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Install pyodbc: Use the pipinstallpyodbc command to install the library; 2. Connect SQLServer: Use the connection string containing DRIVER, SERVER, DATABASE, UID/PWD or Trusted_Connection through the pyodbc.connect() method, and support SQL authentication or Windows authentication respectively; 3. Check the installed driver: Run pyodbc.drivers() and filter the driver name containing 'SQLServer' to ensure that the correct driver name is used such as 'ODBCDriver17 for SQLServer'; 4. Key parameters of the connection string

Use httpx.AsyncClient to efficiently initiate asynchronous HTTP requests. 1. Basic GET requests manage clients through asyncwith and use awaitclient.get to initiate non-blocking requests; 2. Combining asyncio.gather to combine with asyncio.gather can significantly improve performance, and the total time is equal to the slowest request; 3. Support custom headers, authentication, base_url and timeout settings; 4. Can send POST requests and carry JSON data; 5. Pay attention to avoid mixing synchronous asynchronous code. Proxy support needs to pay attention to back-end compatibility, which is suitable for crawlers or API aggregation and other scenarios.

This article aims to help SQLAlchemy beginners resolve the "RemovedIn20Warning" warning encountered when using create_engine and the subsequent "ResourceClosedError" connection closing error. The article will explain the cause of this warning in detail and provide specific steps and code examples to eliminate the warning and fix connection issues to ensure that you can query and operate the database smoothly.

shutil.rmtree() is a function in Python that recursively deletes the entire directory tree. It can delete specified folders and all contents. 1. Basic usage: Use shutil.rmtree(path) to delete the directory, and you need to handle FileNotFoundError, PermissionError and other exceptions. 2. Practical application: You can clear folders containing subdirectories and files in one click, such as temporary data or cached directories. 3. Notes: The deletion operation is not restored; FileNotFoundError is thrown when the path does not exist; it may fail due to permissions or file occupation. 4. Optional parameters: Errors can be ignored by ignore_errors=True

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.

Install the corresponding database driver; 2. Use connect() to connect to the database; 3. Create a cursor object; 4. Use execute() or executemany() to execute SQL and use parameterized query to prevent injection; 5. Use fetchall(), etc. to obtain results; 6. Commit() is required after modification; 7. Finally, close the connection or use a context manager to automatically handle it; the complete process ensures that SQL operations are safe and efficient.

The method of filling Excel data into web forms using Python is: first use pandas to read Excel data, and then use Selenium to control the browser to automatically fill and submit the form; the specific steps include installing pandas, openpyxl and Selenium libraries, downloading the corresponding browser driver, using pandas to read Name, Email, Phone and other fields in the data.xlsx file, launching the browser through Selenium to open the target web page, locate the form elements and fill in the data line by line, using WebDriverWait to process dynamic loading content, add exception processing and delay to ensure stability, and finally submit the form and process all data lines in a loop.

Using PandasStyling in JupyterNotebook can achieve the beautiful display of DataFrame. 1. Use highlight_max and highlight_min to highlight the maximum value (green) and minimum value (red) of each column; 2. Add gradient background color (such as Blues or Reds) to the numeric column through background_gradient to visually display the data size; 3. Custom function color_score combined with applymap to set text colors for different fractional intervals (≥90 green, 80~89 orange, 60~79 red,