GitHub - hamedasgari20/Python-Django-FastAPI-advanced-topics: Python and Django advanced topics

Introduction

In this research, I have given a brief overview of advanced topics in Python and Django. This article does not provide in-depth knowledge on various topics and the purpose of this research is to collect topics in one place to provide the reader with a mental framework. In this article, I have used very simple examples to explain the topics easily. Naturally, studying and examining more and more practical examples will help a lot to understand each topic. I hope reading this article is useful for you. (Hamid Asgari)

I am grateful to all my friends who have guided me in writing this article: Alireza Amouzadeh , Zahra Rezaei, Shokooh Rigi, Saharnaz Rashidi, Ali Emamalinejad , Malihe Sheidaiefar, Ali Lavasani, Fatemeh Pasandideh

Introduction
Python related topics:
Django related topics:
FastAPI related topics:

Python related topics:

object-oriented programming (OOP)

Here are some of the most important topics in object-oriented programming (OOP) in Python, along with simple examples of each:

Inheritance

Inheritance is a mechanism that allows a class to inherit properties and methods from another class. The class that inherits from another class is called a subclass or derived class, while the class that is being inherited from is called a superclass or base class.

from abc import ABC, abstractmethod

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

    @abstractmethod
    def speak(self):
        pass

class Dog(Animal):
    def speak(self):
        return "Woof!"

class Cat(Animal):
    def speak(self):
        return "Meow!"

dog = Dog("Fido")
print(dog.name)  # Output: Fido
print(dog.speak())  # Output: Woof!

cat = Cat("Whiskers")
print(cat.name)  # Output: Whiskers
print(cat.speak())  # Output: Meow!

In this example, Animal is the superclass, and Dog and Cat are subclasses that inherit from it. The speak method is an abstract method in the Animal class that is implemented in the subclasses. We import the ABC (Abstract Base Class) class from the abc module and use the @abstractmethod decorator to mark the speak method as an abstract method in the Animal class. This enforces that subclasses of Animal must provide an implementation for the speak method.

Polymorphism

Polymorphism is the ability of objects to take on different forms or perform different actions depending on the context. In Python, polymorphism is achieved through method overriding and method overloading.

from abc import ABC, abstractmethod
import math

class Shape(ABC):
    @abstractmethod
    def area(self):
        pass

class Rectangle(Shape):
    def __init__(self, width, height):
        self.width = width
        self.height = height

    def area(self):
        return self.width * self.height

class Circle(Shape):
    def __init__(self, radius):
        self.radius = radius

    def area(self):
        return math.pi * self.radius ** 2

shapes = [Rectangle(5, 10), Circle(7)]
for shape in shapes:
    print(shape.area())

In this example, Shape is an abstract class that defines the area method as an abstract method. Rectangle and Circle are concrete subclasses that implement the area method. The shapes list contains instances of both Rectangle and Circle, and the area method is called on each instance, demonstrating polymorphism.

In this code, we use the ABC class and the @abstractmethod decorator to define Shape as an abstract base class with an abstract area method. This ensures that subclasses of Shape must implement the area method. The math.pi constant is used for more accurate calculations of the circle's area.

Encapsulation

Encapsulation is the practice of hiding the internal details of an object and providing a public interface for interacting with it. In Python, encapsulation is achieved through the use of public and private methods and attributes.

class BankAccount:
    def __init__(self, balance):
        self._balance = balance

    def deposit(self, amount):
        self._balance += amount

    def withdraw(self, amount):
        if self._balance >= amount:
            self._balance -= amount
        else:
            raise ValueError("Insufficient balance")

    def get_balance(self):
        return self._balance


account = BankAccount(1000)
account.deposit(500)
account.withdraw(200)
print(account.get_balance())  # Output: 1300

In this example, BankAccount is a class that represents a bank account. The _balance attribute is marked as private by convention (using a single underscore), and can only be accessed through public methods such as deposit, withdraw, and get_balance.

Abstraction

Abstraction is the process of simplifying complex systems by breaking them down into smaller, more manageable parts. In Python, abstraction is achieved through the use of abstract classes and interfaces.

from abc import ABC, abstractmethod

class Shape(ABC):
    @abstractmethod
    def area(self):
        pass


class Rectangle(Shape):
    def __init__(self, width, height):
        self.width = width
        self.height = height

    def area(self):
        return self.width * self.height


shapes = [Rectangle(5, 10)]
for shape in shapes:
    print(shape.area())

In this example, Shape is an abstract class that defines the area method as an abstract method. Rectangle is a concrete subclass that implements the area method. The shapes list contains an instance of Rectangle, demonstrating abstraction.

Decorators

In Python, decorators are a way to modify the behavior of functions or classes by wrapping them with other functions. Here's a simple example to demonstrate the basic concept of decorators:

def decorator_function(original_function):
    def wrapper_function():
        print("Before the original function is called.")
        original_function()
        print("After the original function is called.")
    return wrapper_function

@decorator_function
def greet():
    print("Hello, world!")

greet()

out put

Before the original function is called.
Hello, world!
After the original function is called.

Here is an example of how to implement the previous decorator in a class-based way:

class DecoratorClass:
    def __init__(self, original_function):
        self.original_function = original_function

    def __call__(self):
        print("Before the original function is called.")
        self.original_function()
        print("After the original function is called.")

@DecoratorClass
def greet():
    print("Hello, world!")

greet()

In this example, the DecoratorClass is a class-based decorator that takes the original function as an argument in its constructor. The call method is used to define the behavior of the decorator when the decorated function is called.

More decorators in Python

In Python, class decorators are functions or callable objects that are used to modify or enhance the behavior of a class. They are applied to the class definition itself and can add or modify class-level attributes, methods, or perform other operations on the class. Here are a few examples of class decorators:

classmethod

The classmethod decorator is a built-in decorator that transforms a method into a class method. Class methods can be called on the class itself, rather than on instances of the class. Here's a simple example of a class method in Python

class Car:
    total_cars = 0  # Class variable to keep track of the total number of cars

    def __init__(self, name):
        self.name = name
        Car.total_cars += 1

    def get_name(self):
        return self.name

    @classmethod
    def get_total_cars(cls):
        return cls.total_cars


car1 = Car("Toyota")
car2 = Car("Honda")
car3 = Car("Ford")

print(car1.get_name())  # Output: "Toyota"
print(Car.get_total_cars())  # Output: 3

In this example, we have a Car class with a class variable total_cars, which keeps track of the total number of car objects created. The init method increments total_cars by 1 whenever a new car object is created.

staticmethod

The staticmethod decorator is another built-in decorator that transforms a method into a static method. Static methods are similar to regular functions and do not have access to the class or instance. Here's a simple example of a static method in Python:

class MathUtils:
    @staticmethod
    def add_numbers(x, y):
        return x + y

    @staticmethod
    def multiply_numbers(x, y):
        return x * y


sum_result = MathUtils.add_numbers(5, 3)
print(sum_result)  # Output: 8

product_result = MathUtils.multiply_numbers(4, 2)
print(product_result)  # Output: 8

Static methods are defined using the @staticmethod decorator. They don't receive any special first parameter like instance methods or class methods. They behave like regular functions defined within the class.

property

The property decorator allows you to define methods that can be accessed like attributes, providing a way to implement computed or dynamic properties.

class Circle:
    def __init__(self, radius):
        self.radius = radius

    @property
    def diameter(self):
        return self.radius * 2

    @property
    def area(self):
        return 3.14 * self.radius**2

# Creating an instance of Circle
my_circle = Circle(5)

print(my_circle.diameter)  # Output: 10
print(my_circle.area)  # Output: 78.5

In this example, we use the property decorator to define the diameter and area methods.

abstractmethod

The abstractmethod decorator is used in combination with the ABC module to define abstract methods in abstract base classes The abstractmethod decorator ensures that any subclass of super class must implement this method.

from abc import ABC, abstractmethod

class Shape(ABC):
    @abstractmethod
    def calculate_area(self):
        pass

class Rectangle(Shape):
    def __init__(self, width, height):
        self.width = width
        self.height = height

    def calculate_area(self):
        return self.width * self.height

# Creating an instance of Rectangle
my_rectangle = Rectangle(5, 10)

print(my_rectangle.calculate_area())  # Output: 50

The abstractmethod decorator ensures that any subclass of Shape must implement this method.

dataclass

The dataclass decorator is available in the dataclasses module (introduced in Python 3.7) and provides a concise way to define classes that are primarily used to store data.

from dataclasses import dataclass

@dataclass
class Person:
    name: str
    age: int

# Creating an instance of Person
person = Person("Alice", 25)

print(person.name)  # Output: Alice
print(person.age)   # Output: 25

In this example, we apply the @dataclass decorator to the Person class. The decorator automatically generates special methods like init, repr, and eq based on the class variables (name and age in this case). The dataclass decorator simplifies the process of defining classes that are primarily used for holding data, reducing the amount of boilerplate code required.

cached_property

The cached_property decorator is available in third-party libraries such as django.utils.functional and cachetools and provides a way to cache the result of a method as a property, improving performance when the method is called multiple times.

from django.utils.functional import cached_property

class Square:
    def __init__(self, side):
        self.side = side

    @cached_property
    def area(self):
        print("Calculating area...")
        return self.side**2

# Creating an instance of Square
my_square = Square(5)

print(my_square.area)  # Output: Calculating area... 25
print(my_square.area)  # Output: 25 (Cached result, no recalculation)

In this example, we apply the @cached_property decorator to the area method. The decorator caches the result of the method on the instance, so subsequent accesses to area return the cached value without recalculating it.

Map function

The map() function in Python is a built-in function that applies a given function to each item in an iterable (such as a list, tuple, or string) and returns an iterator with the results. It takes two or more arguments: the function to apply and one or more iterables. The basic syntax of the map() function is as follows:

map(function, iterable1, iterable2, ...)

Here's an example that demonstrates the usage of the map() function:

# Example 1: Squaring numbers using map()
numbers = [1, 2, 3, 4, 5]
squared = map(lambda x: x**2, numbers)
print(list(squared))
# Output: [1, 4, 9, 16, 25]

Itertools

Itertools is a Python module that provides a collection of functions for creating and manipulating iterators, which are objects that can be iterated (looped) over. Here are some commonly used functions provided by the itertools module

count(): Generates an infinite sequence of numbers starting from a specified value.
chain(): Combines multiple iterators into a single iterator.
groupby(): Groups consecutive elements in an iterable based on a common key
cycle(): cycles through the elements of an iterable object infinitely
combinations(): generates all possible combinations of a given iterable object

example of cycle():

import itertools

colors = ['red', 'green', 'blue']
color_cycle = itertools.cycle(colors)

for _ in range(5):
    print(next(color_cycle))

out put:

red
green
blue
red
green

example of combinations():

import itertools

numbers = [1, 2, 3]
combinations = itertools.combinations(numbers, 2)

for combination in combinations:
    print(combination)

output:

(1, 2)
(1, 3)
(2, 3)

Here are a few examples of how you can utilize itertools in the context of Django. In the following example, we use itertools.chain() to combine the querysets of posts and comments into a single iterable. We then sort the combined results based on the creation date, using sorted().

import itertools
from myapp.models import Post, Comment

posts = Post.objects.filter(published=True)
comments = Comment.objects.filter(approved=True)

combined_results = itertools.chain(posts, comments)
sorted_results = sorted(combined_results, key=lambda item: item.created_at, reverse=True)

Lambda function

In Python, a lambda function is a small anonymous function that can be defined without a name. It is also known as an inline function or a lambda expression. Lambda functions are typically used when you need a simple function that will be used only once or as a parameter to another function. The general syntax of a lambda function in Python is:

lambda arguments: expression

For example, let's say we want to create a lambda function that takes two arguments and returns their sum:

add = lambda x, y: x + y
result = add(3, 5)
print(result)  # Output: 8

Lambda functions are often used with built-in functions like map(), filter(), and reduce(). These functions take another function as a parameter, and lambda functions provide a convenient way to define these functions on the fly without explicitly defining a separate function. Here's an example using the map() function with a lambda function to square a list of numbers:

numbers = [1, 2, 3, 4, 5]
squared_numbers = list(map(lambda x: x ** 2, numbers))
print(squared_numbers)  # Output: [1, 4, 9, 16, 25]

For more complex or reusable functions, it's often better to define a regular named function using the def keyword.

Exception Handling

Exception handling in Python allows you to gracefully handle and recover from runtime errors or exceptional conditions that may occur during the execution of your program. Here's an example that demonstrates the usage of exception handling:

try:
    # Code that may raise an exception
    x = 10 / 0  # division by zero raises a ZeroDivisionError
except ZeroDivisionError:
    # Code to handle ZeroDivisionError
    print("Division by zero is not allowed.")


# Output:
# Division by zero is not allowed.

SOLID principles

The SOLID principles are a set of design principles that help in creating maintainable, scalable, and flexible software systems.

Single Responsibility Principle (SRP):

This principle states that a class should have only one responsibility. Let's say we have a User class that handles both user authentication and user data management. Instead, we can separate these responsibilities into two classes: Authenticator and UserManager. Here's a Python example:

class Authenticator:
    def authenticate(self, username, password):
        # Authentication logic here

class UserManager:
    def create_user(self, user_data):
        # User creation logic here

    def delete_user(self, user_id):
        # User deletion logic here

Open/Closed Principle (OCP):

This principle states that software entities (classes, modules, functions) should be open for extension but closed for modification. In other words, you should be able to add new functionality without modifying existing code. Let's say we have a Shape class with different subclasses such as Circle and Rectangle. Instead of modifying the Shape class every time we want to add a new shape, we can use inheritance and polymorphism to extend the functionality:

class Shape:
    def area(self):
        raise NotImplementedError()

class Circle(Shape):
    def __init__(self, radius):
        self.radius = radius

    def area(self):
        return 3.14 * self.radius * self.radius

class Rectangle(Shape):
    def __init__(self, width, height):
        self.width = width
        self.height = height

    def area(self):
        return self.width * self.height

Liskov Substitution Principle (LSP):

This principle states that objects of a superclass should be replaceable with objects of its subclasses without affecting the correctness of the program. Let's say we have a function that expects a Shape object. According to LSP, we should be able to pass any subclass of Shape without causing any issues. In simpler terms, it means that a subclass should be able to be used wherever its superclass is expected, without causing any issues or breaking the functionality of the program.

Here's a simple Python example to illustrate the Liskov Substitution Principle:

class Shape:
    def area(self):
        pass

class Rectangle(Shape):
    def __init__(self, width, height):
        self.width = width
        self.height = height

    def area(self):
        return self.width * self.height

class Square(Shape):
    def __init__(self, side):
        self.side = side

    def area(self):
        return self.side * self.side

In this example, we have a superclass called Shape with a method area() that calculates the area of a shape. We then have two subclasses, Rectangle and Square, that inherit from the Shape class and override the area() method to calculate the area specific to each shape. According to the Liskov Substitution Principle, we should be able to use objects of the Rectangle and Square classes interchangeably with objects of the Shape class. For example:

def print_area(shape):
    print(f"The area is: {shape.area()}")

rectangle = Rectangle(4, 5)
square = Square(4)

print_area(rectangle)  # Output: The area is: 20
print_area(square)  # Output: The area is: 16

In this example, we can see that both the Rectangle and Square objects can be passed to the print_area() function, which expects an object of the Shape class. This demonstrates the Liskov Substitution Principle, as the subclasses can be used in place of the superclass without any issues or breaking the functionality of the program.

