Advanced Python concepts help write:

• Efficient code
• Cleaner programs
• Reusable and optimized solutions

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Iterators in Python

An iterator is an object used to iterate through data one element at a time.

Examples of iterable objects:

• Lists
• Tuples
• Strings
• Dictionaries

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Using Iterator

numbers = [10, 20, 30]

iterator = iter(numbers)

print(next(iterator))
print(next(iterator))
print(next(iterator))

Output:

10
20
30

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StopIteration Exception

When elements finish:

print(next(iterator))

Output:

StopIteration

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Custom Iterator

class Counter:

    def __init__(self, max_value):
        self.max_value = max_value
        self.current = 1

    def __iter__(self):
        return self

    def __next__(self):

        if self.current <= self.max_value:
            value = self.current
            self.current += 1
            return value

        raise StopIteration

counter = Counter(5)

for num in counter:
    print(num)

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Generators in Python

Generators create iterators using yield.

Advantages:

• Memory efficient
• Faster for large data

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Generator Function

def numbers():

    yield 1
    yield 2
    yield 3

gen = numbers()

print(next(gen))
print(next(gen))

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Generator with Loop

def countdown(n):

    while n > 0:
        yield n
        n -= 1

for value in countdown(5):
    print(value)

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Generator Expression

squares = (x * x for x in range(5))

for num in squares:
    print(num)

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Decorators in Python

Decorators modify function behavior without changing original code.

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Basic Decorator

def decorator_function(func):

    def wrapper():
        print("Before function call")

        func()

        print("After function call")

    return wrapper

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Applying Decorator

@decorator_function
def say_hello():
    print("Hello")

say_hello()

Output:

Before function call
Hello
After function call

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Decorator with Arguments

def smart_divide(func):

    def wrapper(a, b):

        if b == 0:
            print("Cannot divide by zero")
            return

        return func(a, b)

    return wrapper

@smart_divide
def divide(a, b):
    print(a / b)

divide(10, 2)
divide(10, 0)

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Context Managers

Context managers manage resources automatically.

Usually used with:

with

statement.

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File Context Manager

with open("data.txt", "r") as file:
    content = file.read()

print(content)

File closes automatically.

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Custom Context Manager

class FileManager:

    def __enter__(self):
        print("File opened")
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        print("File closed")

with FileManager():
    print("Working with file")

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Closures in Python

A closure remembers variables from outer function even after outer function finishes.

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Example

def outer(message):

    def inner():
        print(message)

    return inner

greet = outer("Hello Python")

greet()

Output:

Hello Python

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Comprehensions in Python

Comprehensions provide shorter syntax for creating collections.

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List Comprehension

Syntax

[expression for item in iterable]

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Example

squares = [x * x for x in range(5)]

print(squares)

Output:

[0, 1, 4, 9, 16]

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List Comprehension with Condition

even_numbers = [x for x in range(10) if x % 2 == 0]

print(even_numbers)

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Dictionary Comprehension

Syntax

{key:value for item in iterable}

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Example

squares = {x: x * x for x in range(5)}

print(squares)

Output:

{0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

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Set Comprehension

numbers = {x for x in range(5)}

print(numbers)

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Functional Programming Tools

Python provides built-in functional programming functions.

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1. map()

Applies function to every item.

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Syntax

map(function, iterable)

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Example

numbers = [1, 2, 3, 4]

result = list(map(lambda x: x * 2, numbers))

print(result)

Output:

[2, 4, 6, 8]

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2. filter()

Filters elements based on condition.

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Syntax

filter(function, iterable)

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Example

numbers = [1, 2, 3, 4, 5, 6]

even = list(filter(lambda x: x % 2 == 0, numbers))

print(even)

Output:

[2, 4, 6]

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3. reduce()

Reduces iterable to single value.

Available in:

functools

module.

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Example

from functools import reduce

numbers = [1, 2, 3, 4]

result = reduce(lambda a, b: a + b, numbers)

print(result)

Output:

10

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Comparison of map, filter, and reduce

Function	Purpose
map()	Transform data
filter()	Select data
reduce()	Combine data

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Practical Example

Student Marks Processing

marks = [45, 78, 90, 32, 67]

# Passed students
passed = list(filter(lambda x: x >= 40, marks))

# Add grace marks
updated = list(map(lambda x: x + 5, passed))

print(updated)

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Advantages of Advanced Concepts

✅ Cleaner code
✅ Better performance
✅ Reusable components
✅ Memory optimization
✅ Functional programming support