Section 1.1: Generators

Generators provide a unique method for creating iterators in Python, enabling the generation of values on-the-fly without storing the entire sequence in memory. To define a generator, use the yield statement within a function. Below is a simple example:

def simple_generator():

yield 1

yield 2

yield 3

gen = simple_generator()

for value in gen:

print(value)

Section 1.2: Decorators

Decorators serve to alter the functionality of a function or method. You can create your own decorator by using the @decorator_name syntax. Here’s a basic illustration:

def my_decorator(func):

def wrapper():

print("Something is happening before the function is called.")

func()

print("Something is happening after the function is called.")

return wrapper

@my_decorator

def say_hello():

print("Hello!")

say_hello()

Subsection 1.2.1: Essential Python Concepts

Section 1.3: Context Managers

Context managers are crucial for resource management, implemented using the __enter__ and __exit__ methods. They are particularly effective for managing files and databases. Here’s a file handling example:

with open('example.txt', 'r') as file:

data = file.read()

# Process data as needed

Section 1.4: List Comprehensions

List comprehensions offer a succinct way to construct lists. For instance, here's how to create a list of squares:

squares = [x**2 for x in range(10)]

Section 2.1: Lambda Functions

Lambda functions are compact, unnamed functions defined using the lambda keyword, ideal for short-lived operations. Here’s a straightforward example:

double = lambda x: x * 2

print(double(5))

Section 2.2: Recursion

Recursion occurs when a function calls itself to resolve a problem. Below is a classic example that calculates the factorial of a number:

def factorial(n):

if n == 1:

return 1

else:

return n * factorial(n-1)

Section 2.3: Regular Expressions

Regular expressions are powerful for identifying patterns in text. For instance, to check if an email address is valid, you can use the following code:

import re

pattern = r'^[w.-]+@[w.-]+$'

email = '[email protected]'

if re.match(pattern, email):

print("Valid email")

else:

print("Invalid email")

Section 2.4: Concurrency with Threading

Threading allows multiple threads to run concurrently, improving efficiency. Here’s a simple demonstration:

import threading

def print_numbers():

for i in range(1, 6):

print(f"Number {i}")

def print_letters():

for letter in 'abcde':

print(f"Letter {letter}")

thread1 = threading.Thread(target=print_numbers)

thread2 = threading.Thread(target=print_letters)

thread1.start()

thread2.start()

Section 2.5: Object-Oriented Programming (OOP)

OOP is a core principle in Python programming. Below is a basic example of defining a class:

class Dog:

def __init__(self, name, breed):

self.name = name

self.breed = breed

def bark(self):

print(f"{self.name} says Woof!")

my_dog = Dog("Buddy", "Golden Retriever")

my_dog.bark()

Section 2.6: Virtual Environments

Virtual environments are essential for managing project dependencies. Here’s how to create and activate one:

python -m venv myenv

source myenv/bin/activate # On Windows, use myenvScriptsactivate