Interface Segregation Principle (ISP):

This principle states that clients should not be forced to depend on interfaces they do not use. It promotes the idea of having smaller, focused interfaces rather than large, general-purpose ones. Let's say we have an Animal interface with methods like walk(), swim(), and fly(). Instead of having a single interface Animal, we can split it into smaller interfaces based on functionality:

class Walker:
    def walk(self):
        raise NotImplementedError()

class Swimmer:
    def swim(self):
        raise NotImplementedError()

class Flyer:
    def fly(self):
        raise NotImplementedError()

class Dog(Walker):
    def walk(self):
        print("Dog is walking")

class Fish(Swimmer):
    def swim(self):
        print("Fish is swimming")

Dependency Inversion Principle (DIP):

This principle states that high-level modules should not depend on low-level modules. Both should depend on abstractions. It encourages the use of interfaces or abstract classes to decouple dependencies. . This helps to decouple the high-level and low-level modules, making it easier to change the low-level ones without affecting the high-level ones

Python collection

Python provides several built-in collection types, such as lists, tuples, sets, and dictionaries. These collections can be used to store and organize data efficiently.

lists:

A list is a mutable collection that can store an ordered sequence of elements. It is defined using square brackets ([]). Here's an example:

# Creating a list
fruits = ['apple', 'banana', 'orange']

# Accessing elements
print(fruits[0])  # Output: apple

# Modifying elements
fruits[1] = 'kiwi'
print(fruits)  # Output: ['apple', 'kiwi', 'orange']

# Adding elements
fruits.append('grape')
print(fruits)  # Output: ['apple', 'kiwi', 'orange', 'grape']

# Removing elements
fruits.remove('kiwi')
print(fruits)  # Output: ['apple', 'orange', 'grape']

Tuples:

A tuple is an immutable collection that can store an ordered sequence of elements. It is defined using parentheses (). Here's an example:

# Creating a tuple
point = (3, 4)

# Accessing elements
print(point[0])  # Output: 3

# Unpacking tuple
x, y = point
print(x, y)  # Output: 3 4

Sets:

A set is an unordered collection that stores unique elements. It is defined using curly braces ({}) or the set() function. Here's an example:

# Creating a set
numbers = {1, 2, 3, 4}

# Adding elements
numbers.add(5)
print(numbers)  # Output: {1, 2, 3, 4, 5}

# Removing elements
numbers.remove(2)
print(numbers)  # Output: {1, 3, 4, 5}

Dictionaries:

A dictionary is a collection that stores key-value pairs. It is defined using curly braces ({}) and colons (:). Here's an example:

# Creating a dictionary
student = {
    'name': 'Alice',
    'age': 20,
    'university': 'XYZ'
}

# Accessing values
print(student['name'])  # Output: Alice

# Modifying values
student['age'] = 21
print(student)  # Output: {'name': 'Alice', 'age': 21, 'university': 'XYZ'}

# Adding new key-value pair
student['major'] = 'Computer Science'
print(student)  # Output: {'name': 'Alice', 'age': 21, 'university': 'XYZ', 'major': 'Computer Science'}

# Removing key-value pair
del student['university']
print(student)  # Output: {'name': 'Alice', 'age': 21, 'major': 'Computer Science'}

# Updating a field
student.update({'age': 21})

frozenset in Python

In Python, a frozenset is an immutable (unchangeable) version of the built-in set type. It is an unordered collection of unique elements, just like a regular set, but it cannot be modified once created. This means you cannot add, remove, or modify elements in a frozenset after it is created. Here's an example of creating and utilizing a frozenset:

# Create a frozenset
numbers = frozenset([1, 2, 3, 4, 5])

# Accessing elements
for number in numbers:
    print(number)

# Frozenset operations
other_numbers = frozenset([4, 5, 6, 7, 8])

# Union
union = numbers.union(other_numbers)
print(union)  # Output: frozenset({1, 2, 3, 4, 5, 6, 7, 8})

# Intersection
intersection = numbers.intersection(other_numbers)
print(intersection)  # Output: frozenset({4, 5})

# Difference
difference = numbers.difference(other_numbers)
print(difference)  # Output: frozenset({1, 2, 3})

# Subset check
is_subset = numbers.issubset(other_numbers)
print(is_subset)  # Output: False

Generators and Iterators

Generators and iterators are powerful constructs in Python used for efficient iteration and lazy evaluation. They provide a way to generate or yield values on the fly, enabling you to work with large or infinite sequences without loading everything into memory at once.

Lazy file reading using generators:

def read_lines(filename):
    with open(filename, 'r') as file:
        for line in file:
            yield line.strip()

lines = read_lines('large_file.txt')
for line in lines:
    print(line)

Here, the read_lines() function returns a generator that yields one line at a time. This approach is memory-efficient and allows you to process large files without loading the entire contents into memory. Here is the implementation of the previous example with custom iterator:

class LineIterator:
    def __init__(self, filename):
        self.filename = filename
        self.file = None

    def __iter__(self):
        self.file = open(self.filename, 'r')
        return self

    def __next__(self):
        line = self.file.readline().strip()
        if not line:
            self.file.close()
            raise StopIteration
        return line

lines = LineIterator('large_file.txt')
for line in lines:
    print(line)

The main advantage of the generator approach is its simplicity and conciseness. The generator function hides most of the implementation details required for iterators, resulting in cleaner code. On the other hand, the iterator approach provides more control and flexibility, allowing for more complex iterator implementations beyond what generators can offer.

Ultimately, the choice between generators and iterators depends on the specific requirements of your program. Generators are often the preferred choice for simpler iterations and lazy evaluation, while iterators offer more customization and control when dealing with complex iteration scenarios.

Magic methods

Magic methods, also known as dunder methods, are special methods in Python that start and end with double underscores. They are not meant to be invoked directly by the user, but are called internally by the class on certain actions. Here are some examples of commonly used magic methods in Python:

__new__(self): It is a static method that is called before the __init__ method when creating an object. The primary purpose of new is to handle the object construction process and return an instance of the class.

__init__(self, ...) : This method is called when an object is created and initialized. It takes arguments that are used to initialize the object's attributes.

__str__(self) : This method is called when the object is printed as a string. It returns a string representation of the object.

__len__(self) : This method is called when the built-in len() function is called on the object. It returns the length of the object.

__add__(self, other) : This method is called when the + operator is used on the object. It takes another object as an argument and returns the result of the addition.

__call__(self, other): The method in Python is a special method that allows an object to be called like a function

Here's an example of a class that defines some of these magic methods:

class Person:
   def __init__(self, name, age):
       self.name = name
       self.age = age

   def __str__(self):
       return f"{self.name} is {self.age} years old."

   def __repr__(self):
       return f"Person('{self.name}', {self.age})"

   def __eq__(self, other):
       return self.name == other.name and self.age == other.age

Here is a simple example that demonstrates the difference between str and repr:

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def __str__(self):
        return f"{self.name} ({self.age})"

    def __repr__(self):
        return f"Person(name='{self.name}', age={self.age})"

person = Person("Alice", 30)
print(str(person))    # Output: Alice (30)
print(repr(person))   # Output: Person(name='Alice', age=30)

In this example, we define a Person class that has a name and an age attribute. We define both str and repr methods for the class. The str method returns a string that is intended to be readable by humans, while the repr method returns a string that is intended to be unambiguous and can be used to recreate the object. When we create a Person object and print it using the str() and repr() functions, we get different outputs. The str() function calls the str method, which returns a user-friendly string representation of the object. The repr() function calls the repr method, which returns a developer-friendly string representation of the object.

GIL

The Global Interpreter Lock is a mechanism in CPython (the most common implementation of Python) that serves to serialize access to Python objects, preventing multiple threads from executing Python bytecodes at once. In simple words, the GIL is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter.This means that only one thread can be in a state of execution at any point in time.

concurrency and parallelism

Concurrency involves allowing multiple tasks to take turns accessing the same shared resources, like disk, network, or a single CPU core. Parallelism, on the other hand, is about maximizing the use of hardware resources, such as multiple CPU cores, to execute multiple tasks simultaneously. Threading is a way to achieve concurrency by running multiple threads within a single process, while asyncio is a way to achieve concurrency by running a single thread that can switch between multiple tasks.

Threading:

The following code snippet demonstrates how to use threading to execute multiple tasks concurrently within a single process.

import threading

def task1():
    # do some work

def task2():
    # do some work

# create two threads to execute the tasks concurrently
t1 = threading.Thread(target=task1)
t2 = threading.Thread(target=task2)

# start the threads
t1.start()
t2.start()

# wait for the threads to finish
t1.join()
t2.join()

Asyncio:

The following code snippet demonstrates how to use asyncio to execute multiple tasks concurrently within a single thread:

import asyncio

async def task1():
    # do some work

async def task2():
    # do some work

# create an event loop
loop = asyncio.get_event_loop()

# execute the tasks concurrently within the event loop
loop.run_until_complete(asyncio.gather(task1(), task2()))

# close the event loop
loop.close()

Multiprocessing:

The following code snippet demonstrates how to use multiprocessing to execute multiple tasks in parallel across multiple processes:

import multiprocessing

def task1():
    # do some work

def task2():
    # do some work

# create two processes to execute the tasks in parallel
p1 = multiprocessing.Process(target=task1)
p2 = multiprocessing.Process(target=task2)

# start the processes
p1.start()
p2.start()

# wait for the processes to finish
p1.join()
p2.join()

In general, concurrency is preferred for IO-bound tasks, as it allows you to do something else while the IO resources are being fetched. Parallelism, on the other hand, is preferred for CPU-bound tasks, as it allows you to take advantage of multiple CPU cores to execute multiple tasks simultaneously.

Main types of methods in python classes

In Python, there are three main types of methods that can be defined in a class:

Instance Methods: Instance methods are the most common type of methods used in Python classes. They are defined using the self parameter as the first argument. Instance methods can access and modify the instance attributes of the class.

class MyClass:
    def my_instance_method(self, x, y):
        self.x = x
        self.y = y
        return self.x + self.y

my_object = MyClass()
result = my_object.my_instance_method(3, 4)
print(result)  # Output: 7

Class Methods: Class methods are methods that are bound to the class and not the instance of the class. They are defined using the * @classmethod decorator, and they take the class itself as their first argument, typically named cls. Class methods can be called on the class itself, rather than on an instance of the class.

class MyClass:
    class_attribute = 0

    @classmethod
    def my_class_method(cls, x, y):
        cls.class_attribute += 1
        return cls.class_attribute + x + y

result1 = MyClass.my_class_method(3, 4)
print(result1)  # Output: 1 + 3 + 4 = 8

result2 = MyClass.my_class_method(1, 2)
print(result2)  # Output: 2 + 1 + 2 = 5

Static Methods: Static methods are methods that don't depend on the class or instance. They are defined using the @staticmethod decorator and take no special first argument. Static methods are typically used for utility functions that don't require access to the class or instance.

class MyClass:
    @staticmethod
    def my_static_method(x, y):
        return x + y

result = MyClass.my_static_method(3, 4)
print(result)  # Output: 7

Data serialization

Data serialization is the process of converting structured data into a format that allows sharing or storage of the data in a form that allows recovery of its original structure. In Python, there are several built-in modules for data serialization, including pickle, json, and marshal. Note that the marshal module is not suitable for serializing data that needs to be exchanged between different programming languages or platforms, as the binary format is specific to Python. For that purpose, you may want to consider using a different serialization library such as JSON, pickle, or protobuf.

Here's an example of using the pickle module to serialize and deserialize a Python object:

import pickle

# Define a Python object
grades = {'Alice': 89, 'Bob': 72, 'Charles': 87}

# Serialize the object to a byte stream
serial_grades = pickle.dumps(grades)

# Deserialize the byte stream back into a Python object
received_grades = pickle.loads(serial_grades)

# Print the original and received objects
print('Original object:', grades)
print('Serialized object:', serial_grades)
print('Received object:', received_grades)

# Out put
# Original object: {'Alice': 89, 'Bob': 72, 'Charles': 87}
# Serialized object: b'\x80\x04\x95#\x00\x00\x00\x00\x00\x00\x00}\x94(\x8c\x05Alice\x94KY\x8c\x03Bob\x94KH\x8c\x07Charles\x94KWu.'
# Received object: {'Alice': 89, 'Bob': 72, 'Charles': 87}

In this example, we define a Python dictionary grades and serialize it using the pickle.dumps() function. We then deserialize the byte stream back into a Python object using the pickle.loads() function and print the original and received objects.

Data class in python

Overall, data classes provide a convenient way to define classes that mainly hold data values, without having to write boilerplate code for initialization, representation, and comparisons. A data class is a class that is designed to only hold data values. It is similar to a regular class, but it usually doesn't have any other methods. It is typically used to store information that will be passed between different parts of a program or a system. Here's a simple example of a data class in Python:

from dataclasses import dataclass

@dataclass
class Person:
    name: str
    age: int

In the above example, the @dataclass decorator is used to create a data class called Person. The class has two attributes, name and age, which are defined using type annotations. The dataclass decorator automatically generates several special methods such as init(), repr(), and eq() for the class.

To add custom validation to the example using dataclasses, you can define a custom post_init method that runs after the object is initialized. Within this method, you can perform your custom validation logic. Here's how you can do it:

from dataclasses import dataclass

@dataclass
class Person:
    name: str
    age: int

    def __post_init__(self):
        if self.age < 18:
            raise ValueError("Age must be greater than or equal to 18")

Shallow copy and deep copy

In Python, there are two types of copying: shallow copy and deep copy. A shallow copy creates a new object that stores references to the child objects of the original object. In contrast, a deep copy creates a new object that is completely independent of the original object. To create a shallow copy of an object, we can use the copy method provided by the copy module in Python. The copy method returns a shallow copy of the object. For example:

import copy

list1 = [1, 2, [3, 4]]
list2 = copy.copy(list1)

print(list1)  # [1, 2, [3, 4]]
print(list2)  # [1, 2, [3, 4]]

list2[2][0] = 5

print(list1)  # [1, 2, [5, 4]]
print(list2)  # [1, 2, [5, 4]]

In this example, we create a shallow copy of list1 using the copy method. When we modify the nested list in list2, the same change is reflected in list1 because both lists share the same reference to the nested list. To create a deep copy of an object, we can use the deepcopy method provided by the copy module. The deepcopy method returns a deep copy of the object. For example:

import copy

list1 = [1, 2, [3, 4]]
list2 = copy.deepcopy(list1)

print(list1)  # [1, 2, [3, 4]]
print(list2)  # [1, 2, [3, 4]]

list2[2][0] = 5

print(list1)  # [1, 2, [3, 4]]
print(list2)  # [1, 2, [5, 4]]

In this example, we create a deep copy of list1 using the deepcopy method. When we modify the nested list in list2, the change is not reflected in list1 because both lists have independent copies of the nested list.

Local and global variables

In summary, local variables are declared inside a function or method and can only be accessed within that specific block, while global variables are declared outside any function or method and can be accessed throughout the program and inside every function. To modify a global variable inside a function, we need to use the global keyword. Here are some simple examples of local and global variables in Python:

# Example of a local variable
def my_function():
    x = 5
    print(x)

my_function()  # Output: 5

# Example of a global variable
y = 10

def my_function():
    print(y)

my_function()  # Output: 10

In this example, x is a local variable that is declared inside the my_function function and can only be accessed within that function. y, on the other hand, is a global variable that is declared outside any function and can be accessed inside the my_function function. To modify a global variable inside a function, we need to use the global keyword. Here's an example:

# Example of modifying a global variable inside a function
z = 15

def my_function():
    global z
    z = 20

my_function()
print(z)  # Output: 20

Comprehension

Comprehension is a concise and efficient way to create lists, dictionaries, and sets in Python. Multiple and nested comprehensions can be used to create complex data structures in a single line of code. Here's a very simple example of list comprehension:

# Example of list comprehension
my_list = [x for x in range(10)]
print(my_list)  # Output: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

In this example, we use list comprehension to create a list of numbers from 0 to 9. The list comprehension is enclosed in square brackets and consists of an expression x followed by a for loop that iterates over a range of numbers from 0 to 9.

