一、python多线程
因为CPython的实现使用了Global Interpereter Lock(GIL),使得python中同一时刻只有一个线程在执行,从而简化了python解释器的实现,且python对象模型天然地线程安全。如果你想你的应用程序在多核的机器上使用更好的资源,建议使用multiprocessing或concurrent.futures.processpoolexecutor。但是如果你的程序是IO密集型,则使用线程仍然是很好的选择。
二、python多线程使用的两种方法
实例:
import threading
import time
def worker(num):
print (threading.currentThread().getName() + ' start')
time.sleep(10)
print (threading.currentThread().getName() + ' running')
print (threading.currentThread().getName() + " " + str(num))
print (threading.currentThread().getName() + ' exit')
def deamon():
print (threading.currentThread().getName() + ' start')
time.sleep(20)
print (threading.currentThread().getName() + ' running')
print (threading.currentThread().getName() + ' exit')
print(threading.currentThread().getName())
d = threading.Thread(name='deamon', target=deamon)
d.setDaemon(True)
d.start()
w = threading.Thread(name='worker', target=worker, args=(10,))
w.start()
class myWorker(threading.Thread):
def __init__(self, num):
threading.Thread.__init__(self)
self.num = num
self.thread_stop = False
def run(self):
print (self.getName()+' start')
time.sleep(30)
print (self.getName()+' running')
print (self.getName()+" " + str(self.num))
print (self.getName()+' exit')
mw = myWorker(30)
mw.setName("MyWorker")
mw.start()
print(threading.currentThread().getName())
print("All threads:")
print("------------")
for th in threading.enumerate():
print(th.getName())
print("------------")
d.join()
w.join()
mw.join()
print(threading.currentThread().getName())
运行结果如下:
1)python线程使用的两种方法:
**直接调用threading.Thread来构造thread对象,Thread的参数如下:
class threading.Thread(group=None, target=None, name=None, args=(), kwargs={})
group为None;
target为线程将要执行的功能函数;
name为线程的名字,也可以在对象构造后调用setName()来设定;
args为tuple类型的参数,可以为多个,如果只有一个也的使用tuple的形式传入,例如(1,);
kwargs为dict类型的参数,也即位命名参数;
**实现自己的threading.Thread的子类,需要重载__init__()和run()。
2)threading.Thread对象的其他方法:
start(),用来启动线程;
join(), 等待直到线程结束;
setDeamon(), 设置线程为deamon线程,必须在start()调用前调用,默认为非demon。
注意: python的主线程在没有非deamon线程存在时就会退出。
3)threading的静态方法:
threading.current_thread() , 用来获得当前的线程;
threading.enumerate() , 用来多的当前存活的所有线程;
threading.Timer 定时器,其实是thread的一个字类型,使用如下:
def hello(): print("hello, world")
t = Timer(30.0, hello)
t.start()
4)logging是线程安全的
logging 模块是线程安全的,所以可以使用logging来帮助调试多线程程序。
import logging
logging.basicConfig(level=logging.DEBUG,
format="(%(threadName)-10s : %(message)s",
)
logging.debug("wait_for_event_timeout starting")

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Python's real-world applications include data analytics, web development, artificial intelligence and automation. 1) In data analysis, Python uses Pandas and Matplotlib to process and visualize data. 2) In web development, Django and Flask frameworks simplify the creation of web applications. 3) In the field of artificial intelligence, TensorFlow and PyTorch are used to build and train models. 4) In terms of automation, Python scripts can be used for tasks such as copying files.

Python is widely used in data science, web development and automation scripting fields. 1) In data science, Python simplifies data processing and analysis through libraries such as NumPy and Pandas. 2) In web development, the Django and Flask frameworks enable developers to quickly build applications. 3) In automated scripts, Python's simplicity and standard library make it ideal.

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.


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