Quickly understand the four data types of 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.

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,

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 the Pythonschedule library to easily implement timing tasks. First, install the library through pipinstallschedule, then import the schedule and time modules, define the functions that need to be executed regularly, then use schedule.every() to set the time interval and bind the task function. Finally, call schedule.run_pending() and time.sleep(1) in a while loop to continuously run the task; for example, if you execute a task every 10 seconds, you can write it as schedule.every(10).seconds.do(job), which supports scheduling by minutes, hours, days, weeks, etc., and you can also specify specific tasks.

When processing large data sets that exceed memory in Python, they cannot be loaded into RAM at one time. Instead, strategies such as chunking processing, disk storage or streaming should be adopted; CSV files can be read in chunks through Pandas' chunksize parameters and processed block by block. Dask can be used to realize parallelization and task scheduling similar to Pandas syntax to support large memory data operations. Write generator functions to read text files line by line to reduce memory usage. Use Parquet columnar storage format combined with PyArrow to efficiently read specific columns or row groups. Use NumPy's memmap to memory map large numerical arrays to access data fragments on demand, or store data in lightweight data such as SQLite or DuckDB.

Python's logging module can write logs to files through FileHandler. First, call the basicConfig configuration file processor and format, such as setting the level to INFO, using FileHandler to write app.log; secondly, add StreamHandler to achieve output to the console at the same time; Advanced scenarios can use TimedRotatingFileHandler to divide logs by time, for example, setting when='midnight' to generate new files every day and keep 7 days of backup, and make sure that the log directory exists; it is recommended to use getLogger(__name__) to create named loggers, and produce

This article provides detailed solutions and best practices for the problem that dataset names conflict with group names when operating HDF5 files using the h5py library. The article will analyze the causes of conflicts in depth and provide code examples to show how to effectively avoid and resolve such problems to ensure proper reading and writing of HDF5 files. Through this article, readers will be able to better understand the HDF5 file structure and write more robust h5py code.