Here's a very simple example of nested list comprehension:

# Example of nested list comprehension
my_list = [[x*y for y in range(5)] for x in range(5)]
print(my_list)  # Output: [[0, 0, 0, 0, 0], [0, 1, 2, 3, 4], [0, 2, 4, 6, 8], [0, 3, 6, 9, 12], [0, 4, 8, 12, 16]]

In this example, we use nested list comprehension to create a list of multiplication tables for numbers from 0 to 4. The outer list comprehension iterates over numbers from 0 to 4, and the inner list comprehension iterates over numbers from 0 to 4 to create a list of products.

Pydantic

Pydantic is a Python package that provides data validation and settings management using Python type annotations. It is a lightweight and flexible package that can be used to validate and parse data from various sources such as JSON, YAML, and databases. Here's a simple example of using Pydantic to define a data model and validate input data:

from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int

person_data = {
    "name": "John Doe",
    "age": 30
}

person = Person(**person_data)
print(person)

To add custom validation to the example using Pydantic, you can use Pydantic's validation decorators like @validator. These decorators allow you to define custom validation functions that run when the model is being initialized. Here's how you can add custom validation to the Person model to ensure that the age is greater than or equal to 18:

from pydantic import BaseModel, validator

class Person(BaseModel):
    name: str
    age: int

    @validator("age")
    def validate_age(cls, value):
        if value < 18:
            raise ValueError("Age must be greater than or equal to 18")
        return value

person_data = {
    "name": "John Doe",
    "age": 30
}

In this example, we define a custom validation function validate_age using the @validator decorator. This function checks if the age value is less than 18, and if so, raises a ValueError with a custom error message. If the validation passes, the function returns the value as it is.

In this example, we define a Pydantic data model Person that has two fields: name of type str and age of type int. Pydantic automatically validates the input data against the data model and raises a ValueError if the data is invalid. If the data is valid, Pydantic creates a new Person object with the validated data and assigns it to the person variable. We then print the person object to verify that it was created correctly.

Pydantic vs Data Classes:

In summary, Pydantic is more suitable when you need robust data validation, type conversion, and serialization capabilities. It's often used in scenarios where data integrity and consistency are critical, such as in web APIs or data validation pipelines. On the other hand, data classes are a simpler and more lightweight choice for defining basic data structures or DTOs (Data Transfer Objects) where validation and serialization are not primary concerns. Your choice between the two depends on your specific requirements and the complexity of your data handling needs.

Args and Kwargs in Python

In Python, *args and **kwargs are used to pass a variable number of arguments to a function. They allow you to handle arbitrary numbers of positional and keyword arguments, respectively. args is used to pass a variable number of positional arguments to a function. It allows you to pass any number of arguments to a function without explicitly specifying them in the function definition. The arguments passed using *args are collected into a tuple within the function.

def print_args(*args):
    for arg in args:
        print(arg)
print_args('Hello', 'World', '!')

Output:

Hello
World
!

**kwargs is used to pass a variable number of keyword arguments to a function. It allows you to pass key-value pairs as arguments to a function without explicitly specifying them in the function definition. The arguments passed using **kwargs are collected into a dictionary within the function. Here's a simple example:

def print_kwargs(**kwargs):
    for key, value in kwargs.items():
        print(key, value)
print_kwargs(name='John', age=25, city='New York')

Output:

name John
age 25
city New York

In this example, the print_kwargs() function takes any number of keyword arguments using **kwargs. The key-value pairs are then printed one by one using a loop. You can also combine *args and **kwargs in a function definition to accept both positional and keyword arguments

Operator overloading

Operator overloading in Python refers to the ability to redefine the behavior of built-in operators *(+, -, , /, etc.) for user-defined classes. This feature provides flexibility and allows you to make your classes work with operators in a way that makes sense for your specific use case.

Here's a simple example to illustrate operator overloading using the + operator:

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y
  
    def __add__(self, other):
        new_x = self.x + other.x
        new_y = self.y + other.y
        return Point(new_x, new_y)

point1 = Point(2, 3)
point2 = Point(4, 5)
result = point1 + point2
print(result.x, result.y)  # Output: 6 8

When we use the "+" operator between point1 and point2, it calls the add() method behind the scenes, allowing us to perform the addition operation in a customized way. The result is a new Point object with the summed coordinates.

Recursive function

A recursive function in Python is a function that calls itself during its execution. It is a powerful technique used to solve problems by breaking them down into smaller, more manageable subproblems. Here's a simple example of a recursive function to calculate the factorial of a number:

def factorial(n):
    if n == 0 or n == 1:
        return 1
    else:
        return n * factorial(n - 1)

result = factorial(5)
print(result)  # Output: 120

Recursive functions are particularly useful when solving problems that can be divided into smaller subproblems of the same nature. They provide an elegant and concise way to express repetitive computations and can simplify complex algorithms. However, it's important to ensure that recursive functions have proper termination conditions to avoid infinite recursion. Recursive functions can consume more memory and may have performance implications compared to iterative solutions, so they should be used judiciously based on the problem at hand.

Context manager

In Python, a context manager is an object that defines the methods enter and exit. It allows you to manage resources, such as files or database connections, in a structured and efficient manner. The with statement is used to create a context, and the context manager ensures that the resources are properly set up and cleaned up, even if an exception occurs. When you use the context manager with the with statement, the enter method is called at the beginning of the block, and the exit method is called at the end, regardless of whether an exception occurred or not. This ensures that resources are properly managed and cleaned up.

Let's consider a practical example of a context manager for working with files. We'll create a context manager that opens a file, performs some operations, and automatically closes the file when exiting the context.

class FileManager:
    def __init__(self, filename, mode):
        self.filename = filename
        self.mode = mode
        self.file = None

    def __enter__(self):
        self.file = open(self.filename, self.mode)
        return self.file

    def __exit__(self, exc_type, exc_value, traceback):
        self.file.close()

# Using the file manager context manager with the 'with' statement
with FileManager("example.txt", "w") as file:
    file.write("Hello, World!")

# File is automatically closed outside the context

In this example, the FileManager class is a context manager that takes a filename and a mode (such as 'r', 'w', or 'a') as parameters. In the enter method, it opens the file in the specified mode and returns the file object. This allows us to work with the file within the with block.

The exit method is responsible for closing the file when exiting the context. Regardless of whether an exception occurs or not, the exit method will be called, ensuring that the file is closed properly.

Python 3.11 over previous versions

Here are some simple examples of the advantages of using Python 3.11 over previous versions:

Fine-grained error locations in tracebacks: When an error occurs, Python 3.11 will point to the exact expression that caused the error, instead of just the line. For example, if you have a syntax error in a long line of code, Python 3.11 will point to the exact part of the line that caused the error, making it easier to debug.
Task and exception groups that simplify working with asynchronous code: Task groups provide a cleaner syntax for running and monitoring asynchronous tasks. For example, you can use task groups to run multiple tasks concurrently and wait for them to complete before continuing.
Faster code execution: Python 3.11 is faster than previous versions, which means that your Python programs will run faster. For example, if you have a program that performs a lot of computations, you will notice a significant speed improvement in Python 3.11.
A new built-in library for working with TOML files: Python 3.11 includes a new built-in library for working with TOML files, which is a popular configuration file format. For example, you can use the "toml" module to read and write TOML files in your Python programs.

These are just a few simple examples of the advantages of using Python 3.11 over previous versions

Semaphores and Mutexes

Semaphores and Mutexes are synchronization mechanisms used in concurrent programming to control access to shared resources and avoid race conditions. Here, I'll explain the concepts of Semaphore and Mutex in Python

Mutex (Mutual Exclusion): A Mutex is a synchronization primitive that allows only one thread to access a shared resource at a time. It ensures that only one thread can acquire the mutex and access the protected resource, while other threads must wait until the mutex is released.

Here's a simple Python example using the threading module:

import threading

# Create a mutex
mutex = threading.Lock()

shared_variable = 0

def increment_shared_variable():
    global shared_variable
    with mutex:
        shared_variable += 1

# Create multiple threads to increment the shared variable
threads = []
for _ in range(5):
    thread = threading.Thread(target=increment_shared_variable)
    threads.append(thread)
    thread.start()

for thread in threads:
    thread.join()

print("Shared variable:", shared_variable)

In this example, the mutex ensures that only one thread can execute the critical section (incrementing shared_variable) at a time, preventing race conditions.

Semaphore: A Semaphore is a synchronization primitive that allows a fixed number of threads to access a resource simultaneously. It maintains a count, and threads can acquire or release the semaphore based on this count.

Here's a simple Python example using the threading module:

import threading

# Create a semaphore with a maximum of 2 permits
semaphore = threading.Semaphore(2)

def access_shared_resource(thread_id):
    semaphore.acquire()
    print(f"Thread {thread_id} is accessing the shared resource.")
    # Simulate some work
    threading.Event().wait()
    print(f"Thread {thread_id} is releasing the shared resource.")
    semaphore.release()

# Create multiple threads to access the shared resource
threads = []
for i in range(5):
    thread = threading.Thread(target=access_shared_resource, args=(i,))
    threads.append(thread)
    thread.start()

for thread in threads:
    thread.join()

In this example, the semaphore allows up to 2 threads to access the shared resource concurrently, while other threads will wait until a permit is released.

Python built-in functions

Here are some advanced Python built-in functions with simple examples:

abs() - returns the absolute value of a number

print(abs(-5)) # Output: 5

all() - returns True if all elements in an iterable are true

print(all([True, True, False])) # Output: False

any() - returns True if any element in an iterable is true

print(any([True, True, False])) # Output: True

bin() - converts an integer to a binary string

print(bin(10)) # Output: 0b1010

enumerate() - returns an iterator that generates tuples containing indices and values from an iterable

fruits = ['apple', 'banana', 'cherry']
for index, value in enumerate(fruits):
    print(index, value)
# Output:
# 0 apple
# 1 banana
# 2 cherry

map() - returns an iterator that generates the results of applying a function to the elements of an iterable

def square(num):
    return num ** 2

numbers = [1, 2, 3, 4, 5]
squared_numbers = list(map(square, numbers))
print(squared_numbers) # Output: [1, 4, 9, 16, 25]

max() - returns the largest element in an iterable or the largest of two or more arguments

print(max([1, 2, 3])) # Output: 3

pow() - returns the result of raising a number to a power

print(pow(2, 3)) # Output: 8

reversed() - returns an iterator that generates the elements of a sequence in reverse order

fruits = ['apple', 'banana', 'cherry']
for fruit in reversed(fruits):
    print(fruit)
# Output:
# cherry
# banana
# apple

round() - rounds a number to a specified number of decimal places

print(round(3.14159, 2)) # Output: 3.14

zip() - returns an iterator that generates tuples by aggregating the elements of several iterables

fruits = ['apple', 'banana', 'cherry']
prices = [1.0, 2.0, 3.0]
for fruit, price in zip(fruits, prices):
    print(fruit, price)
# Output:
# apple 1.0
# banana 2.0
# cherry 3.0

format() - formats a string using replacement fields

name = 'Alice'
age = 30
print('My name is {0} and I am {1} years old.'.format(name, age))
# Output: My name is Alice and I am 30 years old.

frozenset() - returns an immutable frozenset object initialized with elements from the given iterable

vowels = ('a', 'e', 'i', 'o', 'u')
f_set = frozenset(vowels)
print(f_set) # Output: frozenset({'a', 'e', 'i', 'o', 'u'})

getattr() - returns the value of a named attribute of an object

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

person = Person('Alice', 30)
print(getattr(person, 'name')) # Output: 'Alice'

hash() - returns the hash value of an object

print(hash('hello')) # Output: -3557965013271865832

isinstance() - returns True if an object is an instance of a specified class

class Person:
    pass

person = Person()
print(isinstance(person, Person)) # Output: True

issubclass() - returns True if a class is a subclass of a specified class

class Animal:
    pass

class Dog(Animal):
    pass

print(issubclass(Dog, Animal)) # Output: True

setattr() - sets the value of a named attribute of an object

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

person = Person('Alice', 30)
setattr(person, 'age', 31)
print(person.age) # Output: 31

delattr() - deletes a named attribute from an object

class Person:
    name = 'Alice'

person = Person()
delattr(person, 'name')
print(hasattr(person, 'name')) # Output: False

open() - opens a file and returns a file object

file = open('example.txt', 'r')
print(file.read()) # Output: This is an example file.
file.close()

slice() - returns a slice object

my_list = [1, 2, 3, 4, 5]
my_slice = slice(1, 4)
print(my_list[my_slice]) # Output: [2, 3, 4]

Django related topics:

Django signals

Django signals can be used to automate tasks and update data in your application without having to manually perform those tasks or update the data. Let's say we have a Django application that allows users to place orders for products. We want to automatically update the product quantity in the database whenever an order is placed. We can use Django signals to accomplish this.

Let's say you want to send an email notification whenever a new user is registered in your Django application. You can use Django's built-in signals for this purpose.

Import necessary modules:

from django.db import models
from django.contrib.auth.models import User
from django.db.models.signals import post_save
from django.dispatch import receiver
from django.core.mail import send_mail

Create a signal handler function to send an email:

@receiver(post_save, sender=User)
def send_registration_email(sender, instance, created, **kwargs):
    if created:
        subject = 'Welcome to My Website'
        message = 'Thank you for registering on our website.'
        from_email = '[email protected]'
        recipient_list = [instance.email]

        send_mail(subject, message, from_email, recipient_list)

Connect the signal handler:

You need to connect the signal handler to the post_save signal of the User model. You can do this in your Django app's apps.py or models.py file, or in a separate signals.py file:

from django.apps import AppConfig

class YourAppConfig(AppConfig):
    default_auto_field = 'django.db.models.BigAutoField'
    name = 'your_app'

    def ready(self):
        import your_app.signals  # Import the signals module where you defined your signal handler

Now, whenever a new user is registered in your Django application, the send_registration_email function will be triggered, sending a welcome email to the user.

Use cases:

User Authentication: Use signals to perform custom actions when a user is registered or when a user logs in. or Send a welcome email to a user upon registration.
Database Changes: Notify users or perform other actions when a certain condition is met, like a low stock alert for an e-commerce application.
Signals for Models: Trigger actions when a model is created, updated, or deleted.
File Uploads: Perform post-processing tasks on uploaded files. For example, generating thumbnails after an image is uploaded.
Notification Systems: Send notifications to users or administrators when certain events occur, such as a new comment on a post.
Django Middleware: For instance, you can create a signal that fires before or after middleware processes a request, allowing you to perform custom actions at different points in the request/response cycle.
Internationalization (i18n) and Localization (l10n): Use signals to switch the language or locale dynamically based on user preferences or specific events within your application

Django middleware

Django middleware provides a way to process requests and responses globally in your application. Middleware can be used to perform authentication, logging, modifying headers, and more. Let's say we have a Django application that needs to add a custom header to every HTTP response. We can use Django middleware to accomplish this. Here are the steps to use Django middleware:

Create a new file called middleware.py in the same directory as your settings.py file.
Define a class that extends the MiddlewareMixin class. Implement the process_response method to modify the response object.
Add your middleware class to the MIDDLEWARE setting in your settings.py file.

Here's an example implementation:

class CustomHeaderMiddleware:
    def process_response(self, request, response):
        response['X-Custom-Header'] = 'Hello, World!'
        return response

In this example, we define a new middleware class called CustomHeaderMiddleware that extends the MiddlewareMixin class. We implement the process_response method to modify the response object by adding a custom header. Finally, we add our middleware class to the MIDDLEWARE setting in our settings.py file:

MIDDLEWARE = [
    # Other middleware classes...
    'myapp.middleware.CustomHeaderMiddleware',
]

Now, whenever an HTTP response is returned by our application, the CustomHeaderMiddleware class will modify the response by adding a custom header.

