下面就先定义一个函数:
def foo():
print('function')
foo()
在上述代码中,定义了一个名为foo的函数,这个函数没有参数。最后一行代码的功能是调用这个函数。这是一个函数的最简单形式。下面来介绍一下有参数的函数:
def foo():
print('function')
def foo1(a,b):
print(a+b)
foo()
foo1(1,2)
foo1就是一个有参数的函数,使用foo1(1,2)就可以调用这个有参的函数了。
在程序中,有变量存在,就会涉及到变量的作用域的问题。在Python中,变量的作用域分三个等级:global、local和nonlocal。
global:顾名思义,表示全局变量。即这个变量在python中处于最高层次上,也就是这个变量的定义层次最高,而不是在函数或类中。
local:局部变量,被定义在函数之中。
nonlocal:这是一个相对的概念。在python中,函数内部可以嵌套定义内部函数,这样函数内部的变量相对于函数内部的内嵌函数来讲就是nonlocal的。
下面,给出相关的程序来说明,首先看一下全局和局部变量:
x = 1
y = 2
def foo(x):
print(x)
print(y)
print('***********')
x = 3
global y
y = 3
print(x)
print(y)
print('***********')
foo(x)
print(x)
print(y)
#************************
#运行结果
1
2
***********
3
3
***********
1
3
在上述程序中,定义了两个全局变量x和y, 在函数foo内部,也定义了一个局部变量x。根据运行结果可知,在foo内部,变量x是真正的局部变量。因为对其所做的修改并没有对全局变量x产生影响。另外,如果在foo内部需要使用全局变量,则需要使用global关键字。global y的意图就是声明变量y为外部声明过的全局变量y。所以,在foo内部对y进行修改后,在foo外部仍然有影响。因为foo修改的是全局变量。
再来看一下nonlocal:
def out():
z = 3
def inner():
nonlocal z
z = 4
print('inner function and z = {0}'.format(z))
inner()
print('out function and z = {0}'.format(z))
out()
#**********
#运行结果
inner function and z = 4
out function and z = 4

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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