


python magic method: make custom classes more like built-in types
Python’s magic methods are those predefined functions of type __XXX__ in Python.
The biggest advantage of using Python's magic methods is that python provides simple methods to make objects behave like built-in types.
__str__ function
The __str__ function is used to process the output content when printing the instance itself. If this function is not overridden, an object name and memory address are output by default.
For example:
>>> class Student(object): ... def __init__(self,name): ... self._name = name ... >>> print Student()
Output:
<__main__.Student object at 0x0000000002A929E8>.
So how do we make the output results more readable? We can override the __str__ function. For example
>>> class Student(object): ... def __init__(self, name): ... self._name = name ... def __str__(self): ... return "I'm a student, named %s" % self._name ... >>> print Student("Charlie")
The output result is:
I'm a student, named Charlie.
When we apply the str() function to the object, In fact, the __str__ function of the object is called.
__repr__ Function
__repr__ also serializes objects, but __repr__ More for the python compiler. __str__ is more readable.
When we apply the repr() function to an object, what we call is actually the __repr__ function of the function.
Paired with repr() is the eval() function. The eval() function converts the serialized object back into an object. The premise is that the object implements the __repr__ function.
The above paragraph is based on my own understanding, I don’t know whether it is right or wrong.
>>> item = [1,2,3] >>> repr(item) '[1, 2, 3]' >>> other_item = eval(repr(item)) >>> other_item[1] 2
__iter__ function
We often use for...in... to iterate over lists or tuples. That is list inherits from Iterable. Iterable implements the __iter__ function.
If you want to turn a custom object into an iterable object, you must implement two methods: __iter__ and next.
__iter__ function returns an object. When iterating, the next function will be continuously called to get the next value until StopIteration is captured.
Teacher Liao Xuefeng’s tutorial writes the __next__ method, I don’t know why.
class Fib(object): def __init__(self): self.a, self.b = 0, 1 def __iter__(self): return self def next(self): self.a, self.b = self.b, self.a + self.b if self.a > 10000: raise StopIteration return self.a for i in Fib(): print i
__getitem__ function
The above implements the object by implementing the __iter__ function Iterate.
#So how to implement the object to extract elements by subscript.
This is done by implementing the __getitem__ method of the object.
Let’s give one

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



There is no built-in sum function in C language, so it needs to be written by yourself. Sum can be achieved by traversing the array and accumulating elements: Loop version: Sum is calculated using for loop and array length. Pointer version: Use pointers to point to array elements, and efficient summing is achieved through self-increment pointers. Dynamically allocate array version: Dynamically allocate arrays and manage memory yourself, ensuring that allocated memory is freed to prevent memory leaks.

Although distinct and distinct are related to distinction, they are used differently: distinct (adjective) describes the uniqueness of things themselves and is used to emphasize differences between things; distinct (verb) represents the distinction behavior or ability, and is used to describe the discrimination process. In programming, distinct is often used to represent the uniqueness of elements in a collection, such as deduplication operations; distinct is reflected in the design of algorithms or functions, such as distinguishing odd and even numbers. When optimizing, the distinct operation should select the appropriate algorithm and data structure, while the distinct operation should optimize the distinction between logical efficiency and pay attention to writing clear and readable code.

There is no absolute salary for Python and JavaScript developers, depending on skills and industry needs. 1. Python may be paid more in data science and machine learning. 2. JavaScript has great demand in front-end and full-stack development, and its salary is also considerable. 3. Influencing factors include experience, geographical location, company size and specific skills.

!x Understanding !x is a logical non-operator in C language. It booleans the value of x, that is, true changes to false, false changes to true. But be aware that truth and falsehood in C are represented by numerical values rather than boolean types, non-zero is regarded as true, and only 0 is regarded as false. Therefore, !x deals with negative numbers the same as positive numbers and is considered true.

There is no built-in sum function in C for sum, but it can be implemented by: using a loop to accumulate elements one by one; using a pointer to access and accumulate elements one by one; for large data volumes, consider parallel calculations.

The H5 page needs to be maintained continuously, because of factors such as code vulnerabilities, browser compatibility, performance optimization, security updates and user experience improvements. Effective maintenance methods include establishing a complete testing system, using version control tools, regularly monitoring page performance, collecting user feedback and formulating maintenance plans.

Copying and pasting the code is not impossible, but it should be treated with caution. Dependencies such as environment, libraries, versions, etc. in the code may not match the current project, resulting in errors or unpredictable results. Be sure to ensure the context is consistent, including file paths, dependent libraries, and Python versions. Additionally, when copying and pasting the code for a specific library, you may need to install the library and its dependencies. Common errors include path errors, version conflicts, and inconsistent code styles. Performance optimization needs to be redesigned or refactored according to the original purpose and constraints of the code. It is crucial to understand and debug copied code, and do not copy and paste blindly.

How to obtain dynamic data of 58.com work page while crawling? When crawling a work page of 58.com using crawler tools, you may encounter this...
