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    Python

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    • Refreshing Python Basics
      • 1.1Python Data Structures
      • 1.2Syntax and Semantics
      • 1.3Conditionals and Loops
    • Introduction to Object-Oriented Programming
      • 2.1Understanding Class and Objects
      • 2.2Design Patterns
      • 2.3Inheritance, Encapsulation, and Polymorphism
    • Python Libraries
      • 3.1Numpy and Matplotlib
      • 3.2Pandas and Seaborn
      • 3.3SciPy
    • Handling Files and Exception
      • 4.1Reading, writing and manipulating files
      • 4.2Introduction to Exceptions
      • 4.3Handling and raising Exceptions
    • Regular Expressions
      • 5.1Introduction to Regular Expressions
      • 5.2Python’s re module
      • 5.3Pattern Matching, Substitution, and Parsing
    • Databases and SQL
      • 6.1Introduction to Databases
      • 6.2Python and SQLite
      • 6.3Presentation of Data
    • Web Scraping with Python
      • 7.1Basics of HTML
      • 7.2Introduction to Beautiful Soup
      • 7.3Web Scraping Case Study
    • Python for Data Analysis
      • 8.1Data cleaning, Transformation, and Analysis using Pandas
      • 8.2Data visualization using Matplotlib and Seaborn
      • 8.3Real-world Data Analysis scenarios
    • Python for Machine Learning
      • 9.1Introduction to Machine Learning with Python
      • 9.2Scikit-learn basics
      • 9.3Supervised and Unsupervised Learning
    • Python for Deep Learning
      • 10.1Introduction to Neural Networks and TensorFlow
      • 10.2Deep Learning with Python
      • 10.3Real-world Deep Learning Applications
    • Advanced Python Concepts
      • 11.1Generators and Iterators
      • 11.2Decorators and Closures
      • 11.3Multithreading and Multiprocessing
    • Advanced Python Concepts
      • 12.1Generators and Iterators
      • 12.2Decorators and Closures
      • 12.3Multithreading and Multiprocessing
    • Python Project
      • 13.1Project Kick-off
      • 13.2Mentor Session
      • 13.3Project Presentation

    Advanced Python Concepts

    Understanding Decorators and Closures in Python

    general-purpose programming language

    General-purpose programming language.

    Python is a powerful and flexible language that offers a variety of advanced concepts. Among these are decorators and closures, which are powerful tools that can make your code more efficient and easier to manage. This article will provide a comprehensive overview of these two concepts.

    Closures in Python

    A closure in Python is a function object that has access to variables from its enclosing lexical scope, even when the function is called outside that scope. This means that the function remembers the state of these variables, even if they are no longer present in memory.

    Practical Use of Closures

    Closures are used for a variety of purposes. They can be used to replace hard-coded constants in a function, to implement data hiding and encapsulation, or to implement function factories.

    Decorators in Python

    Decorators are a significant part of Python. In simple terms, a decorator is a function that modifies the behavior of another function. They allow us to wrap another function in order to extend the behavior of the wrapped function, without permanently modifying it.

    Writing Your Own Decorators

    Creating your own decorator involves defining a function that takes another function as an argument, defines a nested function inside it, and returns this nested function. Here's a simple example:

    def my_decorator(func): def wrapper(): print("Something is happening before the function is called.") func() print("Something is happening after the function is called.") return wrapper @my_decorator def say_hello(): print("Hello!") say_hello()

    In this example, my_decorator is a decorator that wraps the say_hello function and modifies its behavior.

    Practical Use of Decorators

    Decorators are widely used in Python development. They are used for logging, enforcing access control and authentication, rate limiting, caching, and much more. They allow for cleaner and more readable code by abstracting away boilerplate patterns.

    Understanding Decorators with Parameters

    Decorators can also take parameters. This allows you to pass values to your decorator to control its behavior. To create a decorator with parameters, you need to create a function that returns a decorator, rather than creating the decorator directly.

    Chaining Decorators in Python

    Python also allows for decorators to be chained. This means that a function can be wrapped by multiple decorators, with each decorator modifying the behavior of the function in some way.

    In conclusion, understanding closures and decorators is crucial for advanced Python programming. They provide a way to make your code more efficient, readable, and manageable. By mastering these concepts, you can write more professional and efficient Python code.

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