Use cases:

Authentication and Authorization: Middleware can check if a user is authenticated and has the required permissions to access a particular view.
Security Measures: Implement security-related tasks such as preventing Cross-Site Request Forgery (CSRF) attacks by adding CSRF tokens to forms or setting security headers like HTTP Strict Transport Security (HSTS) headers.
Request Transformation: Modify the request data before it reaches the view. For example, parsing JSON data from the request body and making it available to the view as a Python dictionary.
Throttling and Rate Limiting: Implement rate limiting to prevent abuse or overuse of your API or services by limiting the number of requests a client can make within a certain time frame.
CORS (Cross-Origin Resource Sharing): Enforce security policies for allowing or denying cross-origin requests to your application's resources.
Content Compression: Compress responses to reduce bandwidth usage and improve page load times, especially for resources like HTML, CSS, and JavaScript files.

Django custom template tags

Custom template tags can be used to perform complex operations on data or to create reusable components for your templates. Here's a simple example of how to create a custom template tag in Django:

Create a new Python module in one of your Django app's templatetags directory. For example, if you have an app named myapp, you could create a new module named myapp_tags.py in the myapp/templatetags directory.
Define a function in the module that takes a template context and any arguments you want to pass to the tag. The function should return the value you want to output in the template. For example, here's a function that takes a list of strings and returns the first item in the list:

from django import template

register = template.Library()

@register.simple_tag
def first_item(my_list):
    if my_list:
        return my_list[0]
    return ''

In this example, we're using the @register.simple_tag decorator to register the function as a simple template tag.

In your template, load the custom tag library and use the new tag in your HTML code. To load the custom tag library, add {% load myapp_tags %} at the top of your template. Here's an example of how to use the first_item tag we defined earlier:

{% load myapp_tags %}

<ul>
  {% for item in my_list %}
    <li>{% first_item item %}</li>
  {% endfor %}
</ul>

In this example, we're using the first_item tag to output the first item in each list item in a loop.

Here are some common use cases for template tags in Django:

Displaying Data from the Database: Template tags are often used to fetch and display data from the database. You can use {% for %} loops to iterate through querysets and display records, or use {% if %} tags to conditionally show content based on database values.
Including Other Templates: You can use the {% include %} tag to include other templates within your template. This is useful for reusing common components across multiple pages.
Conditional Rendering: Template tags like {% if %} and {% else %} are used for conditional rendering. You can show different content based on certain conditions.
Loading External Scripts and Styles: You can use the {% load %} tag to load custom template tags or template libraries that provide additional functionality.
Extending Base Templates: Template inheritance allows you to create a base template and extend it in other templates. This is useful for maintaining a consistent layout across your site.
Setting Variables: The {% with %} tag allows you to set variables within a template, making it easier to reuse values.
Handling Forms: Template tags like {% csrf_token %} are used for including CSRF tokens in forms to ensure security. Additionally, you can use tags like {% forloop %} to iterate through form fields.

These are just a few examples of how template tags can be used in Django templates to add dynamic behavior and logic to your web pages.

Django permissions

Django provides a built-in permissions system that allows you to control access to views and models in your application. Permissions can be assigned to users or groups, and can be checked in your views or templates to determine whether a user has the necessary permissions to perform certain actions. Here's a simple example of how to use Django permissions:

First, you need to define the permissions for your models. You can do this by adding a permissions attribute to your model's meta class. For example, let's say you have a Book model and you want to define a permission called can_edit_book:

from django.db import models
from django.contrib.auth.models import User

class Book(models.Model):
    title = models.CharField(max_length=100)
    author = models.ForeignKey(User, on_delete=models.CASCADE)
    published_date = models.DateField()

    class Meta:
        permissions = [
            ("can_edit_book", "Can edit book"),
        ]

In this example, we've added a permissions attribute to the Book model's meta class, and defined a single permission named can_edit_book.

Next, you need to assign the permissions to users or groups. You can do this using Django's built-in admin interface, or programmatically in your code. For example, let's say we want to assign the can_edit_book permission to a group called Editors:

from django.contrib.auth.models import Group

editors_group, created = Group.objects.get_or_create(name='Editors')
editors_group.permissions.add('myapp.can_edit_book')

In this example, we're using the Group model to get or create a group called Editors, and then adding the myapp.can_edit_book permission to the group.

Finally, you can check the user's permissions in your views or templates to control access to certain actions. For example, let's say we have a view that allows users to edit a book:

from django.shortcuts import get_object_or_404, render
from django.contrib.auth.decorators import login_required, permission_required
from myapp.models import Book

@login_required
@permission_required('myapp.can_edit_book', raise_exception=True)
def edit_book(request, book_id):
    book = get_object_or_404(Book, id=book_id)
    # perform edit operation
    return render(request, 'book_edit.html', {'book': book})

In this example, we're using the permission_required decorator to check if the user has the myapp.can_edit_book permission before allowing them to edit the book. If the user does not have the necessary permission, a PermissionDenied exception will be raised.

Use cases:

Access Control for Views and URLs: For example, allow only authenticated users to access their profile page, while allowing administrators to access an admin dashboard.
Admin Panel Permissions: Define who can create, modify, or delete records in the admin interface for specific models.
API Access Control: Implement role-based access control (RBAC) for APIs, allowing different levels of access for different user roles.

Django custom user models

Django's built-in User model provides a lot of functionality for authentication and authorization, but sometimes you may need to customize the user model to include additional fields or functionality. In such cases, you can create a custom user model that inherits from Django's AbstractBaseUser or AbstractUser classes. Here's a simple example of how to create a custom user model in Django:

Create a new app for your custom user model. For example, let's say you want to create a custom user model for a blog app. You could create a new app called blog_auth.
Create a new model for your custom user model. For example, let's say you want to add a bio field to your user model:

from django.contrib.auth.models import AbstractBaseUser, BaseUserManager, PermissionsMixin
from django.db import models

class BlogUser(AbstractBaseUser, PermissionsMixin):
    email = models.EmailField(unique=True)
    first_name = models.CharField(max_length=30, blank=True)
    last_name = models.CharField(max_length=30, blank=True)
    bio = models.TextField(blank=True)

    is_staff = models.BooleanField(default=False)
    is_active = models.BooleanField(default=True)

    USERNAME_FIELD = 'email'
    EMAIL_FIELD = 'email'

    def __str__(self):
        return self.email

In this example, we've created a new model called BlogUser that inherits from AbstractBaseUser and PermissionsMixin. We've also defined the necessary fields, including a custom bio field.

Update your project settings to use the custom user model. In your project's settings.py file, update the * AUTH_USER_MODEL setting to point to your new user model:

AUTH_USER_MODEL = 'blog_auth.BlogUser'

Migrate your database to create the new user model table. Run the following commands in your terminal:

python manage.py makemigrations
python manage.py migrate

That's it! You now have a custom user model in Django that you can use for authentication and authorization in your blog app.

Django Custom Managers

Django provides a default manager for each model that allows you to interact with the database. However, sometimes you may need to customize the default manager to add additional functionality or implement custom queries. Here's a simple example of how to create a custom manager in Django:

Let's say we have a model called Book in our app that represents books in a library. We want to create a custom manager that returns only the books that are currently available for borrowing.

from django.db import models

class AvailableBooksManager(models.Manager):
    def get_queryset(self):
        return super().get_queryset().filter(is_available=True)

class Book(models.Model):
    title = models.CharField(max_length=100)
    author = models.CharField(max_length=100)
    is_available = models.BooleanField(default=True)

    objects = models.Manager()  # default manager
    available_books = AvailableBooksManager()  # custom manager

In this example, we've created a custom manager called AvailableBooksManager that filters the books based on the is_available field. We've also added a available_books attribute to the Book model that uses the custom manager.

Now we can use the custom manager in our views or templates to get only the books that are currently available for borrowing. For example:

from django.shortcuts import render
from myapp.models import Book

def available_books(request):
    books = Book.available_books.all()
    return render(request, 'available_books.html', {'books': books})

In this example, we're using the available_books manager to get all the books that are currently available for borrowing, and passing them to the available_books.html template.

Use cases:

Filtering and Query Optimization: For example, you can create a manager that filters out inactive or deleted records by default.
Data Validation and Preprocessing: For instance, you can create a manager that ensures all data entered is in uppercase.
Complex Query Construction: For example, you can create a manager that fetches the top-rated products based on user reviews.
Pagination and Result Limiting: For example, Create a manager that retrieves a specified number of records per page.

Django Custom validators

Django provides a set of built-in validators that can be used to validate form data and model fields. However, sometimes you may need to create your own custom validators to validate data in a specific way. Here's a simple example of how to create a custom validator in Django:

Let's say we have a model called Book in our app that represents books in a library. We want to create a custom validator that checks whether the book's title starts with a capital letter.

from django.core.exceptions import ValidationError

def validate_title(value):
    if not value[0].isupper():
        raise ValidationError("Title must start with a capital letter.")

class Book(models.Model):
    title = models.CharField(max_length=100, validators=[validate_title])
    author = models.CharField(max_length=100)

In this example, we've created a custom validator called validate_title that checks whether the first character of the title is a capital letter. If the validation fails, a ValidationError is raised. We've also added the validate_title validator to the title field of the Book model. Now when a user tries to create a book with a title that doesn't start with a capital letter, the validation will fail and an error message will be displayed.

Use cases

Password Strength Validation: Ensure that user passwords meet certain complexity requirements, such as a minimum length, the presence of uppercase and lowercase characters, and special characters.
Age Verification: Validate that a user's age is above a certain threshold for age-restricted content or actions.
URL or Domain Validation: Verify that a URL or domain meets specific criteria, such as being a valid domain name or belonging to an allowed list of domains.
File Type Validation: Ensure that uploaded files have valid file extensions or meet specific criteria.
Consistency Checks: Ensure consistency between related fields in a model. For example, checking that start dates are earlier than end dates.

Custom management commands

Custom management commands allow you to add your own functionality to the manage.py command, making it easy to automate tasks and perform custom operations on your Django project.

Here is a simple example to demonstrate how to create and use custom management commands in Django:

Create a new Django app:

python manage.py startapp myapp

Create a new directory called management inside the app directory, and create another directory called commands inside the management directory:

mkdir myapp/management
mkdir myapp/management/commands

Create a new Python file inside the commands directory, and name it mycommand.py. This will be the file that contains the code for your custom management command:

from django.core.management.base import BaseCommand

class Command(BaseCommand):
    help = 'My custom management command'

    def handle(self, *args, **options):
        print('Hello from my custom management command!')

Register the new command with Django by adding an empty** init.py file inside the management directory:

touch myapp/management/__init__.py

Test the new command by running it from the command line:

python manage.py mycommand

You should see the message "Hello from my custom management command!" printed to the console.

Use cases:

Data Import/Export: Export data from your database to different formats for backup or analysis.
Database Maintenance: Implement commands for database maintenance tasks, such as creating database backups, purging old records, or optimizing database tables.
User Management: Create custom commands for managing users, such as creating user accounts in bulk, resetting passwords, or deactivating inactive users.
Content Population: Populate your database with sample or test data for development and testing purposes using custom commands.
Cache Management: Develop commands to clear or refresh your cache to keep your application's data cache up to date.
API Data Fetching: Create commands to fetch data from external APIs, update your database with the retrieved data, and perform any necessary data transformations.
Report Generation: Generate and distribute reports as PDFs, Excel sheets, or other formats using custom management commands.

Django's Query API

Here are some examples of how to use Django's Query API:

Retrieving all objects from a model:

from myapp.models import MyModel

all_objects = MyModel.objects.all()

This will retrieve all objects from the MyModel model and store them in the all_objects variable.

Filtering objects based on a condition:

from myapp.models import MyModel

filtered_objects = MyModel.objects.filter(attribute=value)

This will retrieve all objects from the MyModel model where the attribute matches the value and store them in the filtered_objects variable.

Chaining filters:

from myapp.models import MyModel

filtered_objects = MyModel.objects.filter(attribute1=value1).filter(attribute2=value2)

This will retrieve all objects from the MyModel model where attribute1 matches value1 and attribute2 matches value2 and store them in the filtered_objects variable.

Querying related fields:

from myapp.models import MyModel

related_objects = MyModel.objects.filter(related_model__attribute=value)

This will retrieve all objects from the MyModel model where the related model's attribute matches value and store them in the related_objects variable.

Ordering results:

from myapp.models import MyModel

ordered_objects = MyModel.objects.order_by('attribute')

This will retrieve all objects from the MyModel model and order them by the attribute field in ascending order. You can also use the - sign to order in descending order.

Limiting results:

from myapp.models import MyModel

limited_objects = MyModel.objects.all()[:10]

This will retrieve the first 10 objects from the MyModel model and store them in the limited_objects variable.

Using OR conditions:

from myapp.models import MyModel
from django.db.models import Q

objects = MyModel.objects.filter(Q(attribute1=value1) | Q(attribute2=value2))

This will retrieve all objects from the MyModel model where either attribute1 matches value1 or attribute2 matches value2 and store them in the objects variable.

Using LIKE conditions:

from myapp.models import MyModel

objects = MyModel.objects.filter(attribute__contains=value)

This will retrieve all objects from the MyModel model where attribute contains value and store them in the objects variable.

Using NOT conditions:

from myapp.models import MyModel

objects = MyModel.objects.exclude(attribute=value)

This will retrieve all objects from the MyModel model where attribute does not match value and store them in the objects variable.

Retrieving a single object:

from myapp.models import MyModel

object = MyModel.objects.get(id=value)

This will retrieve a single object from the MyModel model where id matches value and store it in the object variable.

Updating objects:

from myapp.models import MyModel

MyModel.objects.filter(attribute=value).update(attribute=new_value)

This will update all objects from the MyModel model where attribute matches value and set attribute to new_value.

Custom query expressions or Custom Lookup

Custom query expressions are a way to extend the Django query API with your own custom SQL expressions. Custom query expressions can be useful when you need to perform queries that are not easily expressible using the standard query API.

Suppose you have a model called Person that represents a person with a first name, last name, and age. You want to perform a query that returns all people whose first name starts with a given letter. Here's how you can create a custom query expression to perform this query:

from django.db.models import Lookup

class StartsWith(Lookup):
    lookup_name = 'startswith'

    def as_sql(self, compiler, connection):
        lhs, lhs_params = self.process_lhs(compiler, connection)
        rhs, rhs_params = self.process_rhs(compiler, connection)
        params = lhs_params + [rhs_params[0] + '%']
        return f"{lhs} LIKE %s", params

Person.objects.filter(first_name__startswith='A')

In this example, we define a custom lookup called StartsWith that extends the Lookup class. We set the lookup_name attribute to 'startswith' to define the name of the lookup. We then define the as_sql method to generate the SQL expression for the lookup.

Here's another simple example of defining and using a custom lookup in Django:

from django.db import models
from django.db.models import Lookup


class GreaterThanLookup(Lookup):
    lookup_name = 'gt'
    def as_sql(self, compiler, connection):
        lhs, lhs_params = self.process_lhs(compiler, connection)
        rhs, rhs_params = self.process_rhs(compiler, connection)
        params = lhs_params + rhs_params
        return f'{lhs} > {rhs}', params


models.IntegerField.register_lookup(GreaterThanLookup)
class Product(models.Model):
    name = models.CharField(max_length=100)
    price = models.DecimalField(max_digits=8, decimal_places=2)

Usage of the custom lookup

expensive_products = Product.objects.filter(price__gt=100.00)

In this example, we define a custom lookup called GreaterThanLookup that performs a greater-than comparison (>) for IntegerField fields. We subclass django.db.models.Lookup and override the as_sql() method to define the SQL representation of the lookup. The usage of the custom lookup is demonstrated, where we filter the Product objects based on the price field using the gt lookup.

