Master Python Functions: Definitions, Calls, Parameters, and Advanced Techniques

Python Functions

Functions are organized, reusable blocks of code that perform a specific task. They improve modularity and code reuse. Python offers many built‑in functions (e.g., print()) and allows you to create user‑defined functions.

1. Defining Functions

A function definition starts with the def keyword, followed by the function name and parentheses containing any parameters. The body follows a colon and is indented.

def function_name(parameters):
    # function body
    return [expression]

2. Calling Functions

After defining a function, you can invoke it by using its name followed by arguments in parentheses.

def add(x, y):
    print("{0} + {1} = {2}".format(x, y, x + y))
    return x + y

add(2, 3)

3. Function Parameters

Parameters listed in the definition are formal parameters; the values supplied at call time are actual arguments. Python supports default values, positional arguments, keyword arguments, and variable‑length arguments ( *args for tuples and **kwargs for dictionaries).

# Default parameters
def fun(x, y=100):
    print(x, y)

fun(1)          # y uses default 100
fun(1, 2)       # overrides default

4. Return Values

A function can return a value using return. If no value is specified, None is returned. Execution stops after return.

def greet():
    print('hello world')
    return 'ok'
    print(123)   # never executed

result = greet()
print(result)   # prints 'ok'

5. Variables and Scope

Variables defined inside a function are local. Variables defined at the module level are global. Use the global statement to modify a global variable from within a function.

x = 100

def inc():
    global x
    x += 1
    print(x)

inc()   # prints 101
print(x)  # also 101

6. Anonymous (Lambda) Functions

Lambda functions are single‑expression anonymous functions useful for short, inline operations.

multiply = lambda x, y: x * y
print(multiply(3, 4))  # 12

7. Higher‑Order Functions

Functions that take other functions as arguments or return them.

7.1 map()

Applies a function to each item of an iterable and returns a list of results.

def square(x):
    return x * x

for i in map(square, [1,2,3,4,5,6,7]):
    print(i)

7.2 reduce()

Reduces an iterable to a single value by repeatedly applying a binary function.

from functools import reduce

def add(x, y):
    return x + y

print(reduce(add, [1,2,3,4,5,6,7,8,9,10]))  # 55
print(reduce(add, range(1, 101)))           # 5050

7.3 filter()

Filters items of an iterable using a predicate function.

for i in filter(lambda x: x < 7, [1,2,3,4,5,40,8]):
    print(i)

7.4 sorted()

Returns a new sorted list from the items of any iterable.

m = {'a':1, 'c':10, 'b':20, 'd':15}
print(sorted(m.items(), key=lambda d: d[1], reverse=True))
# [('b', 20), ('d', 15), ('c', 10), ('a', 1)]