividend // divisor
    remainder = dividend % divisor
    return quotient, remainder  # Returns a tuple

q, r = divide_with_remain(17, 5)
print(f"17 ÷ 5 = {q} remainder {r}")  # 17 ÷ 5 = 3 remainder 2

Early Return

python

def classify_age(age):
    """Classify a person's age group."""
    if age < 0:
        return "Invalid age"
    if age < 13:
        return "Child"
    if age < 18:
        return "Teenager"
    if age < 65:
        return "Adult"
    return "Senior"

ages = [-5, 8, 15, 30, 70]
for age in ages:
    print(f"Age {age:3d}: {classify_age(age)}")

Output:

Age  -5: Invalid age
Age   8: Child
Age  15: Teenager
Age  30: Adult
Age  70: Senior

Variable Scope

Scope determines where a variable can be accessed.

Scope Levels

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Scope Examples

python

# Global variable
global_var = "I'm global"

def demonstrate_scope():
    # Local variable
    local_var = "I'm local"
    
    # Can read global variable
    print(f"Inside function - global: {global_var}")
    print(f"Inside function - local: {local_var}")

demonstrate_scope()
print(f"Outside function - global: {global_var}")
# print(local_var)  # ERROR: NameError - local_var not defined here

Modifying Global Variables

python

counter = 0

def increment():
    global counter  # Declare we're using the global variable
    counter += 1

increment()
increment()
increment()
print(f"Counter: {counter}")  # 3

💡Tip

Avoid using global when possible. Instead, pass values as parameters and return results:

python

# Better approach
def increment_counter(counter):
    return counter + 1
counter = 0
counter = increment_counter(counter)

Docstrings

Docstrings are string literals that appear as the first statement in a function. They document what the function does.

Docstring Formats

python

def calculate_bmi(weight_kg, height_m):
    """
    Calculate Body Mass Index (BMI).
    
    Args:
        weight_kg: Weight in kilograms (float)
        height_m: Height in meters (float)
    
    Returns:
        float: BMI value
    
    Raises:
        ValueError: If weight or height is not positive
    
    Example:
        >>> calculate_bmi(70, 1.75)
        22.86
    """
    if weight_kg <= 0 or height_m <= 0:
        raise ValueError("Weight and height must be positive")
    
    return weight_kg / (height_m ** 2)

# Access docstring
print(calculate_bmi.__doc__)
# Or use help()
# help(calculate_bmi)

Using help()

python

def fibonacci(n):
    """
    Calculate the nth Fibonacci number.
    
    The Fibonacci sequence: 0, 1, 1, 2, 3, 5, 8, 13, ...
    Each number is the sum of the two preceding ones.
    
    Args:
        n: Position in the sequence (non-negative integer)
    
    Returns:
        int: The nth Fibonacci number
    """
    if n < 0:
        raise ValueError("n must be non-negative")
    if n <= 1:
        return n
    
    a, b = 0, 1
    for _ in range(2, n + 1):
        a, b = b, a + b
    return b

# Display documentation
help(fibonacci)

Lambda Functions

Lambda functions are small anonymous functions defined with the lambda keyword.

Lambda Syntax

python

# Regular function
def square(x):
    return x ** 2

# Equivalent lambda
square_lambda = lambda x: x ** 2

print(f"square(5) = {square(5)}")              # 25
print(f"square_lambda(5) = {square_lambda(5)}") # 25

Lambda Use Cases

python

# Sorting with lambda
students = [
    ("Alice", 85),
    ("Bob", 92),
    ("Charlie", 78),
]

# Sort by grade (second element)
by_grade = sorted(students, key=lambda s: s[1])
print("Sorted by grade:", by_grade)

# Sort by name (first element)
by_name = sorted(students, key=lambda s: s[0])
print("Sorted by name:", by_name)

# Using with map()
numbers = [1, 2, 3, 4, 5]
squared = list(map(lambda x: x ** 2, numbers))
print(f"Squared: {squared}")  # [1, 4, 9, 16, 25]

# Using with filter()
evens = list(filter(lambda x: x % 2 == 0, numbers))
print(f"Evens: {evens}")  # [2, 4]

Real-World Example: Temperature Converter System

python

# temperature_converter.py
"""
Temperature Converter System
Supports Celsius, Fahrenheit, and Kelvin conversions.
"""

def celsius_to_fahrenheit(c):
    """Convert Celsius to Fahrenheit."""
    return c * 9/5 + 32

def celsius_to_kelvin(c):
    """Convert Celsius to Kelvin."""
    return c + 273.15

def fahrenheit_to_celsius(f):
    """Convert Fahrenheit to Celsius."""
    return (f - 32) * 5/9

def fahrenheit_to_kelvin(f):
    """Convert Fahrenheit to Kelvin."""
    return (f - 32) * 5/9 + 273.15

def kelvin_to_celsius(k):
    """Convert Kelvin to Celsius."""
    return k - 273.15

def kelvin_to_fahrenheit(k):
    """Convert Kelvin to Fahrenheit."""
    return (k - 273.15) * 9/5 + 32

# Conversion map
CONVERSIONS = {
    ("C", "F"): celsius_to_fahrenheit,
    ("C", "K"): celsius_to_kelvin,
    ("F", "C"): fahrenheit_to_celsius,
    ("F", "K"): fahrenheit_to_kelvin,
    ("K", "C"): kelvin_to_celsius,
    ("K", "F"): kelvin_to_fahrenheit,
}

def convert_temperature(value, from_unit, to_unit):
    """
    Convert temperature between units.
    
