The settings.json file is located in the user-level or workspace-level path and is used to customize VSCode settings. 1. User-level path: Windows is C:\Users\\AppData\Roaming\Code\User\settings.json, macOS is /Users//Library/ApplicationSupport/Code/User/settings.json, Linux is /home//.config/Code/User/settings.json; 2. Workspace-level path: .vscode/settings in the project root directory

Use datetime.strptime() to convert date strings into datetime object. 1. Basic usage: parse "2023-10-05" as datetime object through "%Y-%m-%d"; 2. Supports multiple formats such as "%m/%d/%Y" to parse American dates, "%d/%m/%Y" to parse British dates, "%b%d,%Y%I:%M%p" to parse time with AM/PM; 3. Use dateutil.parser.parse() to automatically infer unknown formats; 4. Use .d

Go generics are supported since 1.18 and are used to write generic code for type-safe. 1. The generic function PrintSlice[Tany](s[]T) can print slices of any type, such as []int or []string. 2. Through type constraint Number limits T to numeric types such as int and float, Sum[TNumber](slice[]T)T safe summation is realized. 3. The generic structure typeBox[Tany]struct{ValueT} can encapsulate any type value and be used with the NewBox[Tany](vT)*Box[T] constructor. 4. Add Set(vT) and Get()T methods to Box[T] without

Yes, a common CSS drop-down menu can be implemented through pure HTML and CSS without JavaScript. 1. Use nested ul and li to build a menu structure; 2. Use the:hover pseudo-class to control the display and hiding of pull-down content; 3. Set position:relative for parent li, and the submenu is positioned using position:absolute; 4. The submenu defaults to display:none, which becomes display:block when hovered; 5. Multi-level pull-down can be achieved through nesting, combined with transition, and add fade-in animations, and adapted to mobile terminals with media queries. The entire solution is simple and does not require JavaScript support, which is suitable for large

json.loads() is used to parse JSON strings into Python data structures. 1. The input must be a string wrapped in double quotes and the boolean value is true/false; 2. Supports automatic conversion of null→None, object→dict, array→list, etc.; 3. It is often used to process JSON strings returned by API. For example, response_string can be directly accessed after parsing by json.loads(). When using it, you must ensure that the JSON format is correct, otherwise an exception will be thrown.

@property decorator is used to convert methods into properties to implement the reading, setting and deletion control of properties. 1. Basic usage: define read-only attributes through @property, such as area calculated based on radius and accessed directly; 2. Advanced usage: use @name.setter and @name.deleter to implement attribute assignment verification and deletion operations; 3. Practical application: perform data verification in setters, such as BankAccount to ensure that the balance is not negative; 4. Naming specification: internal variables are prefixed, property method names are consistent with attributes, and unified access control is used to improve code security and maintainability.

itertools.combinations is used to generate all non-repetitive combinations (order irrelevant) that selects a specified number of elements from the iterable object. Its usage includes: 1. Select 2 element combinations from the list, such as ('A','B'), ('A','C'), etc., to avoid repeated order; 2. Take 3 character combinations of strings, such as "abc" and "abd", which are suitable for subsequence generation; 3. Find the combinations where the sum of two numbers is equal to the target value, such as 1 5=6, simplify the double loop logic; the difference between combinations and arrangement lies in whether the order is important, combinations regard AB and BA as the same, while permutations are regarded as different;

Python is an efficient tool to implement ETL processes. 1. Data extraction: Data can be extracted from databases, APIs, files and other sources through pandas, sqlalchemy, requests and other libraries; 2. Data conversion: Use pandas for cleaning, type conversion, association, aggregation and other operations to ensure data quality and optimize performance; 3. Data loading: Use pandas' to_sql method or cloud platform SDK to write data to the target system, pay attention to writing methods and batch processing; 4. Tool recommendations: Airflow, Dagster, Prefect are used for process scheduling and management, combining log alarms and virtual environments to improve stability and maintainability.