What __init__.py has to do with Python?

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.

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.

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.

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,

To create a Python virtual environment, you can use the venv module. The steps are: 1. Enter the project directory to execute the python-mvenvenv environment to create the environment; 2. Use sourceenv/bin/activate to Mac/Linux and env\Scripts\activate to Windows; 3. Use the pipinstall installation package, pipfreeze>requirements.txt to export dependencies; 4. Be careful to avoid submitting the virtual environment to Git, and confirm that it is in the correct environment during installation. Virtual environments can isolate project dependencies to prevent conflicts, especially suitable for multi-project development, and editors such as PyCharm or VSCode are also

Use multiprocessing.Queue to safely pass data between multiple processes, suitable for scenarios of multiple producers and consumers; 2. Use multiprocessing.Pipe to achieve bidirectional high-speed communication between two processes, but only for two-point connections; 3. Use Value and Array to store simple data types in shared memory, and need to be used with Lock to avoid competition conditions; 4. Use Manager to share complex data structures such as lists and dictionaries, which are highly flexible but have low performance, and are suitable for scenarios with complex shared states; appropriate methods should be selected based on data size, performance requirements and complexity. Queue and Manager are most suitable for beginners.