Python Functions

Learn how to create and use functions in Python to organize and reuse your code.

Creating a Function

A function is a block of code which only runs when it is called.

You can pass data, known as parameters, into a function.

A function can return data as a result.

In Python a function is defined using the def keyword:

Example

def my_function():
    print("Hello from a function")

Calling a Function

To call a function, use the function name followed by parenthesis:

Example

def my_function():
    print("Hello from a function")

my_function()

Arguments

Information can be passed into functions as arguments.

Arguments are specified after the function name, inside the parentheses. You can add as many arguments as you want, just separate them with a comma.

Example

def my_function(fname):
    print(fname + " Refsnes")

my_function("Emil")
my_function("Tobias")
my_function("Linus")

Arguments are often shortened to args in Python documentations.

Parameters or Arguments?

The terms parameter and argument can be used for the same thing: information that are passed into a function.

From a function's perspective:

  • A parameter is the variable listed inside the parentheses in the function definition.
  • An argument is the value that is sent to the function when it is called.

Number of Arguments

By default, a function must be called with the correct number of arguments. Meaning that if your function expects 2 arguments, you have to call the function with 2 arguments, not more, and not less.

Example

def my_function(fname, lname):
    print(fname + " " + lname)

my_function("Emil", "Refsnes")

Arbitrary Arguments, *args

If you do not know how many arguments that will be passed into your function, add a * before the parameter name in the function definition.

This way the function will receive a tuple of arguments, and can access the items accordingly:

Example

def my_function(*kids):
    print("The youngest child is " + kids[2])

my_function("Emil", "Tobias", "Linus")

Arbitrary Arguments are often shortened to *args in Python documentations.

Keyword Arguments

You can also send arguments with the key = value syntax.

This way the order of the arguments does not matter.

Example

def my_function(child3, child2, child1):
    print("The youngest child is " + child3)

my_function(child1 = "Emil", child2 = "Tobias", child3 = "Linus")

The phrase Keyword Arguments are often shortened to kwargs in Python documentations.

Arbitrary Keyword Arguments, **kwargs

If you do not know how many keyword arguments that will be passed into your function, add two asterisk: ** before the parameter name in the function definition.

This way the function will receive a dictionary of arguments, and can access the items accordingly:

Example

def my_function(**kid):
    print("His last name is " + kid["lname"])

my_function(fname = "Tobias", lname = "Refsnes")

Arbitrary Keyword Arguments are often shortened to **kwargs in Python documentations.

Default Parameter Value

The following example shows how to use a default parameter value.

If we call the function without argument, it uses the default value:

Example

def my_function(country = "Norway"):
    print("I am from " + country)

my_function("Sweden")
my_function("India")
my_function()
my_function("Brazil")

Passing a List as an Argument

You can send any data types of argument to a function (string, number, list, dictionary etc.), and it will be treated as the same data type inside the function.

E.g. if you send a List as an argument, it will still be a List when it reaches the function:

Example

def my_function(food):
    for x in food:
        print(x)

fruits = ["apple", "banana", "cherry"]

my_function(fruits)

Return Values

To let a function return a value, use the return statement:

Example

def my_function(x):
    return 5 * x

print(my_function(3))
print(my_function(5))
print(my_function(9))

The pass Statement

function definitions cannot be empty, but if you for some reason have a function definition with no content, put in the pass statement to avoid getting an error.

Example

def myfunction():
    pass

Recursion

Python also accepts function recursion, which means a defined function can call itself.

Recursion is a common mathematical and programming concept. It means that a function calls itself. This has the benefit of meaning that you can loop through data to reach a result.

Example

def tri_recursion(k):
    if(k > 0):
        result = k + tri_recursion(k - 1)
        print(result)
    else:
        result = 0
    return result

print("\n\nRecursion Example Results")
tri_recursion(6)

Function Examples

Calculator Function

def calculator(operation, a, b):
    if operation == "add":
        return a + b
    elif operation == "subtract":
        return a - b
    elif operation == "multiply":
        return a * b
    elif operation == "divide":
        if b != 0:
            return a / b
        else:
            return "Cannot divide by zero"
    else:
        return "Invalid operation"

print(calculator("add", 10, 5))      # 15
print(calculator("divide", 10, 2))   # 5.0

Function with Multiple Return Values

def get_name_parts(full_name):
    parts = full_name.split()
    first_name = parts[0]
    last_name = parts[-1]
    return first_name, last_name

# Unpack the returned tuple
first, last = get_name_parts("John Doe")
print(f"First: {first}, Last: {last}")

Default, Keyword, and Variable Arguments

def greet(name, greeting="Hello"):     # default value
    return f"{greeting}, {name}!"

print(greet("Sam"))                     # Hello, Sam!
print(greet("Sam", greeting="Hi"))      # keyword argument

def total(*args):        # any number of positional args -> tuple
    return sum(args)
print(total(1, 2, 3))    # 6

def profile(**kwargs):   # any number of keyword args -> dict
    return kwargs
print(profile(age=30, city="NYC"))   # {'age': 30, 'city': 'NYC'}

The Mutable Default Trap

A default argument is created once, when the function is defined. A mutable default (like a list) is shared across calls — a classic bug.