Django Filterset

Django Filterset provides a simple and intuitive way to filter data in Django. It allows you to define filterset classes that specify the fields to filter on and provides a range of built-in filters that you can use to filter data. You can also define custom filters and combine filters to create more complex filtering logic. Here are some simple examples of using Django Filterset:

Basic filtering:

To filter a queryset using Django Filterset, you first need to define a filterset class that specifies the fields to filter on. For example:

import django_filters
from .models import Product

class ProductFilter(django_filters.FilterSet):
    class Meta:
        model = Product
        fields = ['name', 'price']

This defines a filterset class that filters on the name and price fields of the Product model. You can then use this filterset class to filter a queryset:

from .filters import ProductFilter

def product_list(request):
    queryset = Product.objects.all()
    filterset = ProductFilter(request.GET, queryset=queryset)
    return render(request, 'product_list.html', {'filterset': filterset})

This filters the queryset based on the parameters provided in the GET request.

Custom filtering:

You can also define custom filters that perform more complex filtering logic. For example:

import django_filters
from .models import Product

class ProductFilter(django_filters.FilterSet):
    min_price = django_filters.NumberFilter(field_name='price', lookup_expr='gte')
    max_price = django_filters.NumberFilter(field_name='price', lookup_expr='lte')

    class Meta:
        model = Product
        fields = ['name', 'min_price', 'max_price']

This defines custom filters that filter on the price field of the Product model. The min_price filter filters on products with a price greater than or equal to the provided value, and the max_price filter filters on products with a price less than or equal to the provided value. Instead of using FilterSets, you can directly use the Django ORM filters to perform filtering on your querysets. Django ORM provides a wide range of filter options such as exact match, case-insensitive match, range filters, and more. You can chain multiple filters together to create complex queries.

Use cases:

Search Functionality: Implement a search feature allowing users to search for records based on specific criteria, such as keywords or partial matches in various fields.
Data Filtering: Create filters to allow users to filter data based on multiple fields, such as date ranges, categories, or status.
Exporting Filtered Data: Enable users to export filtered data to CSV, Excel, or other formats for further analysis or reporting.
Geolocation Filtering: Build Filtersets for location-based queries, such as finding nearby places or events within a specific radius.
Data Visualization: Integrate Filtersets with data visualization libraries to enable users to filter and explore data interactively.

Context managers

The context manager is responsible for setting up the context, running the block of code, and then cleaning up the context when the block is exited, regardless of whether the block raised an exception or not. In Django, context managers can be used to control signals, transactions, and caching. Here are some simple examples of context managers in Django:

Database transactions: In Django, context managers can be used to control database transactions. For example:

from django.db import transaction

with transaction.atomic():
    # code that requires a transaction

Caching: Context managers can be used to control caching in Django. For example:

from django.core.cache import cache

class CacheContext:
    def __init__(self, key, value):
        self.key = key
        self.value = value

    def __enter__(self):
        cache.set(self.key, self.value)

    def __exit__(self, exc_type, exc_val, exc_tb):
        cache.delete(self.key)

with CacheContext('my_key', 'my_value'):
    # code that requires the cache to be set

In summary, context managers in Django provide a simple and intuitive API for a powerful construct. They allow you to allocate and release resources precisely when you want to, and they make it easier to write safe and readable code. Overall, context managers provide a robust and efficient way to manage resources and ensure proper cleanup in Django applications. They contribute to cleaner code, improved error handling, and better resource utilization.

Use Cases:

Database Transactions: Use context managers to manage database transactions. This ensures that database changes are committed if the code block executes without errors or rolled back in case of exceptions.
File Handling: Open and close files safely using a context manager, ensuring that files are closed properly even if an exception occurs.
Resource Locking: Implement resource locking to ensure exclusive access to a resource, preventing concurrent access by multiple threads or processes.
Database Connections: Manage database connections and cursors safely, ensuring that connections are closed after use.

Django Channels

Django Channels is a package that allows Django to handle WebSockets and other non-HTTP protocols. It extends the built-in capabilities of Django, allowing Django projects to handle not only HTTP but also protocols that require long-running connections, such as WebSockets, MQTT (IoT), chatbots, radios, and other real-time applications. Here are some differences between Django Channels and Django's built-in HTTP capabilities:

Django Channels:

Allows Django projects to handle protocols other than HTTP, such as WebSockets, MQTT, and chatbots
Provides support for Django's core features like authentication and sessions
Preserves the synchronous behavior of Django and adds a layer of asynchronous protocols allowing users to write views that are entirely synchronous, asynchronous, or a mixture of both
Replaces Django's default WSGI with its ASGI (Asynchronous Server Gateway Interface)

Django's built-in HTTP capabilities:

Handle only HTTP requests
Do not support protocols that require long-running connections, such as WebSockets and MQTT
Do not provide support for asynchronous code execution

In summary, Django Channels extends Django's built-in capabilities to handle protocols other than HTTP and provides support for asynchronous code execution.

Use Cases:

Real-Time Chat Applications: Create real-time chat applications where users can send and receive messages instantly using WebSockets.
Live Notifications: Implement live notifications to inform users about new messages, friend requests, updates, or any other events that require immediate user attention.
Multiplayer Online Games: Develop real-time multiplayer games using WebSockets for game state synchronization, player interaction, and chat functionality.
IoT Integration: Integrate with Internet of Things (IoT) devices by handling real-time data streams, sensor data, and device control.
Financial Trading Platforms: Build financial trading applications that require real-time price updates, order execution, and live trading data.
Geolocation and Mapping: Develop applications that track and display the real-time location of vehicles, deliveries, or assets on a map.
Live Video Streaming: Build live video streaming platforms for webinars, conferences, or live events, where viewers can interact with the stream in real time.

HTTP methods in Django

Django provides built-in support for handling HTTP requests and responses using request and response objects Here are some examples of how to use HTTP methods in Django:

GET:

To handle a GET request in a class-based view, you can define a method called get() in your view class. For example:

from django.views import View
from django.http import HttpResponse

class HelloView(View):
    def get(self, request):
        return HttpResponse('Hello, World!')

POST:

To handle a POST request in a class-based view, you can define a method called post() in your view class. For example:

from django.views import View
from django.http import HttpResponse

class LoginView(View):
    def post(self, request):
        username = request.POST['username']
        password = request.POST['password']
        # Authenticate user
        return HttpResponse('Logged in successfully')

PUT and DELETE:

Django's class-based views do not have built-in support for handling PUT and DELETE requests. However, you can use mixins or third-party packages like Django REST framework to handle these requests. For example, using Django REST framework, you can define a class-based view that inherits from APIView and override the appropriate methods for PUT and DELETE requests:

from rest_framework.views import APIView
from rest_framework.response import Response

class UserDetailView(APIView):
    def put(self, request, pk):
        # Update user object with pk
        return Response({'message': 'User updated'})

    def delete(self, request, pk):
        # Delete user object with pk
        return Response({'message': 'User deleted'})

In Django, the difference between PUT and PATCH methods is similar to their difference in general HTTP terms.

PUT is used to modify an entire resource on the server. When a client sends a PUT request in Django, it updates the entire resource with the new data provided. If the resource does not exist, PUT creates a new resource. PUT is idempotent, meaning that calling it once or multiple times successively has the same effect.
PATCH is used to modify a part of a resource on the server. When a client sends a PATCH request in Django, it updates only the specified part of the resource with the new data provided. PATCH is not idempotent, meaning that successive identical PATCH requests may have additional effects. PATCH allows partial updates and side-effects on other resources.

Here's an example of how to handle PUT and PATCH requests in Django using Django REST Framework:

from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status

class MyResourceView(APIView):
    def put(self, request, pk):
        # Retrieve the resource with the given pk
        resource = MyResource.objects.get(pk=pk)

        # Update the entire resource with the new data
        resource.name = request.data.get('name')
        resource.age = request.data.get('age')
        resource.save()

        return Response(status=status.HTTP_200_OK)

    def patch(self, request, pk):
        # Retrieve the resource with the given pk
        resource = MyResource.objects.get(pk=pk)

        # Update only the specified part of the resource with the new data
        if 'name' in request.data:
            resource.name = request.data['name']
        if 'age' in request.data:
            resource.age = request.data['age']
        resource.save()

        return Response(status=status.HTTP_200_OK)

In the above example, the put method is used to handle PUT requests and update the entire resource with the new name and age. The patch method is used to handle PATCH requests and update only the specified part of the resource. The request.data attribute is used to access the data sent in the request body.

annotate and aggregate in Django

annotate() is typically used when you want to add additional information or calculated fields to each object in the queryset. For example, you can annotate a queryset of books with the number of authors for each book On the other hand, aggregate() is used when you want to perform calculations on the entire queryset, such as calculating the sum, average, or count of a specific field across all objects in the queryset.

The output of annotate() is a QuerySet, which means it returns a modified queryset with the annotated values. On the other hand, aggregate() returns a dictionary containing the calculated aggregate values

Here is a simple example of using annotate() and aggregate() in Django:

from django.db.models import Count
from myapp.models import Book, Author

# Count the number of books for each author
authors = Author.objects.annotate(num_books=Count('book'))

# Count the total number of books
total_books = Book.objects.aggregate(total=Count('id'))

In the above example, annotate() is used to count the number of books for each author. The Count() function is used to count the number of related books for each author. The resulting queryset will have an additional attribute num_books for each author object. aggregate() is used to count the total number of books. The Count() function is used to count the number of books in the Book model. The resulting dictionary will have a single key total with the total number of books as its value. It is important to note that annotate() returns a queryset, while aggregate() returns a dictionary.

Mixin in Django

In Django, a mixin is a class that provides additional functionality to other classes through inheritance. Mixins are used to add common or reusable functionality to multiple classes without the need for duplicating code. Here's a simple example to demonstrate how mixins work in Django:

# Define a mixin class
class TimestampMixin:
    created_at = models.DateTimeField(auto_now_add=True)
    updated_at = models.DateTimeField(auto_now=True)

    class Meta:
        abstract = True

# Use the mixin in a model
class MyModel(TimestampMixin, models.Model):
    name = models.CharField(max_length=100)
    # other fields

# Use the mixin in a view
class MyView(TimestampMixin, View):
    def get(self, request):
        # handle GET request
        pass

    def post(self, request):
        # handle POST request
        pass

In this example, we have defined a mixin class called TimestampMixin. It adds two fields, created_at and updated_at, to any class that inherits from it. The Meta class with abstract = True ensures that the mixin itself is not treated as a model. By using mixins, you can easily add common functionality to multiple classes without duplicating code. This promotes code reuse, improves maintainability, and keeps your codebase organized.

Use Cases:

Authentication Mixin: Add authentication checks to views, ensuring that only authenticated users can access specific views.
Permission Mixin: Check user permissions or roles before allowing access to certain views, ensuring that users have the required privileges.
Pagination Mixin: Implement pagination for list views, allowing users to navigate through large datasets.
Timestamp Mixin: Add timestamp fields (created_at, updated_at) to models for automatic tracking of creation and modification times.
Geolocation Mixin: Add latitude and longitude fields to models, enabling geospatial queries and location-based services.
Captcha Mixin: Integrate CAPTCHA validation with forms to prevent spam submissions.
File Upload Mixin: Add file upload functionality to forms, allowing users to upload files like images or documents.
User Tracking Mixin: Implement middleware to track user activity, such as page views or interactions, and log them for analytics.

These are just a few examples of how mixins can enhance Django applications by promoting code reuse and modularity. They allow you to easily add and extend functionality across various components of your Django project, making your code more maintainable and efficient.

Cache in Django

By using caching in Django, you can significantly improve the performance of your application by reducing the load on the database and serving cached results faster. It is an essential technique for optimizing web applications and improving user experience. Django provides built-in support for caching through its caching framework. The caching framework allows you to cache the results of views, template fragments, and even low-level database queries. It supports various cache backends and provides flexibility in configuring cache settings. Here's a simple example to demonstrate how caching works in Django:

1- Configure the cache backend in your Django settings:

CACHES = {
    'default': {
        'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache',
        'LOCATION': '127.0.0.1:11211',
    }
}

2- Use the cache_page decorator to cache the result of a view:

from django.views.decorators.cache import cache_page

@cache_page(60 * 15)  # Cache the page for 15 minutes
def my_view(request):
    # Expensive computation or database query
    # ...
    return HttpResponse('Hello, World!')

We then use the cache_page decorator to cache the result of the my_view function for 15 minutes. This means that the first time the view is accessed, the result will be computed and stored in the cache. For subsequent requests within the next 15 minutes, the cached result will be returned directly without executing the view function again.

Use Cases:

Page Caching: Cache the rendered HTML of frequently accessed pages to reduce the load on the database and speed up page rendering.
Queryset Caching: Cache the results of database queries, especially for complex or frequently used queries. This reduces the need to hit the database repeatedly.
Template Fragment Caching: Cache specific portions of templates, such as navigation menus or sidebars, to minimize rendering time for frequently used components.
Computed Values Caching: Cache the results of expensive computations or data transformations to avoid recomputation for the same input.
Third-Party API Responses: Cache responses from third-party APIs to reduce the number of requests made to external services and improve the application's reliability.
Rate Limiting: Implement rate limiting by caching user request information to control the number of requests allowed within a specific time period.

Django provides a flexible caching framework that supports various caching backends, including in-memory caching, file-based caching, and distributed cache systems like Memcached and Redis. By strategically implementing caching in your Django application, you can significantly improve its performance and scalability.

Django constraint

In Django, a constraint refers to a rule or condition that can be applied to a database table to enforce data integrity. Constraints ensure that the data stored in the table follows certain rules and meets specific requirements. Django provides a way to define constraints on model fields using the constraints attribute. This attribute allows you to specify one or more constraints for a model, such as unique constraints, check constraints, foreign key constraints, and more. Here's a simple example of defining and using a constraint in Django:

from django.db import models
class Person(models.Model):
    name = models.CharField(max_length=100)
    age = models.IntegerField()
    class Meta:
        constraints = [
            models.CheckConstraint(check=models.Q(age__gte=18), name='age_gte_18')
        ]

In this example, we define a Person model with two fields: name and age. We want to enforce a constraint that ensures the age field is greater than or equal to 18. By defining this constraint, Django will automatically create the necessary SQL statements to enforce the constraint when creating or modifying the database table for the Person model. Constraints help maintain data integrity and ensure that the data stored in the database follows the defined rules. They can be particularly useful in scenarios where you want to enforce specific conditions or relationships between fields in your models.

Django constraints vs model validators differences

Django constraints and model validators are two different mechanisms for ensuring data integrity in Django applications. Constraints are used to enforce data integrity rules at the database level, while validators are used to enforce data integrity rules at the application leve. During exceptions in Django Constraints raise database-level errors such as IntegrityError, while validators raise application-level errors such as ValidationError

Use Cases:

Unique Constraints: Ensure that a specific field or combination of fields contains unique values across records. Commonly used for unique usernames, email addresses, or product codes.
Check Constraints: Specify custom validation rules for data in one or more fields. For example, ensure that a price field is always positive, or that a date falls within a certain range.
Unique Together Constraints: Ensure that a combination of fields contains unique values. Useful when you want to enforce uniqueness based on multiple fields, such as a combination of username and email.
Table-Level Constraints: Enforce constraints that involve multiple fields or records at the table level. For instance, ensuring that the start date is always earlier than the end date for date ranges.

bulk creation in Django

In Django, bulk creation refers to the process of creating multiple model instances in a single database query, rather than creating them one by one. This can significantly improve the performance of creating large numbers of objects. This method takes a list of model instances as an argument and inserts them into the database efficiently. Here's a simple example of using bulk_create() in Django:

from django.db import models

class Book(models.Model):
    title = models.CharField(max_length=100)
    author = models.CharField(max_length=100)
    publication_year = models.IntegerField()


# Create a list of Book instances
books = [
    Book(title='Book 1', author='Author 1', publication_year=2020),
    Book(title='Book 2', author='Author 2', publication_year=2021),
    Book(title='Book 3', author='Author 3', publication_year=2022),
]

# Bulk create the books
Book.objects.bulk_create(books)

In this example, we define a Book model with three fields: title, author, and publication_year. We then create a list of Book instances with different values. To perform bulk creation, we use the bulk_create() method of the Book.objects manager. We pass the list of Book instances as an argument to bulk_create(), and Django will efficiently insert them into the database in a single query. Overall, bulk_create() is a useful feature in Django for efficiently creating multiple model instances in a single database query.

