众所周知,程序在启动后,各个程序文件都会被加载到内存中,这样如果程序文本再次变化,对当前程序的运行没有影响,这对程序是一种保护。
但是,对于像python这样解释执行的语言,我们有时候会用到“from 模块 import 变量名”这样的形式,如果这个变量直接被定义在文件当中,那么这些变量在程序开始时就会被定义、赋值,运行过程中值不变。如果打算在运行过程中对这个模块进行重写,那么更改后的变量值是无法被使用的。
对于这个问题,可以换一种思路,将这个模块中的变量定义在函数里,而函数是在程序运行的时候动态执行的,这样就能够获取到变量的最新值。下面是例子:
首先,不使用函数的情况:
#model1.py
p_hello = 'hello world!'
#test1.py
from model1 import p_hello
file = open('model1.py', 'w')
file.write("p_hello = '%s!'"%('hello you'))
file.close()
print p_hello
这样,执行test1.py的时候,出现的结果仍然是'hello world',而非‘hello you',说明变量已经加载到内存中,尽管该模块的文件在硬盘上已经被重写。
接下来,使用函数的情况:
#model1.py
def rule():
p_hello = 'hello world!'
return locals()
#test1.py
from model1 import rule
file = open('model1.py', 'w')
file.write('def rule():\n')
file.write(" p_hello = '%s!'\n"%('hello you'))
file.write(" return locals()\n")
file.close()
p_hello = rule()['p_hello']
print p_hello
这样,print出来的结果就是hello you 了,因为在运行的时候,先执行了一遍这个函数,再通过函数获取了这个变量,这样就会获得新值。

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

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.


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