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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 Multithreading and Multiprocessing in Python

    smallest sequence of programmed instructions that can be managed independently by a scheduler

    Smallest sequence of programmed instructions that can be managed independently by a scheduler.

    In the world of Python, multithreading and multiprocessing are powerful tools that programmers can use to handle multiple tasks simultaneously. This article will delve into these concepts, their differences, and when to use each one.

    Introduction to Threading in Python

    Threading is a technique in programming where a single set of code can be used by several processors at different stages of execution. Python's threading module allows for the creation and management of threads.

    A thread, in the simplest terms, is a separate flow of execution. This means that your program will have two things happening at once. However, for most Python 3 implementations, different threads do not actually execute at the same time, they merely appear to.

    Creating and Managing Threads

    Creating threads in Python involves making threading.Thread() instances and then calling .start() to start the thread’s activity. To manage threads, you can use .join() which tells the program to wait for the thread to finish before moving on.

    Here's a simple example:

    import threading def print_numbers(): for i in range(10): print(i) def print_letters(): for letter in 'abcdefghij': print(letter) thread1 = threading.Thread(target=print_numbers) thread2 = threading.Thread(target=print_letters) thread1.start() thread2.start() thread1.join() thread2.join()

    Introduction to Multiprocessing

    Multiprocessing, on the other hand, involves the use of multiple processors in a system. Each processor runs a different process, and each process operates independently of the others. Python's multiprocessing module allows for the creation and management of processes.

    Difference between Multithreading and Multiprocessing

    The key difference between multithreading and multiprocessing lies in the way they use system resources. In multithreading, threads share memory space, which makes it faster to start, stop, and communicate between threads. However, due to Python's Global Interpreter Lock (GIL), only one thread can execute at a time.

    Multiprocessing, on the other hand, involves multiple processes, each with its own Python interpreter and memory space. This allows processes to run truly concurrently, bypassing the GIL, but starting, stopping, and communication between processes is slower.

    When to Use Multithreading and When to Use Multiprocessing

    The choice between multithreading and multiprocessing depends on the nature of the task.

    • For I/O-bound tasks (like downloading files from the internet, reading from the disk, etc.), where the program spends most of its time waiting for input/output operations, multithreading is usually a better choice.

    • For CPU-bound tasks (like computations, data processing, etc.), where the program spends most of its time doing CPU operations, multiprocessing can help you get around the GIL and take full advantage of multiple CPU cores.

    Real-world Use Cases of Multithreading and Multiprocessing

    Multithreading is commonly used in scenarios such as multi-user web servers, GUI applications, and in situations where program execution needs to be paused or resumed.

    Multiprocessing is used in data-intensive tasks that require parallel processing like data analysis, machine learning model training, image processing, etc.

    By understanding these concepts, you can write more efficient Python code that can handle a higher volume of tasks and perform complex computations faster.

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