Use Cases:

Data Import and Migration: Importing data from external sources, such as CSV files or JSON files, into your Django application's database.
Bulk Updates: Updating multiple rows in a database table simultaneously when changes need to be made to many records at once.
Historical Data: Maintaining historical records, such as version history, snapshots, or backups, by inserting historical data into dedicated tables.

prefetch_related and select_related in Django

In Django, prefetch_related and select_related are query optimization techniques that allow you to reduce the number of database queries when retrieving related objects.

1- select_related

It works for ForeignKey and OneToOneField relationships. By using select_related, you can avoid the overhead of multiple database queries when accessing related objects. Here's a simple example:

from django.db import models

class Author(models.Model):
    name = models.CharField(max_length=100)

class Book(models.Model):
    title = models.CharField(max_length=100)
    author = models.ForeignKey(Author, on_delete=models.CASCADE)

Suppose we have a Book model with a ForeignKey relationship to the Author model. If we want to retrieve all books and their corresponding authors, we can use select_related to fetch the related authors in a single query:

books = Book.objects.select_related('author').all()

for book in books:
    print(f"Book: {book.title}, Author: {book.author.name}")

In this example, select_related('author') is used to fetch the related Author objects along with the Book objects in a single query. This avoids the need for an additional query for each book's author.

2- prefetch_related

It works for ManyToManyField and reverse ForeignKey relationships. By using prefetch_related, you can reduce the number of queries when accessing related objects. Here's a simple example:

from django.db import models

class Category(models.Model):
    name = models.CharField(max_length=100)

class Product(models.Model):
    name = models.CharField(max_length=100)
    categories = models.ManyToManyField(Category)

Suppose we have a Product model with a ManyToManyField relationship to the Category model. If we want to retrieve all products and their corresponding categories, we can use prefetch_related to fetch the related categories efficiently:

products = Product.objects.prefetch_related('categories').all()

for product in products:
    print(f"Product: {product.name}")
    for category in product.categories.all():
        print(f"Category: {category.name}")

To retrieve all authors along with their books efficiently, we can use prefetch_related as follows:

authors = Author.objects.prefetch_related('books').all()
for author in authors:
    print(f"Author: {author.name}")
    for book in author.books.all():
        print(f"Book: {book.title}")

In this example, prefetch_related('books') is used to fetch all the related Book objects efficiently for each author. It reduces the number of queries by fetching all the related books in a separate query, rather than fetching them individually for each author.

In this example, prefetch_related('categories') is used to fetch the related Category objects efficiently. It reduces the number of queries by fetching all the related categories in a separate query, rather than fetching them individually for each product. Using select_related and prefetch_related can significantly improve the performance of your Django queries by reducing the number of database queries. It is especially useful when dealing with large datasets or complex relationships between models.

Third-party packages in Django

There are several third-party packages that can greatly enhance your development experience and provide additional functionality. Here is a list of must-have third-party packages that are commonly used in Django projects:

Django REST framework: A powerful and flexible toolkit for building Web APIs.
Django Crispy Forms: Easily manage Django forms' layout using bootstrap styles.
Django Debug Toolbar: Provides a set of panels displaying various debug information about the current request/response.
Django Celery: Distributed task queue system for asynchronous processing.
Django allauth: User registration, authentication, and account management.
Django Rest Auth: Provides a set of REST API endpoints for user registration, authentication, and account management.
Django Filter: Simplifies the process of filtering querysets dynamically.
Django Rest Framework Swagger: Generates interactive API documentation using the OpenAPI standard.
Django Environ: Allows you to define environment variables for your Django project.

Property decorators

The @property decorator in Django is used to define a method that behaves like a model attribute. It allows you to call custom model methods as if they were normal model attributes. Here's a simple example to illustrate the concept:

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In this example, we define a Book model with fields for title, author, and price. We then define a price_in_euros method with the @property decorator. This method returns the price of the book in euros as a string.

By using the @property decorator, we can access the price_in_euros method as if it were a model attribute. For example, we can retrieve the price of a book in euros as follows:

book = Book.objects.get(pk=1)
print(book.price_in_euros)  # Output: "19.99 EUR"

In Django models, a method with the @property decorator behaves like a model attribute, while a method without this decorator behaves like a normal method. Here's an example to illustrate the difference:

class Book(models.Model):
    title = models.CharField(max_length=100)
    author = models.CharField(max_length=50)
    price = models.DecimalField(max_digits=5, decimal_places=2)

    @property
    def price_in_euros(self):
        return f"{self.price} EUR"

    def calculate_discounted_price(self, discount):
        return self.price * (1 - discount)

When we access my_book.price_in_euros, it behaves like a model attribute and returns the price of the book in euros as a string. On the other hand, when we call my_book.calculate_discounted_price(0.1), it behaves like a normal method and returns the discounted price of the book.

WSGI and ASGI

The Web Server Gateway Interface (WSGI) and the Asynchronous Server Gateway Interface (ASGI) are two different specifications in Django for handling web requests and responses. Here's a brief explanation of each:

WSGI (Web Server Gateway Interface):

WSGI is a synchronous interface that defines how web servers communicate with Python web applications.
It provides a standard way for web servers to forward requests to Python web frameworks and applications.
WSGI servers handle one request at a time and block until the response is generated, making it suitable for traditional synchronous web applications.
WSGI is the older and more established interface, widely used by Python web frameworks and servers.

ASGI (Asynchronous Server Gateway Interface):

ASGI is an asynchronous interface that extends the capabilities of WSGI to support asynchronous web applications.
It allows multiple, concurrent requests to be handled asynchronously, making it suitable for real-time applications, long-polling, and WebSockets.
ASGI servers can handle both synchronous and asynchronous applications, providing flexibility for developers.
ASGI is designed to be a superset of WSGI, meaning that WSGI applications can be run inside ASGI servers.

In summary, WSGI is a synchronous interface for handling web requests and responses, while ASGI is an asynchronous interface that extends the capabilities of WSGI to support asynchronous applications. ASGI provides the ability to handle multiple concurrent requests and is suitable for real-time and long-polling applications.

Advanced features in ORM

Django ORM (Object-Relational Mapping) provides several advanced features that allow for more complex and powerful database queries and data manipulation. Here are some of the advanced features of Django ORM:

Q objects:

Q objects allow for complex queries by combining multiple filters using logical operators like AND (&) and OR (|). They encapsulate keyword arguments for filtering and provide more flexibility in query construction

from django.db.models import Q

# Query to retrieve books with either 'fiction' or 'mystery' genre
books = Book.objects.filter(Q(genre='fiction') | Q(genre='mystery'))

F expressions:

F expressions allow you to perform database operations using field values without pulling the values from the database. They enable calculations and comparisons directly within the database query, improving performance and reducing round trips to the database

from django.db.models import F

# Query to update the price of all books by increasing it by 10%
Book.objects.all().update(price=F('price') * 1.1)

QuerySet methods

Django provides a comprehensive set of QuerySet methods for retrieving, filtering, and manipulating data from the database. These methods include annotate(), aggregate(), values(), distinct(), order_by(), reverse(), and many more

# Query to retrieve the top 5 books with the highest ratings
top_books = Book.objects.order_by('-rating')[:5]

# Query to retrieve the distinct authors of all books
authors = Book.objects.values('author').distinct()

# Query to annotate the average price for each genre
genres = Book.objects.values('genre').annotate(avg_price=Avg('price'))

Model managers

Model managers allow you to customize the default QuerySet behavior by defining custom methods on the manager class. Managers provide a way to encapsulate common query logic and make it easily accessible across multiple models

class BookManager(models.Manager):
    def get_best_sellers(self):
        return self.filter(sales__gt=1000)

class Book(models.Model):
    title = models.CharField(max_length=100)
    author = models.CharField(max_length=50)
    price = models.DecimalField(max_digits=5, decimal_places=2)
    sales = models.IntegerField()

    objects = BookManager()

# Query to retrieve the best-selling books
best_sellers = Book.objects.get_best_sellers()

Model inheritance

Django supports model inheritance, allowing you to create more complex data models by building relationships between models. You can use abstract base classes, multi-table inheritance, and proxy models to implement different types of model inheritance

class Publication(models.Model):
    title = models.CharField(max_length=100)
    publisher = models.CharField(max_length=50)

class Book(Publication):
    author = models.CharField(max_length=50)
    price = models.DecimalField(max_digits=5, decimal_places=2)

class Magazine(Publication):
    issue_number = models.IntegerField()

# Query to retrieve all publications (books and magazines)
publications = Publication.objects.all()

These examples demonstrate the usage of advanced features in Django ORM, such as Q objects for complex queries, F expressions for database operations, QuerySet methods for data retrieval and manipulation, model managers for custom query logic, and model inheritance for creating relationships between models.

Class-based views methods

Django class-based views provide a set of methods that can be overridden to customize the behavior of the view. Here are some of the commonly used methods in Django class-based views:

as_view(): This method returns a callable view function that handles the request and generates the response. It is called when the URL pattern is matched to the view class.
dispatch(): This method is called by the as_view() method to handle the request. It inspects the request method (GET, POST, etc.) and calls the appropriate method (get(), post(), etc.) to generate the response.
HTTP method handlers: The HTTP method handlers (get(), post(), etc.) are called by the dispatch() method to generate the response. They perform the necessary operations (querying the database, rendering templates, etc.) and return an HttpResponse object.
get_context_data(): This method is called by the HTTP method handlers to retrieve the context data for the template. It returns a dictionary of context variables that are passed to the template.
get(), post(), put(), delete(): These methods are called by the dispatch() method to handle the corresponding HTTP methods. They perform the necessary operations (querying the database, rendering templates, etc.) and return an HttpResponse object.
get_queryset(): This method is used to retrieve the queryset that the view will use to retrieve objects. It can be overridden to customize the queryset based on the request.
get_object(): This method is used to retrieve a single object from the queryset. It can be overridden to customize the object retrieval based on the request.
form_valid(), form_invalid(): These methods are called when a form is submitted and validated. They perform the necessary operations (saving the form data, redirecting to a new page, etc.) and return an HttpResponse object.

By understanding these methods, you can customize the behavior of Django class-based views to suit your specific requirements and optimize the performance of your Django applications.

Django optimization

To optimize your Django application for scalability, there are several techniques you can implement:

Database Optimization:

Django's database layer provides various ways to improve performance, such as using database indexes, optimizing queries, and reducing database round trips Suppose you have a Django model called Book with a field called title. To optimize the database query, you can add an index to the title field by adding db_index=True to the field definition in the model:

class Book(models.Model):
    title = models.CharField(max_length=100, db_index=True)
    author = models.CharField(max_length=50)

this index will speed up queries that filter or order by the title field, as the database can use the index to quickly find the relevant rows

Caching:

Implementing caching can significantly improve the performance of your Django application. You can use tools like Django's built-in caching framework. For example, to cache the result of a query for 5 minutes, you can use the cache_page decorator:

from django.views.decorators.cache import cache_page

@cache_page(60 * 5)
def my_view(request):
    # perform database query
    books = Book.objects.all()

Code Optimization:

Optimizing your code can help improve the overall performance of your application. This includes writing efficient algorithms, reducing unnecessary database queries, and optimizing resource-intensive operations.

Suppose you have a Django view that returns a list of all Book objects in the database. To optimize the code, you can use the prefetch_related method to fetch related objects in a single query:

code example:

def book_list(request):
    # perform database query for all books
    books = Book.objects.all()
    # perform database query for each author of each book
    for book in books:
        authors = book.author_set.all()
        ...

Optimized version:

def book_list(request):
    # fetch related authors in a single query
    books = Book.objects.prefetch_related('author_set').all()
    for book in books:
        authors = book.author_set.all()
        ...

In the first example, the code performs a separate database query for each author of each book, resulting in a large number of queries. In the second example, the prefetch_related method fetches all related authors in a single query, reducing the number of queries and improving performance.

Distributed Task Queues:

Using distributed task queues, such as Celery, can help offload time-consuming tasks to separate worker processes, allowing your application to handle more concurrent requests. Suppose you have a view that performs a time-consuming task, such as sending an email. To offload the task to a separate worker process, you can use Celery such as below:

from celery import shared_task

@shared_task
def send_email_task(email):
    # send email
    ...

def my_view(request):
    # offload task to Celery worker
    send_email_task.delay(email)
    ...

Scaling Horizontally:

To handle increased traffic and user load, you can scale your Django application horizontally by splitting it into individual services (SOA) and scaling them independently. This approach allows you to distribute the load and optimize each service individually. Suppose you have a Django application that handles both user authentication and payment processing. To scale the application horizontally, you can split it into two separate services: 1- Authentication service: Handles user authentication and authorization. 2- Payment service: Handles payment processing and billing.

Benchmarking:

Regularly benchmarking and profiling your application can help identify performance bottlenecks and areas for improvement. Tools like Django Debug Toolbar and Django Silk can assist in analyzing and optimizing your code.

Generic Foreign Key in Django

In Django, a Generic Foreign Key is a feature that allows you to create a relationship between a model and any other model in the database without directly specifying the related model. This is useful when you have a model that needs to be related to multiple other models. Instead of creating separate foreign keys for each related model, you can use a generic foreign key to achieve this flexibility.

In this example, we will create a model called Vote, which can be used to represent votes on different types of content, such as posts, comments, or images.

1- Define the models in models.py:

from django.contrib.contenttypes.fields import GenericForeignKey
from django.contrib.contenttypes.models import ContentType
from django.db import models

class Vote(models.Model):
    content_type = models.ForeignKey(ContentType, on_delete=models.CASCADE)
    object_id = models.PositiveIntegerField()
    content_object = GenericForeignKey('content_type', 'object_id')

    voter_name = models.CharField(max_length=100)
    vote_type = models.CharField(max_length=10)  # e.g., 'upvote', 'downvote'

    def __str__(self):
        return f"{self.voter_name} voted on {self.content_object} ({self.vote_type})"

2- Create instances of the models: Now, you can create instances of the Vote model and associate them with any other models in the database. For example:

from django.contrib.contenttypes.models import ContentType

# Create a post instance (just an example, you can have other models)
post = Post.objects.create(title="My Post", content="This is my post content")

# Create a comment instance (just an example, you can have other models)
comment = Comment.objects.create(text="Nice post!")

# Create a vote for the post
post_vote = Vote.objects.create(
    content_object=post,
    voter_name="John Doe",
    vote_type="upvote"
)

# Create a vote for the comment
comment_vote = Vote.objects.create(
    content_object=comment,
    voter_name="Alice",
    vote_type="downvote"
)

In this example, we have created a Vote model with a generic foreign key, allowing it to be associated with different types of content (posts, comments, etc.). You can now use the Vote model to track votes on various content types throughout your Django project.

Django custom exceptions

In Django, you can create custom exceptions to handle specific error scenarios in your application. Custom exceptions allow you to define your own exception classes that inherit from Python's built-in Exception class or any other existing exception class. By doing so, you can raise and catch these custom exceptions in your code to handle different types of errors more effectively. Here's a simple example of how to create and use custom exceptions in Django:

1- Define the custom exception in a exceptions.py file (you can create this file in your app directory):

class InvalidDataError(Exception):
    def __init__(self, message="Invalid data provided."):
        self.message = message
        super().__init__(self.message)

2- Raise the custom exception in your views or other parts of the code:

from django.shortcuts import render
from .exceptions import InvalidDataError

def custom_exception_view(request):
    try:
        # Some logic to validate data
        data = request.POST.get('data')
        if not data:
            raise InvalidDataError("Data is missing!")

