Apache's default web root directory is /var/www/html in most Linux distributions. This is because the Apache server provides files from a specific document root directory. If the configuration is not customized, systems such as Ubuntu, CentOS, and Fedora use /var/www/html, while macOS (using Homebrew) is usually /usr/local/var/www, and Windows (XAMPP) is C:\xampp\htdocs; to confirm the current path, you can check the Apache configuration file such as httpd.conf or apache2.conf, or create a P with phpinfo()

Yes,aPythonclasscanhavemultipleconstructorsthroughalternativetechniques.1.Usedefaultargumentsinthe__init__methodtoallowflexibleinitializationwithvaryingnumbersofparameters.2.Defineclassmethodsasalternativeconstructorsforclearerandscalableobjectcreati

__post_init__ is used in Python's dataclass to run custom logic after object initialization. The problem it solves is that when you need to perform verification, calculate derivative properties or set internal state after field initialization, you do not need to manually rewrite __init__ and retain the initialization function automatically generated by dataclass. The usage method is to define the __post_init__ method, which Python will automatically call after the default __init__ is executed. Applicable scenarios include field verification, derivative attribute calculation and repeated logic avoidance. Not recommended for initialization that depends on external resources or overly complex. Notes include: __post_init__ does not accept parameters other than self

When do you need to use default_factory? When you want to assign a default mutable object to a field, you should use default_factory. How to set default value with default_factory? You can pass any object that has no arguments to default_factory, such as built-in types, functions, or lambda expressions. Common usages include: 1. Initialize to an empty list: default_factory=list; 2. Initialize to an empty dictionary: default_factory=dict; 3. Initialize to a specific structure: default_factory=lambda:[1,2,3]; 4

Function annotations are a feature used in Python to add metadata, which can improve code readability and maintenance. It does not force type checking, but provides type prompts or other information for parameters and return values. Its uses include: 1. Improve code readability and enable developers to clarify the expected input and output of functions; 2. Use it in conjunction with static type checking tools (such as mypy and pyright); 3. Used by frameworks (such as FastAPI) to generate documents or verify requests. Annotations do not affect the operation of the program. For example, name:str and ->str in defgreet(name:str)->str are only additional information, and the actual parameter transmission can still be of other types. Use suggestions include keeping the annotations concise and combining types and types

In Python, a nested class refers to a class defined inside another class, which is suitable for cases where the inner class is only related to the outer class context. Its core purpose is to improve code readability through logical grouping, such as: classInner: defined in classOuter:. There is no need for special syntax when creating it, it can be instantiated through Outer.Inner() or outer_instance.Inner(), but the inner class does not automatically access outer instance variables by default, and the outer instance needs to be manually passed. Usage scenarios include: inner class is only used by outer class, logical encapsulation requirements, and improved readability; situations that should be avoided include: inner class reusing elsewhere, multi-level nesting, and inner class frequently depends on outer state. Python

Python's map() function implements efficient data conversion by acting as specified functions on each element of the iterable object in turn. 1. Its basic usage is map(function,iterable), which returns a "lazy load" map object, which is often converted to list() to view results; 2. It is often used with lambda, which is suitable for simple logic, such as converting strings to uppercase; 3. It can be passed in multiple iterable objects, provided that the number of function parameters matches, such as calculating the discounted price and discount; 4. Usage techniques include combining built-in functions to quickly type conversion, handling None situations similar to zip(), and avoiding excessive nesting to affect readability. Mastering map() can make the code more concise and professional

The key to using Python to call WebAPI to obtain data is to master the basic processes and common tools. 1. Using requests to initiate HTTP requests is the most direct way. Use the get method to obtain the response and use json() to parse the data; 2. For APIs that need authentication, you can add tokens or keys through headers; 3. You need to check the response status code, it is recommended to use response.raise_for_status() to automatically handle exceptions; 4. Facing the paging interface, you can request different pages in turn and add delays to avoid frequency limitations; 5. When processing the returned JSON data, you need to extract information according to the structure, and complex data can be converted to Data