    Args:
        value: Temperature value (float)
        from_unit: Source unit ('C', 'F', or 'K')
        to_unit: Target unit ('C', 'F', or 'K')
    
    Returns:
        float: Converted temperature
    """
    from_unit = from_unit.upper()
    to_unit = to_unit.upper()
    
    if from_unit == to_unit:
        return value
    
    key = (from_unit, to_unit)
    if key not in CONVERSIONS:
        raise ValueError(f"Invalid conversion: {from_unit} to {to_unit}")
    
    return CONVERSIONS[key](value)

def display_conversion_table():
    """Display a temperature conversion table."""
    print("=" * 55)
    print("     TEMPERATURE CONVERSION TABLE")
    print("=" * 55)
    print(f"{'Celsius':>10} {'Fahrenheit':>12} {'Kelvin':>10}")
    print("-" * 55)
    
    for c in range(-20, 51, 5):
        f = celsius_to_fahrenheit(c)
        k = celsius_to_kelvin(c)
        print(f"{c:10.1f} {f:12.1f} {k:10.1f}")
    
    print("=" * 55)

# Run the converter
display_conversion_table()

# Interactive conversion
print("\nQuick Conversions:")
test_values = [
    (0, "C", "F"),
    (100, "C", "F"),
    (98.6, "F", "C"),
    (37, "C", "K"),
    (0, "K", "C"),
]

for value, frm, to in test_values:
    result = convert_temperature(value, frm, to)
    print(f"  {value}°{frm} = {result:.2f}°{to}")

Output:

=======================================================
     TEMPERATURE CONVERSION TABLE
=======================================================
   Celsius   Fahrenheit     Kelvin
-------------------------------------------------------
     -20.0        -4.0      253.2
     -15.0          5.0      258.2
     -10.0         14.0      263.2
      -5.0         23.0      268.2
       0.0         32.0      273.2
       5.0         41.0      278.2
      10.0         50.0      283.2
      15.0         59.0      288.2
      20.0         68.0      293.2
      25.0         77.0      298.2
      30.0         86.0      303.2
      35.0         95.0      308.2
      40.0        104.0      313.2
      45.0        113.0      318.2
      50.0        122.0      323.2
=======================================================

Quick Conversions:
  0°C = 32.00°F
  100°C = 212.00°F
  98.6°F = 37.00°C
  37°C = 310.15K
  0K = -273.15°C

Practice Exercises

Exercise 1: Simple Function

Write a function is_even(n) that returns True if n is even, False otherwise.

Exercise 2: Temperature Converter

Write a function that converts Celsius to Fahrenheit and vice versa based on a parameter.

Exercise 3: String Repeater

Write a function repeat_string(text, n) that returns the text repeated n times, separated by spaces.

Exercise 4: Maximum of Three

Write a function max_of_three(a, b, c) that returns the largest of three numbers without using max().

Exercise 5: Palindrome Checker

Write a function is_palindrome(text) that returns True if the text reads the same forwards and backwards.

Exercise 6: Function with Default Parameters

Write a function format_currency(amount, symbol="$", decimals=2) that formats a number as currency.

Exercise 7: Fibonacci Sequence

Write a function fibonacci_sequence(n) that returns a list of the first n Fibonacci numbers.

Exercise 8: Statistics Functions

Write functions mean(numbers), median(numbers), and mode(numbers) that calculate basic statistics.

Summary

In this lesson, you learned:

How to define and call functions with def
Different parameter types: positional, keyword, default, *args, **kwargs
How to return single and multiple values
Variable scope and the LEGB rule
How to write effective docstrings
How to use lambda functions for simple operations
How to organize code into reusable, well-documented functions

Functions are the building blocks of modular programming. Master them to write clean, maintainable code.

Progreso70%

Control Flow: Loops

Data Structures: Lists & Tuples

Contenido del Curso

1. How Computers Work 2. Python Setup & First Program 3. Variables & Data Types 4. Operators & Expressions 5. Control Flow: Conditionals 6. Control Flow: Loops 7. Functions 8. Data Structures: Lists & Tuples 9. Data Structures: Dictionaries & Sets 10. Imperative Algorithm Projects