Python multi-threading and multi-process: Learning resource guide to quickly master the essence of concurrent programming

python Multi-threading and multi-process are the basis of Concurrent programming, which can significantly improve the performance of the program. MultiThreading allows multiple tasks to be executed simultaneously in one process, while multiprocessing allows multiple processes to be executed simultaneously on one computer.

To learn Python multi-threading and multi-process, you can use the following resources:

• Tutorial

  • Python multi-threading tutorial
  • Python multi-process tutorial
  • Concurrent Programming Basics

• books

  • 《Python ConcurrencyProgramming: From Getting Started to Mastery》
  • "Python Multi-threading and Multi-process Practical Combat"
  • 《Concurrent Programming in Practice》

• video

  • Python multi-threading and multi-process video tutorial
  • Python multi-process programming video tutorial
  • Concurrent Programming Basics Video Tutorial

• project

  • Python multi-threading and multi-process examples
  • Python multi-process example
  • Concurrent Programming Project

After mastering Python multi-threading and multi-process, you can apply this knowledge in actual projects to improve the performance of the program. For example, a computationally intensive task can be broken down into multiple subtasks, and then multiple threads or processes can be used to execute these subtasks simultaneously, thereby shortening the running time of the program.

The following are some code examples demonstrating Python multithreading and multiprocessing:

# 多线程示例

import threading

def task1():
print("Task 1")

def task2():
print("Task 2")

thread1 = threading.Thread(target=task1)
thread2 = threading.Thread(target=task2)

thread1.start()
thread2.start()

# 多进程示例

import multiprocessing

def task1():
print("Task 1")

def task2():
print("Task 2")

process1 = multiprocessing.Process(target=task1)
process2 = multiprocessing.Process(target=task2)

process1.start()
process2.start()

Hope these resources can help you quickly master Python multi-threading and multi-process, and apply this knowledge in actual projects to improve program performance.

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