ToconnecttoadatabaseinPython,usetheappropriatelibraryforthedatabasetype.1.ForSQLite,usesqlite3withconnect()andmanagewithcursorandcommit.2.ForMySQL,installmysql-connector-pythonandprovidecredentialsinconnect().3.ForPostgreSQL,installpsycopg2andconfigu

def is suitable for complex functions, supports multiple lines, document strings and nesting; lambda is suitable for simple anonymous functions and is often used in scenarios where functions are passed by parameters. The situation of selecting def: ① The function body has multiple lines; ② Document description is required; ③ Called multiple places. When choosing a lambda: ① One-time use; ② No name or document required; ③ Simple logic. Note that lambda delay binding variables may throw errors and do not support default parameters, generators, or asynchronous. In actual applications, flexibly choose according to needs and give priority to clarity.

The key to dealing with API authentication is to understand and use the authentication method correctly. 1. APIKey is the simplest authentication method, usually placed in the request header or URL parameters; 2. BasicAuth uses username and password for Base64 encoding transmission, which is suitable for internal systems; 3. OAuth2 needs to obtain the token first through client_id and client_secret, and then bring the BearerToken in the request header; 4. In order to deal with the token expiration, the token management class can be encapsulated and automatically refreshed the token; in short, selecting the appropriate method according to the document and safely storing the key information is the key.

The way to access nested JSON objects in Python is to first clarify the structure and then index layer by layer. First, confirm the hierarchical relationship of JSON, such as a dictionary nested dictionary or list; then use dictionary keys and list index to access layer by layer, such as data "details"["zip"] to obtain zip encoding, data "details"[0] to obtain the first hobby; to avoid KeyError and IndexError, the default value can be set by the .get() method, or the encapsulation function safe_get can be used to achieve secure access; for complex structures, recursively search or use third-party libraries such as jmespath to handle.

Yes, you can parse HTML tables using Python and Pandas. First, use the pandas.read_html() function to extract the table, which can parse HTML elements in a web page or string into a DataFrame list; then, if the table has no clear column title, it can be fixed by specifying the header parameters or manually setting the .columns attribute; for complex pages, you can combine the requests library to obtain HTML content or use BeautifulSoup to locate specific tables; pay attention to common pitfalls such as JavaScript rendering, encoding problems, and multi-table recognition.

The "Dogcoin" in the currency circle usually refers to newly issued cryptocurrencies with extremely low market value, opaque project information, weak technical foundation or even no practical application scenarios. These tokens often appear with high-risk narratives.

Asynchronous programming is made easier in Python with async and await keywords. It allows writing non-blocking code to handle multiple tasks concurrently, especially for I/O-intensive operations. asyncdef defines a coroutine that can be paused and restored, while await is used to wait for the task to complete without blocking the entire program. Running asynchronous code requires an event loop. It is recommended to start with asyncio.run(). Asyncio.gather() is available when executing multiple coroutines concurrently. Common patterns include obtaining multiple URL data at the same time, reading and writing files, and processing of network services. Notes include: Use libraries that support asynchronously, such as aiohttp; CPU-intensive tasks are not suitable for asynchronous; avoid mixed

To view Git commit history, use the gitlog command. 1. The basic usage is gitlog, which can display the submission hash, author, date and submission information; 2. Use gitlog--oneline to obtain a concise view; 3. Filter by author or submission information through --author and --grep; 4. Add -p to view code changes, --stat to view change statistics; 5. Use --graph and --all to view branch history, or use visualization tools such as GitKraken and VSCode.