        # Rest of the code for processing valid data
        return render(request, 'success.html', {'data': data})

    except InvalidDataError as e:
        # Handle the custom exception
        error_message = str(e)
        return render(request, 'error.html', {'error_message': error_message})

In this example, we create a view function called custom_exception_view. Inside the view, we perform some validation on the data received in the POST request. If the data is missing or invalid, we raise the InvalidDataError custom exception with a specific error message.

here are some examples of Exception Handling in Django:

Catching exceptions in views:

from django.http import HttpResponseServerError
def my_view(request):
    try:
        # Code that may raise an exception
        # ...
    except SomeException as e:
        # Code to handle SomeException
        return HttpResponseServerError("An error occurred: {}".format(str(e)))

In this example, if SomeException is raised within the try block, the corresponding except block will be executed. It returns an HTTP 500 response with a custom error message.

Handling database-related exceptions:

When working with Django's ORM (Object-Relational Mapping) and database operations, you may encounter exceptions related to database errors.

from django.db import DatabaseError

def my_view(request):
    try:
        # Perform database operations
        # ...
    except DatabaseError as e:
        # Handle database-related exception
        return render(request, 'error.html', {'message': str(e)})

In this example, if a DatabaseError occurs during database operations, you can render an error template with the exception message.

select_for_update in Django

In Django, select_for_update is a method that allows you to lock database rows for update during a database query. This is useful in scenarios where you want to prevent multiple transactions from simultaneously modifying the same rows, thus avoiding potential conflicts and ensuring data consistency. When you use select_for_update, it places a "select for update" lock on the selected rows in the database. Other transactions attempting to modify the same rows will have to wait until the lock is released. This feature is particularly important in situations where concurrent updates could lead to incorrect results or data integrity issues.

Assume we have a model called Book that represents books in a library. We want to ensure that when a user borrows a book, the book's availability status is updated, and during this process, no other user can borrow the same book until the update is complete.

1- Define the model in models.py:

from django.db import models

class Book(models.Model):
    title = models.CharField(max_length=100)
    is_available = models.BooleanField(default=True)

    def __str__(self):
        return self.title

2- Borrow the book using select_for_update: In your view or business logic, use select_for_update to borrow the book and update its availability status:

from django.db import transaction
from .models import Book

def borrow_book(book_id):
    try:
        with transaction.atomic():
            book = Book.objects.select_for_update().get(pk=book_id)
            if book.is_available:
                book.is_available = False
                book.save()
                return f"Successfully borrowed {book.title}."
            else:
                return f"Sorry, {book.title} is not available for borrowing."

    except Book.DoesNotExist:
        return "Book not found."

In this example, we use a context manager (with transaction.atomic()) to ensure that the update operation is atomic and handled within a single database transaction. The select_for_update() method is called on the Book.objects queryset to obtain a lock on the selected row for update.

This example demonstrates how to use select_for_update to lock a specific row in the database during an update operation to maintain data integrity and consistency in a concurrent environment.

Django model methods

To list all the methods available for models in Django, we can refer to the methods provided by the base Model class and other related classes. Here are some common methods available for models in Django:

save(): : Saves the model instance to the database.
delete(): Deletes the model instance from the database.
full_clean(): Performs model validation, including field validation, and raises any validation errors.
clean_fields(): Performs field validation and raises any validation errors.
clean(): Performs model-specific validation and raises any validation errors.
validate_unique(): Validates the uniqueness of fields and raises any validation errors.
refresh_from_db(): Reloads the model instance's fields from the database.
get_FOO_display(): Returns the display value of a choices field, where FOO is the name of the field.
get_absolute_url(): Returns the URL for displaying the model instance.
get_field_name(): Returns the name of the field associated with a given database column name.
get_next_by_FOO(): Returns the next model instance by a specified field, where FOO is the name of the field.
get_previous_by_FOO(): Returns the previous model instance by a specified field, where FOO is the name of the field.
serializable_value(): Returns the value of a field as a serializable object.
to_json(): Returns a JSON representation of the model instance.
to_dict(): Returns a dictionary representation of the model instance.
queryset(): Is used to customize the initial queryset used in a model's manager
pre_save() and post_save(): Are signals that are emitted before and after saving a model instance
all(): Returns a QuerySet containing all objects of the model from the database
filter(): Is used to retrieve a subset of objects from the database based on specified criteria

Parametric unit tests

In Django, parametric unit tests refer to the practice of executing the same test logic with different input parameters or test data. This allows you to write more concise and reusable tests by avoiding code duplication. Here's a simple example of a parametric unit test in Django:

from django.test import TestCase

def multiply_numbers(a, b):
    return a * b

class MathTestCase(TestCase):
    def test_multiply_numbers(self):
        test_cases = [
            (2, 3, 6),  # (a, b, expected_result)
            (0, 5, 0),
            (-4, 2, -8),
            (10, -3, -30),
        ]

        for a, b, expected_result in test_cases:
            result = multiply_numbers(a, b)
            self.assertEqual(result, expected_result)

When running the test, Django's test runner will execute the test_multiply_numbers method for each test case, providing detailed feedback for each iteration.

Using parametric unit tests like this helps to reduce code duplication and makes it easier to add, modify, or remove test cases. It also provides a clear overview of which test cases pass or fail and enables you to test your code against a variety of scenarios.

FastAPI related topics:

Dependency Injection in FastAPI

In FastAPI, dependency injection is a powerful feature that allows you to declare dependencies for your route handlers, making your code modular, testable, and reusable. Dependencies can be functions or classes that provide the necessary data or services required by a route. FastAPI automatically manages the injection of these dependencies when a request is processed. Here's a simple example of dependency injection in FastAPI:

from fastapi import FastAPI, Depends

app = FastAPI()

# Dependency function
def get_query_parameter(q: str = None):
    return q or "No query parameter provided."

# Route using the dependency
@app.get("/items/")
async def read_item(commons: str = Depends(get_query_parameter)):
    return {"message": commons}

get_query_parameter is a dependency function that takes a query parameter q and returns its value or a default message if no parameter is provided. Depends is used to declare the dependency in the route handler (read_item). FastAPI will automatically execute the get_query_parameter function and inject its result into the commons parameter of the route handler. The route handler then uses the value provided by the dependency.

use cases

Authentication: You can use dependency injection to handle authentication. For example, a dependency function could verify an API key, OAuth token, or JWT token and provide user information to the route handler.
Database Connections: Dependency injection is useful for managing database connections. You can create a dependency that establishes a database connection and injects it into route handlers that require database access.
Configuration Settings: Injecting configuration settings (e.g., API keys, environment variables) into your route handlers allows for easy configuration changes without modifying the route functions.
Logging: Use dependency injection to inject a logging service into route handlers, enabling consistent logging across your application.
Caching: Dependencies can be employed for caching mechanisms. For instance, a dependency can check if the requested data is already cached and return it without hitting the actual data source.
Request Validation: You can create a dependency function to handle request validation, checking headers, parameters, and request bodies before the route handler is executed.
Authorization: Dependency injection can be used to handle authorization logic. A dependency function could check whether the authenticated user has the required permissions for a specific operation.
Rate Limiting: Implementing rate limiting is another use case. A dependency can check the rate limit for a user and prevent excessive requests.

Dependency Ordering

Dependency ordering in FastAPI refers to the order in which dependencies are executed. FastAPI executes dependencies in a top-down order, meaning that dependencies listed first are executed before those listed later. Understanding dependency ordering is crucial when you have dependencies that depend on the results of other dependencies.

Here's an example to illustrate dependency ordering in FastAPI:

from fastapi import FastAPI, Depends

app = FastAPI()

# First dependency
async def common_parameters(q: str = None, skip: int = 0, limit: int = 10):
    return {"q": q, "skip": skip, "limit": limit}

# Second dependency that depends on the result of the first one
async def query_processing(params: dict = Depends(common_parameters)):
    # Some processing using the result of the first dependency
    processed_query = f"Processed query: {params['q']}, Skip: {params['skip']}, Limit: {params['limit']}"
    return {"processed_query": processed_query}

# Route using the dependencies
@app.get("/items/")
async def read_item(query_result: dict = Depends(query_processing)):
    return query_result

common_parameters is the first dependency that provides common parameters like q, skip, and limit. query_processing is the second dependency that depends on the result of common_parameters. It performs some processing based on the parameters obtained from the first dependency. The route handler read_item depends on the result of query_processing. It uses the processed query result in the route response.

Pydantic methods in FastAPI

In FastAPI, models are typically defined using Pydantic, a data validation and parsing library. Pydantic models in FastAPI can utilize various methods to customize their behavior. These methods help in validating, parsing, and processing the data before it is used in your application. Let's explore some of these methods with examples and discuss their use cases in real-world applications.

@validator Decorator: You can use the @validator decorator to define custom validation logic for a specific field.

from typing import List
from pydantic import BaseModel, validator

class Item(BaseModel):
    name: str
    price: float

    @validator("price")
    def validate_price(cls, value):
        if value < 0:
            raise ValueError("Price must be non-negative")
        return round(value, 2)

@root_validator Decorator: Use Case: Use the @root_validator decorator to perform validation that involves multiple fields.

from pydantic import BaseModel, root_validator

class Item(BaseModel):
    name: str
    price: float
    quantity: int

    @root_validator
    def validate_total_price(cls, values):
        total_price = values['price'] * values['quantity']
        if total_price > 100:
            raise ValueError("Total price cannot exceed 100")
        return values

Decorators in FastAPI

In FastAPI, decorators are used to modify the behavior of functions or class methods. They are functions that take another function as an argument and extend or modify the behavior of that function. Decorators in FastAPI are often used for creating reusable components, handling dependencies, and adding extra functionality to route handlers or other parts of your application. Let's start with a simple example of using decorators in FastAPI:

from fastapi import FastAPI, Depends

app = FastAPI()

# Decorator to log information before and after handling a request
def log_request(func):
    async def wrapper(*args, **kwargs):
        print("Request received")
        response = await func(*args, **kwargs)
        print("Request handled")
        return response
    return wrapper

# Using the decorator for a route
@app.get("/items/", dependencies=[Depends(log_request)])
async def read_items():
    return {"message": "Items retrieved"}

Real-World Use Cases of decorators:

Authentication and Authorization: To check if a user is authenticated before allowing access to certain routes.
Rate Limiting: To limit the rate of requests to a specific route.
Logging and Monitoring: To log information about incoming requests and responses.
Validation and Transformation: To validate input data before processing it.

WebSockets in FastAPI

WebSocket is a communication protocol that provides full-duplex communication channels over a single, long-lived connection. FastAPI supports WebSocket communication, allowing you to build real-time applications, such as chat applications, live updates, notifications, or collaborative editing. Let's go through a simple example of using WebSocket in FastAPI and then discuss potential use cases.

Now, let's create a simple WebSocket application:

from fastapi import FastAPI, WebSocket

app = FastAPI()

# WebSocket endpoint
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    while True:
        data = await websocket.receive_text()
        await websocket.send_text(f"Message text was: {data}")

The /ws route is designated as a WebSocket endpoint. The websocket_endpoint function is called when a WebSocket connection is established. It accepts the WebSocket connection and enters a loop to continuously receive and send messages.

The await websocket.receive_text() line waits for incoming messages, and await websocket.send_text() sends a response back to the client.

Real-World Use Cases of websocket:

Real-Time Chat Applications: Implementing real-time chat applications where users can send and receive messages instantly.
Live Updates and Notifications: Providing live updates and notifications to users, such as news updates, sports scores, or social media notifications.
Real-Time Monitoring and Dashboards: Building real-time monitoring dashboards that display live data, such as system metrics, financial data, or IoT sensor readings.
Financial Trading Platforms: Developing financial trading platforms where real-time updates of stock prices, trades, and market data are crucial.
Job Processing and Notifications: Providing real-time updates on the progress of long-running tasks or job processing.

Asynchronous File Uploads

FastAPI supports asynchronous file uploads, allowing you to handle large file uploads efficiently. This is especially beneficial when dealing with web applications that need to process file uploads without blocking the server for other requests.

Now, let's create a FastAPI application with an asynchronous file upload endpoint:

from fastapi import FastAPI, File, UploadFile

app = FastAPI()

# Asynchronous file upload endpoint
@app.post("/uploadfile/")
async def create_upload_file(file: UploadFile = File(...)):
    return {"filename": file.filename}

The /uploadfile/ route is designated as an asynchronous file upload endpoint. The create_upload_file function is called when a POST request is made to /uploadfile/ with a file attached. The UploadFile class from FastAPI's File module is used to handle file uploads asynchronously.

Real-World Use Cases of Asynchronous File Uploads:

Large File Uploads: Allowing users to upload large files, such as images, videos, or documents, without causing timeouts or blocking other requests.
Image and Video Processing: Processing images or videos uploaded by users asynchronously, such as generating thumbnails, applying filters, or transcoding videos.
Document Processing: Handling document uploads, such as PDFs or text files, and processing them asynchronously, such as extracting text, generating previews, or converting formats.
Audio File Processing: Uploading audio files for processing, such as extracting metadata, analyzing content, or converting formats.

Security Headers

Security headers play a crucial role in web applications to enhance security by preventing certain types of attacks. FastAPI allows you to set security headers easily, providing protection against common web vulnerabilities. Now, let's create a FastAPI application with custom security headers:

from fastapi import FastAPI
from fastapi.middleware.trustedhost import TrustedHostMiddleware

app = FastAPI()

# Middleware for trusted hosts
app.add_middleware(TrustedHostMiddleware, allowed_hosts=["example.com", "sub.example.com"])

# Middleware for security headers
@app.middleware("http")
async def add_security_headers(request, call_next):
    response = await call_next(request)

    # Set security headers
    response.headers["Strict-Transport-Security"] = "max-age=31536000; includeSubDomains"
    response.headers["Content-Security-Policy"] = "default-src 'self'"
    response.headers["X-Content-Type-Options"] = "nosniff"
    response.headers["X-Frame-Options"] = "DENY"
    response.headers["X-XSS-Protection"] = "1; mode=block"
    response.headers["Referrer-Policy"] = "no-referrer"

    return response

# Route to demonstrate security headers
@app.get("/")
async def read_root():
    return {"message": "Welcome to the secure endpoint!"}

The TrustedHostMiddleware is used to enforce a list of allowed hostnames for better protection against HTTP Host header attacks. The add_security_headers function is a middleware that sets various security headers in the HTTP response.

The security headers set include:

Strict-Transport-Security (HSTS): Enforces the use of HTTPS for a specified duration.
Content-Security-Policy (CSP): Defines content security policies to mitigate XSS attacks.
X-Content-Type-Options: Prevents browsers from MIME-sniffing a response.
X-Frame-Options: Prevents the browser from rendering a page in a frame.
X-XSS-Protection: Enables a browser's built-in XSS protection.
Referrer-Policy: Controls how much information is included in the Referer header.

Implementing these security headers is an essential part of securing web applications and protecting against a wide range of common web vulnerabilities. They contribute to creating a robust and secure environment for your users and help in maintaining compliance with security standards and best practices.

Background Tasks

Background tasks in FastAPI allow you to execute functions asynchronously in the background, separate from the main request-response cycle. This is particularly useful for tasks that do not need an immediate response but can be performed in the background to improve the overall responsiveness of your application. Now, let's create a FastAPI application with a background task:

from fastapi import FastAPI, BackgroundTasks
import time

app = FastAPI()

# Background task
def process_data(background_tasks: BackgroundTasks, data: str):
    # Simulate a time-consuming task
    time.sleep(5)
    print(f"Processing data: {data}")

# Route using the background task
@app.post("/process_data/")
async def create_process_data(data: str, background_tasks: BackgroundTasks):
    background_tasks.add_task(process_data, data)
    return {"message": "Data processing started in the background"}

The process_data function is a background task that simulates a time-consuming operation. In a real-world scenario, this could be a task like sending emails, processing images, updating records in a database, etc.

