相对于C++的继承编写,Python更简洁,而且效率也是很高的,下面编写一个简单Python的继承例子。
代码如下:
#!/usr/bin/python
#filename: pyclass.py
class Member:
def __init__(self, name, age):
self.name = name
self.age = age
print 'Member init:%s' % self.name
def tell(self):
print 'Name:%s,Age:%d' % (self.name, self.age),
class Student(Member):
def __init__(self, name, age, marks):
Member.__init__(self, name, age)
self.marks = marks
print 'Student init:%s' % self.name
def tell(self):
Member.tell(self)
print 'Marks:%d' % self.marks
class Teacher(Member):
def __init__(self, name, age, salary):
Member.__init__(self, name, age)
self.salary = salary
print 'Teacher init:%s' % self.name
def tell(self):
Member.tell(self)
print 'Salary:%d' % self.salary
s = Student('Tom', 20, 80)
t = Teacher('Mrs.Huang', 30, 50000)
members = [s, t]
for mem in members:
mem.tell()
运行效果:
代码如下:
[root@localhost hhl]# python pyclass.py
Member init:Tom
Student init:Tom
Member init:Mrs.Huang
Teacher init:Mrs.Huang
Name:Tom,Age:20 Marks:80
Name:Mrs.Huang,Age:30 Salary:50000
我们同样编写同样效果的C++例子:
代码如下:
//filename: class.cpp
#include
#include
using namespace std;
class Member
{
public:
Member(char *n, int a);
void tell();
private:
char name[10];
int age;
};
Member::Member(char *n, int a)
{
memcpy(name, n, sizeof(name));
age = a;
cout
}
void Member::tell()
{
cout
}
class Student:public Member
{
public:
Student(char *n, int a, int m);
void tell_s();
private:
int marks;
};
Student::Student(char *n, int a, int m):Member(n, a)
{
marks = m;
cout
}
void Student::tell_s()
{
Member::tell();
cout
}
class Teacher:public Member
{
public:
Teacher(char *n, int a, int s);
void tell_t();
private:
int salary;
};
Teacher::Teacher(char *n, int a, int s):Member(n, a)
{
salary = s;
cout
}
void Teacher::tell_t()
{
Member::tell();
cout
}
int main(void)
{
Student s("Tom", 20, 80);
Teacher t("Mrs.Huang", 30, 50000);
s.tell_s();
t.tell_t();
return 0;
}
运行效果:
代码如下:
[root@localhost hhl]# ./class
Member init:Tom
Student init:Tom
Member init:Mrs.Huang
Teacher init:Mrs.Huang
Name:Tom,Age:20,Marks:80
Name:Mrs.Huang,Age:30,Salary:50000
这两者的运行效果是一样的,但是python更简洁些。。。

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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