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.

In Python, there are two main ways to call the __init__ method of the parent class. 1. Use the super() function, which is a modern and recommended method that makes the code clearer and automatically follows the method parsing order (MRO), such as super().__init__(name). 2. Directly call the __init__ method of the parent class, such as Parent.__init__(self,name), which is useful when you need to have full control or process old code, but will not automatically follow MRO. In multiple inheritance cases, super() should always be used consistently to ensure the correct initialization order and behavior.

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.

ToscrapeawebsitethatrequiresloginusingPython,simulatetheloginprocessandmaintainthesession.First,understandhowtheloginworksbyinspectingtheloginflowinyourbrowser'sDeveloperTools,notingtheloginURL,requiredparameters,andanytokensorredirectsinvolved.Secon

In Python's for loop, use the continue statement to skip some operations in the current loop and enter the next loop. When the program executes to continue, the current loop will be immediately ended, the subsequent code will be skipped, and the next loop will be started. For example, scenarios such as excluding specific values ??when traversing the numeric range, skipping invalid entries when data cleaning, and skipping situations that do not meet the conditions in advance to make the main logic clearer. 1. Skip specific values: For example, exclude items that do not need to be processed when traversing the list; 2. Data cleaning: Skip exceptions or invalid data when reading external data; 3. Conditional judgment pre-order: filter non-target data in advance to improve code readability. Notes include: continue only affects the current loop layer and will not

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.