The create_process_data route uses the BackgroundTasks dependency to add the process_data background task. The route returns a response immediately, and the background task runs asynchronously.

Middleware in FastAPI

Middleware in FastAPI is a function that works with every request before it is processed by any specific path operation and with every response before returning it. You can add middleware to FastAPI applications using the @app.middleware("http") decorator on top of a function. The middleware function receives the request and a call_next function that will receive the request as a parameter. It then passes the request to be processed by the rest of the application and takes the response generated by the application, allowing you to modify it further before returning it

Here's a simple example of a middleware that adds a custom header to the response to measure the processing time:

from fastapi import FastAPI, Request
import time

app = FastAPI()

@app.middleware("http")
async def add_process_time_header(request: Request, call_next):
    start_time = time.time()
    response = await call_next(request)
    process_time = time.time() - start_time
    response.headers["X-Process-Time"] = str(process_time)
    return response

Real-World Use Cases of Middlewares:

Adding custom functionality to the request-response cycle without disrupting the core framework
Separating concerns and keeping API endpoints focused on their core tasks while handling shared operations through middleware components
Enabling the addition of authentication, error handling, data transformation, and more by extending the functionality of APIs in unique ways

In addition to built-in middleware, FastAPI allows the creation of custom middleware to extend its capabilities beyond the built-in options, offering a powerful way to enhance the functionality of APIs

Permissions in FastAPI

In FastAPI, permissions are used to control access to different parts of an application based on the user's role or other factors.

Here's a simple example of using permissions in FastAPI to restrict access to a specific route based on the user's role:


from fastapi import FastAPI, Depends, HTTPException
from fastapi.security import OAuth2PasswordBearer
from typing import Optional

app = FastAPI()

oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")

async def get_current_user(token: str = Depends(oauth2_scheme)):
    # Implement logic to get user details from the token
    # Example: decode the token and fetch user details from the database
    # Return the user object if the token is valid, else raise an HTTPException
    return {"username": "user1", "role": "admin"}

def has_permission(user: dict = Depends(get_current_user), required_role: Optional[str] = None):
    if required_role and user["role"] != required_role:
        raise HTTPException(status_code=403, detail="Permission denied")
    return user

@app.get("/admin")
async def admin_route(current_user: dict = Depends(has_permission)):
    return {"message": "Welcome admin!"}

Permissions in FastAPI are crucial for implementing role-based access control (RBAC) and ensuring that users can only access the resources they are authorized to. Some real-world use cases include:

Role-Based Access Control (RBAC): Restricting access to certain routes or data based on the user's role, such as admin, user, or manager
Row-Level Security: Implementing fine-grained permissions based on the state or attributes of specific resources, such as allowing users to view, edit, or retract scientific papers based on the submission process state
Custom Business Logic: Enforcing custom business rules and access restrictions based on specific application requirements, such as allowing users to modify only their own items

By using permissions, FastAPI applications can ensure that sensitive data and functionality are protected, and that users are granted appropriate access based on their roles and other contextual factors.

Custom Validators in FastAPI

Custom validators in FastAPI can be implemented using Pydantic, which is used for data validation in FastAPI. Pydantic allows you to define custom validation rules for request data. Here's a simple example of using custom validators in FastAPI with Pydantic:

from fastapi import FastAPI
from pydantic import BaseModel, Field, ValidationError

app = FastAPI()

class Item(BaseModel):
    name: str
    price: float

    @property
    def price_must_be_positive(cls):
        if cls.price <= 0:
            raise ValueError('price must be positive')

@app.post("/items/")
async def create_item(item: Item):
    try:
        item.price_must_be_positive
    except ValueError as e:
        return {"error": str(e)}
    return {"item_name": item.name, "item_price": item.price}

In this example, a custom validator price_must_be_positive is defined within the Item class to ensure that the price of an item is positive. If the validation fails, a ValueError is raised, and an appropriate error message is returned.

Use Cases in Real-World Applications

Custom validators in FastAPI can be used in various real-world scenarios, such as:

Data Integrity Checks: Ensuring that the incoming data meets specific business rules or constraints, such as validating the format of user input, ensuring the correctness of submitted data, or enforcing specific conditions on the input data
Complex Data Validation: Implementing custom validation logic for complex data structures or interdependent fields, such as validating relationships between different fields or performing custom checks based on multiple input parameters
Integration with External Services: Validating data against external services or APIs, such as verifying the correctness of input data before making requests to external systems or services

By using custom validators, FastAPI applications can enforce specific data validation rules tailored to their business requirements, ensuring the integrity and correctness of incoming data.

FastAPI BaseSettings

In FastAPI, BaseSettings is a class provided by Pydantic for managing application settings and environment variables. It allows you to define a settings model with default values and data types, and then load and use these settings throughout your application. Here's a simple example of using BaseSettings in FastAPI:

from fastapi import FastAPI
from pydantic import BaseSettings

class Settings(BaseSettings):
    app_name: str = "Awesome API"
    admin_email: str
    items_per_user: int = 50

settings = Settings()

app = FastAPI()

@app.get("/info")
async def info():
    return {
        "app_name": settings.app_name,
        "admin_email": settings.admin_email,
        "items_per_user": settings.items_per_user
    }

In this example, the Settings class inherits from BaseSettings and defines the application settings, including default values and data types. These settings can then be used throughout the FastAPI application.

The use of BaseSettings in FastAPI has several real-world applications, including:

Centralized Settings Management: Providing a centralized and structured way to manage application settings, including environment-specific configurations, database credentials, API keys, and other parameters
Environment Variable Handling: Facilitating the loading and management of environment variables, allowing for easy configuration of application behavior across different deployment environments such as development, testing, and production

By using BaseSettings, FastAPI applications can maintain a clear and consistent approach to managing settings and configurations, ensuring that the application behaves predictably across different environments and deployment scenarios.

Dependency Caching

In FastAPI, dependency caching refers to the ability to cache the result of a dependency function so that it is not re-evaluated for each request. This can be useful for improving performance and reducing redundant computations. Here's a simple example of using dependency caching in FastAPI:

from fastapi import FastAPI, Depends
from functools import lru_cache

app = FastAPI()

@lru_cache
def expensive_operation():
    # Simulate an expensive operation
    return "result"

async def get_cached_result(result: str = Depends(expensive_operation)):
    return {"cached_result": result}

@app.get("/cached")
async def cached_endpoint(response: dict = Depends(get_cached_result)):
    return response

In this example, the @lru_cache decorator is used to cache the result of the expensive_operation function. The get_cached_result function then depends on the cached result of expensive_operation, and the cached result is used in the /cached endpoint.

Use Cases in Real-World Applications

Dependency caching in FastAPI has several real-world applications, including:

Performance Optimization: Caching the result of computationally expensive operations, such as database queries, external API calls, or complex calculations, to improve response times and reduce server load
Rate Limiting: Using caching to enforce rate limits on certain operations or endpoints by storing and checking the frequency of requests within a specific time frame
External Service Integration: Caching the results of requests to external services or APIs to reduce latency and minimize the impact of service outages or slowdowns

By leveraging dependency caching, FastAPI applications can efficiently manage the results of dependencies, reduce redundant computations, and enhance overall system performance.

Rate Limiting

Rate limiting in FastAPI allows you to restrict the number of requests a client can make to an API within a given time frame. This can be achieved using third-party packages such as fastapi-limiter. Here's a simple example of using rate limiting in FastAPI with fastapi-limiter:

from fastapi import FastAPI
from fastapi_limiter import FastAPILimiter
from fastapi_limiter.depends import RateLimiter

app = FastAPI()

# Initialize the rate limiter
FastAPILimiter.init()

@app.get("/limited")
@RateLimiter(times=5, seconds=60)
async def limited_endpoint():
    return {"message": "This endpoint is rate-limited"}

In this example, the fastapi-limiter package is used to apply rate limiting to the /limited endpoint, allowing a maximum of 5 requests per 60 seconds.

Use Cases in Real-World Applications

Rate limiting in FastAPI has several real-world applications, including:

Protection Against Abuse: Preventing abuse and misuse of APIs by limiting the number of requests from a single client within a specific time period, safeguarding the API from excessive traffic and potential denial-of-service attacks
Throttling: Managing the flow of requests to ensure that the API server is not overwhelmed by a large number of requests, maintaining system stability and preventing performance degradation during peak usage periods

By implementing rate limiting, FastAPI applications can effectively manage and control the volume of incoming requests, ensuring the stability, security, and fair usage of the API.

Cache in FastAPI

FastAPI provides various options for caching responses and function results, including third-party packages such as fastapi-cache. This tool allows you to cache FastAPI responses and function results, with support for backends like Redis, Memcached, and Amazon DynamoDB Here's a simple example of using fastapi-cache to cache a FastAPI response:

from fastapi import FastAPI
from fastapi_cache import FastAPICache
from fastapi_cache.backends.redis import RedisBackend
from fastapi_cache.decorator import cache
import aioredis

app = FastAPI()

# Initialize the cache with Redis as the backend
@app.on_event("startup")
async def startup():
    redis = await aioredis.create_redis_pool('redis://localhost')
    FastAPICache.init(RedisBackend(redis), prefix="fastapi-cache")

# Cache the response of the endpoint
@app.get("/")
@cache()
async def cached_endpoint():
    return {"message": "This response is cached"}

Use Cases in Real-World Applications

Caching in FastAPI has several real-world applications, including:

Performance Optimization: Caching responses to reduce the computational load and improve the response time of frequently accessed endpoints, enhancing the overall performance of the API
Database Query Results: Caching the results of database queries to minimize the load on the database and improve the responsiveness of data retrieval operations
External API Responses: Caching responses from external APIs to reduce latency and minimize the impact of external service outages or slowdowns, ensuring a more reliable and responsive API

By leveraging caching in FastAPI, applications can efficiently manage and optimize the handling of responses and data, leading to improved performance and a better user experience.

Custom Exceptions in FastAPI

Custom exceptions in FastAPI allow you to define and handle application-specific errors in a structured manner. You can create custom exception classes and handle them using FastAPI's exception handling mechanisms. Here's a simple example of using custom exceptions in FastAPI:

from fastapi import FastAPI, HTTPException

app = FastAPI()

class CustomException(Exception):
    def __init__(self, detail: str):
        self.detail = detail

@app.get("/custom_exception")
async def custom_exception_endpoint():
    raise CustomException(detail="Custom exception message")

In this example, a custom exception class CustomException is defined, and it is raised within the /custom_exception endpoint. You can then handle this custom exception using FastAPI's exception handling mechanisms.

Use Cases in Real-World Applications

Custom exceptions in FastAPI have several real-world applications, including:

Application-Specific Errors: Defining custom exceptions to represent specific error conditions or business logic failures within the application, providing a structured way to handle and communicate these errors to clients
Error Abstraction: Abstracting and encapsulating complex error handling logic into custom exception classes, promoting code modularity and maintainability by centralizing error management
Consistent Error Responses: Standardizing error responses by using custom exceptions to represent different error scenarios, ensuring a consistent and predictable API behavior for clients

By utilizing custom exceptions, FastAPI applications can effectively manage and communicate application-specific errors, enhancing the robustness and reliability of the API.

Optimization techniques in FastAPI

FastAPI is a high-performance web framework for building REST APIs in Python. It provides various optimization techniques to enhance the performance of applications.

Some of the Optimization Techniques

Asynchronous Programming: Utilize asynchronous functions to handle heavy request payloads and I/O bound operations
Use of Pydantic for Data Validation: Leverage Pydantic models for data validation and conversion
Dependency Caching: Reuse dependencies and cache their results within a request's scope to avoid recalculating them
BackgroundTasks: Use BackgroundTasks for handling tasks that can be run in the background to improve response times
Optimizing CPU Intensive Tasks: Send CPU intensive tasks to workers in another process for optimization
Caching Responses: Implement response caching to store the results of expensive computations and serve them directly for subsequent identical requests
Optimizing JSON Responses: Utilize FastAPI's JSON response classes for efficient serialization and deserialization of JSON data
Asynchronous Database Operations: Use asynchronous database drivers and queries to optimize database interactions and improve overall request handling
Project Structure: Organize the project structure following best practices to enhance maintainability and performance
Use of APIRouter: Utilize APIRouter to modularize and organize route handling, especially for larger applications with multiple files
Observability Tools: Employ observability tools like New Relic to gain insights into performance issues and optimize FastAPI applications
Custom Base Model: Implement a custom base model from the beginning to ensure consistency and efficiency in data handling
Use of Pydantic's BaseSettings for Configs: Leverage Pydantic's BaseSettings for efficient management of application configurations
Optimizing File Handling: Save files in chunks to optimize file handling and improve overall performance
Avoid Unnecessary Asynchronous Operations: Do not make routes async if there are only blocking I/O operations, as unnecessary async operations can impact performance
Careful Usage of Dynamic Pydantic Fields: Exercise caution when using dynamic Pydantic fields to avoid potential performance impacts
Dependency Calls Caching: Cache dependency calls to improve performance by reusing previously calculated results
Documentation: Ensure comprehensive documentation to facilitate understanding and usage, contributing to efficient development and performance

Concurrency and Parallelism In FastAPI

Concurrency and parallelism are essential concepts in FastAPI for handling multiple tasks simultaneously and improving performance. Concurrency refers to the ability to execute multiple tasks in overlapping time periods, while parallelism involves executing multiple tasks simultaneously. Real-world use cases of concurrency and parallelism in FastAPI include:

Handling Multiple Requests: Concurrency allows the server to handle multiple incoming requests simultaneously, improving the responsiveness of the application.
Asynchronous I/O Operations: Concurrency is beneficial for I/O-bound operations such as reading from databases, making API calls, or accessing files, as it allows the server to perform other tasks while waiting for I/O operations to complete.
Parallel Processing: For CPU-bound tasks like data processing or machine learning computations, parallelism can be used to execute multiple tasks concurrently, leveraging multi-core processors for improved performance.
Real-time Data Streaming: Concurrency enables the server to handle real-time data streaming, such as sending continuous updates to clients while simultaneously processing other tasks.
Background Tasks: Asynchronous operations are useful for executing background tasks, such as sending emails, processing notifications, or performing periodic maintenance tasks without blocking the main application flow

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Introduction

Python related topics:

object-oriented programming (OOP)

Inheritance

Polymorphism

Encapsulation

Abstraction

Decorators

More decorators in Python

Map function

Itertools

Lambda function

Exception Handling

SOLID principles

Python collection

frozenset in Python

Generators and Iterators

Magic methods

GIL

concurrency and parallelism

Main types of methods in python classes

Data serialization

Data class in python

Shallow copy and deep copy

Local and global variables

Comprehension

Pydantic

Args and Kwargs in Python

Operator overloading

Recursive function

Context manager

Python 3.11 over previous versions

Semaphores and Mutexes

Python built-in functions

Django related topics:

Django signals

Django middleware

Django custom template tags

Django permissions

Django custom user models

Django Custom Managers

Django Custom validators

Custom management commands

Django's Query API

Custom query expressions or Custom Lookup

Django Filterset

Context managers

Django Channels

HTTP methods in Django

annotate and aggregate in Django

Mixin in Django

Cache in Django

Django constraint

bulk creation in Django

prefetch_related and select_related in Django

Third-party packages in Django

Property decorators

WSGI and ASGI

WSGI (Web Server Gateway Interface):

ASGI (Asynchronous Server Gateway Interface):

Advanced features in ORM

Class-based views methods

Django optimization

Generic Foreign Key in Django

Django custom exceptions

select_for_update in Django

Django model methods

Parametric unit tests

FastAPI related topics:

Dependency Injection in FastAPI

use cases

Dependency Ordering

Pydantic methods in FastAPI

Decorators in FastAPI

Real-World Use Cases of decorators:

Packages