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In Python, Object-Oriented Programming (OOP) allows you to define classes and create objects with attributes and methods. Classes are blueprints for creating objects, and they support key concepts like inheritance, encapsulation, polymorphism, and abstraction.

class Animal:
def __init__(self, name):
self.name = name

def speak(self):
return f"{self.name} makes a sound"

class Dog(Animal):
def speak(self):
return f"{self.name} says Woof!"

class Cat(Animal):
def speak(self):
return f"{self.name} says Meow!"

# Creating instances
dog = Dog("Buddy")
cat = Cat("Whiskers")

print(dog.speak()) # Output: Buddy says Woof!
print(cat.speak()) # Output: Whiskers says Meow!

#Python #OOP #Classes #Inheritance #Polymorphism #Encapsulation #Programming #ObjectOriented #PythonTips #CodeExamples

By: @DataScienceQ 🚀
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In Python, a list comprehension is a concise and elegant way to create lists. It allows you to generate a new list by applying an expression to each item in an existing iterable (like a list or range), often in a single line of code, making it more readable and compact than a traditional for loop.

# Traditional way using a for loop
squares_loop = []
for i in range(10):
    squares_loop.append(i i)

print(f"Using a loop: {squares_loop}")

The Pythonic way using a list comprehension

squares_comp = [i i for i in range(10)]

print(f"Using comprehension: {squares_comp}")

You can also add conditions

even_squares = [i * i for i in range(10) if i % 2 == 0]
print(f"Even squares only: {even_squares}")

Both the loop and the basic list comprehension produce the exact same result: a list of the first 10 square numbers. However, the list comprehension is more efficient and easier to read once you are familiar with the syntax.

#Python #ListComprehension #PythonTips #CodeExamples #Programming #Pythonic #Developer #Code

By: @DataScienceQ 🩵
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In Python, "Magic Methods" (also known as Dunder methods, short for "double underscore") are special methods that allow you to define how objects of your class behave with built-in functions and operators. While init handles object initialization, str and repr are crucial for defining an object's string representation.

str: Returns a "user-friendly" string representation of an object, primarily for human readability (e.g., when print() is called).
repr: Returns an "official" string representation of an object, primarily for developers, often aiming to be unambiguous and allow recreation of the object.

class Book:
def init(self, title, author, year):
self.title = title
self.author = author
self.year = year

def str(self):
return f'"{self.title}" by {self.author} ({self.year})'

def repr(self):
return f"Book('{self.title}', '{self.author}', {self.year})"

Creating an instance

my_book = Book("The Hitchhiker's Guide to the Galaxy", "Douglas Adams", 1979)

str is used by print()

print(my_book)

repr is used by the interpreter or explicitly with repr()

print(repr(my_book))

In collections, repr is used by default

bookshelf = [my_book, Book("Pride and Prejudice", "Jane Austen", 1813)]
print(bookshelf)

Output:
"The Hitchhiker's Guide to the Galaxy" by Douglas Adams (1979)
Book('The Hitchhiker\'s Guide to the Galaxy', 'Douglas Adams', 1979)
[Book('The Hitchhiker\'s Guide to the Galaxy', 'Douglas Adams', 1979), Book('Pride and Prejudice', 'Jane Austen', 1813)]

#Python #MagicMethods #DunderMethods #OOP #Classes #PythonTips #CodeExamples #StringRepresentation #ObjectOrientation #Programming

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By: @DataScienceQ