# BUG: the list persists between calls
def add_bad(item, bucket=[]):
    bucket.append(item)
    return bucket

print(add_bad(1))   # [1]
print(add_bad(2))   # [1, 2]  -> not what you expect!

# FIX: use None as the sentinel
def add_ok(item, bucket=None):
    if bucket is None:
        bucket = []
    bucket.append(item)
    return bucket

Never use a list, dict, or set as a default argument value. Default to None and create the object inside the function.

Try It Yourself

Exercise 1: Write area(width, height=1) that returns their product, defaulting height to 1.

Show solution
def area(width, height=1):
    return width * height

print(area(5))       # 5
print(area(5, 3))    # 15

Exercise 2: Write a function that accepts any number of numbers and returns their average.

Show solution
def average(*nums):
    return sum(nums) / len(nums) if nums else 0

print(average(2, 4, 6))   # 4.0

Key Takeaways

  • Define with def; return values with return.
  • Use defaults, keyword args, *args, and **kwargs for flexible signatures.
  • Never use mutable default arguments — default to None.
  • Give functions one clear job and a descriptive name.
← Previous: For LoopsNext: Lambda →

📘 Real-World Deep Dive

Functions are how you turn tribal knowledge into reusable building blocks. Good function design (single purpose, small surface, pure-when-possible) is the cheapest refactoring tool you have.

Real-Life Scenario

A small invoicing pipeline with a pure pricing function, a dirty side-effecting "save" function, and clean composition at the top.

Real-Life Example

from dataclasses import dataclass
from typing import Iterable, Callable

@dataclass
class Line:
    sku: str
    qty: int
    unit_price: float

def line_total(lines: Iterable[Line]) -> float:
    """Pure: same input ⇒ same output. Trivial to test."""
    return sum(l.qty * l.unit_price for l in lines)

def apply_minimum_fee(total: float, minimum: float = 10.0) -> float:
    return max(total, minimum)

def save(path: str, payload: dict) -> None:
    """Side-effecting: writes to disk. Tested with tmp_path fixture."""
    import json, pathlib
    pathlib.Path(path).write_text(json.dumps(payload, indent=2))

def invoice(lines: Iterable[Line], save_to: str | None = None) -> dict:
    raw = line_total(lines)
    gross = apply_minimum_fee(raw)
    payload = {"lines": [l.__dict__ for l in lines], "gross": gross}
    if save_to:
        save(save_to, payload)
    return payload

lines = [Line("A", 2, 9.99), Line("B", 1, 24.50)]
inv = invoice(lines, save_to="out.json")
print(inv)

Expected Output

{'lines': [{'sku': 'A', 'qty': 2, 'unit_price': 9.99}, {'sku': 'B', 'qty': 1, 'unit_price': 24.5}], 'gross': 44.48}

Common mistakes

  • Default arguments are evaluated once — def f(x, seen=[]): seen.append(x) accumulates across calls. Use None and a sentinel.
  • Side effects hidden inside "pure-looking" helpers are the #1 source of test pain.
  • Too many parameters usually means the function wants to be a class or take a config dataclass.

🚀 Performance & Best Practices

  • Local variable lookups are faster than global — bind hot globals to locals inside the function.
  • Return early on invalid input rather than nesting 5 levels deep; flat functions JIT-friendlier too.
  • Use functools.lru_cache on expensive pure functions.

🧪 Try It Yourself

  1. Add an apply_tax(gross, rate) pure function and rewrite invoice() to use it.
  2. Refactor invoice() into a Pipeline class with named steps.
  3. Write three pytest tests for line_total covering empty, one, and many inputs.

FAQ: Python Functions

Common questions about this page.

What is Python Functions?

Python Functions is a Python Tutorial lesson that explains python functions in Python. Learn how to create and use functions in Python to organize and reuse your code. Copy the samples and run them in the Python editor. It is written for beginners who want a clear definition and working examples.

Should I run python functions examples locally for better learning?

Yes. Use the browser editor on StudyGrid for a quick check, then Download the example and run it on your computer. Local runs show real errors and the real toolchain, which is one of the fastest ways to learn python functions in this Python Tutorial Python lesson (Python Functions).

How do I use python functions in Python?

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What is the syntax of python functions?

This Python Functions tutorial shows python functions syntax with short Python examples. Use the code blocks in this lesson for the exact statements, then try them in your editor.

Python Functions example for beginners

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What are common mistakes with python functions?

Common python functions mistakes include wrong syntax, mixing types, and skipping practice. Work through this Python Tutorial chapter in order, run every example, and check the output before moving on.

Why should I learn python functions?

Python Functions is used in real Python work. Learning python functions helps you write clearer programs and continue the Python Tutorial tutorial on StudyGrid.

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