Page 6-Python Tutorial Programming Tutorials: Learn Python Tutorial Online-php.cn

Article Tags

Home

Technical Articles

Backend Development

Python Tutorial

Containerizing Python Applications for Production

Container deployment of Python applications ensures environmental consistency and automated scalability. Containers are isolated from the running environment, solve the problem of inconsistency in dependencies, and can achieve automatic scaling and health checks with Docker or Kubernetes. When writing Dockerfiles, you should use multi-stage construction, lightweight basic mirroring, non-root user operation, and optimize cache; the example uses two-stage construction to improve efficiency and security. During deployment, the log output must be unified to stdout/stderr, use environment variables to manage the configuration, set resource restrictions and health check interfaces, and pay attention to time zone configuration. It is recommended to use Gunicorn Uvicorn or Hypercorn to start applications, or to combine Supervisord management in production environments.

Jul 29, 2025 am 02:18 AM

python

python delete file example

To delete a file, the most commonly used method in Python is to use os.remove(), os.unlink(), or pathlib.Path.unlink(), and the pathlib module is recommended. ① When using os.remove(), you should first use os.path.exists() or os.path.isfile() to check whether the file exists to avoid throwing FileNotFoundError; ②pathlib.Path.unlink() is a Python 3.4 recommended method, with clearer syntax and should be used with Path.is_file(); ③ To ensure safety, try-except can be used

Jul 29, 2025 am 02:17 AM

python http.server example

To quickly build a simple HTTP server, just use the built-in http.server module in Python. 1. Run python-mhttp.server8000 in the terminal, and the service is started by default with the current directory as the root directory. The browser accesses http://localhost:8000 to view the content; 2. You can specify the IP such as python-mhttp.server8000-bind192.168.1.100 through --bind to enable other devices to access; 3. Replace the port to avoid conflicts, such as using 3000 or 8080; 4. You can inherit SimpleHTTPRequestHand when customizing the server script.

Jul 29, 2025 am 02:14 AM

python datetime timedelta example

In Python, datetime and timedelta are used to process datetime and time difference calculations. 1. You can use timedelta to add or subtract the time, such as 1 day later, 3 hours ago, 1 week and 2 days later; 2. You can calculate the difference between two time points, get the full number of days through diff.days, and get the total number of seconds; 3. You can determine whether the time expires, such as the validity period of the token of 30 minutes; 4. You can generate a date list of the past 7 days, and combine strftime to format the output; timedelta supports days, seconds, microseconds, milliseconds, minute

Jul 29, 2025 am 02:12 AM

Optimizing Python for CPU-Bound Tasks

PythoncanbeoptimizedforCPU-heavytasksbyfollowingspecificstrategies.First,useefficientdatastructureslikesetsformembershipchecks,dictionariesforkey-valuelookups,andNumPyarraysfornumericaldata.Second,avoidtheGILbyusingmultiprocessingwithPooltoparalleliz

Jul 29, 2025 am 02:05 AM

python pandas explode example

pandas.DataFrame.explode() method is used to expand columns containing iterable objects such as lists, tuples, etc. into multiple rows. Each row corresponds to one element, and the values of other columns are automatically aligned; 1. Basic usage: After using explore for purchases columns, each product is individually formed into rows, and the user value is copied accordingly; 2. Actual applications include processing nested array data returned by the API, such as line items or tags; 3. Multi-label data (such as movie types) can be expanded and used for statistics or recommendation systems; 4. Note that the empty list becomes NaN after expansion, None remains NaN, and can only expand one column at a time; this method is suitable for cleaning and analyzing nested structure data, often with groupby and value_c

Jul 29, 2025 am 01:55 AM

python deepcopy example

deepcopy will recursively copy the object and all its sub-objects to create completely independent copies; 1. Direct assignment only copy references, and modification of the original object will affect the copy; 2. shallow copy only copy outer objects, sub-objects are still shared, and the nested structure will change synchronously; 3. Deepcopy is implemented through copy.deepcopy(), and the modification of the copy does not affect the original data, and is suitable for nested lists, dictionaries or custom class instances; usage scenarios include data preprocessing, state rollback, multi-threaded data delivery, etc.; using deepcopy for custom objects such as Person class instances can ensure that the attributes are modified independently, and deepcopy can handle circular references, but the performance is slow and it is impossible to copy special objects such as file handles, so you need to pay attention to potential errors.

Jul 29, 2025 am 01:52 AM

python deep copy

python sqlalchemy orm example

First, install SQLAlchemy and use its ORM to define the model, create tables, insert and query data: 1. Define the User model and create the SQLite database and table structure; 2. Create a session and add a single or multiple user data and submit it; 3. Use the query method to query all, conditions, scope and fuzzy matching users; 4. Update data by modifying object properties or calling the update method; 5. Use the delete method to delete the specified user; 6. Close the session after the operation is completed; finally submit the changes through session.commit(), and the entire process realizes the database addition, deletion, modification and query operations, and can be adapted to MySQL or PostgreSQL to simplify database operations in an object-oriented manner.

Jul 29, 2025 am 01:46 AM

python mock open file example

Use @patch('mymodule.open',new_callable=mock_open) to simulate file operations to avoid real reading and writing files; 2. Set the mock file content through the mock_open read_data parameter, such as read_data='debug=True'; 3. Verify the return result and open call parameters when reading the function; 4. Get the handle through mock_file() and verify the write method call; 5. Use side_effect to return multiple mock_open instances when mocking multiple reads of different files; 6. The key is that the patch path must point to using open

Jul 29, 2025 am 01:41 AM

java programming

Customizing Python Interpreters for Embedded Systems

To customize Python interpreters in embedded systems, you must first clarify the resource limitations of the target platform and select the appropriate interpreter such as CPython or MicroPython; then cut the standard library and built-in modules, delete unnecessary files, disable unused functions, and replace simplified modules; finally perform cross-compilation and deployment optimization to control firmware size, startup time and memory management. The specific steps are: 1. Determine the hardware architecture and resource situation and select the appropriate interpreter version; 2. Simplify the volume by deleting files, modifying configurations, replacing modules, etc.; 3. Prepare a cross-compilation tool chain to optimize code compression, startup process and memory usage during deployment, and ensure that it meets the needs of the embedded environment.

Jul 29, 2025 am 01:38 AM

Handling Large Datasets with Python Dask for Scalability

Dask is chosen because it is compatible with Pandas and NumPy, supports delayed execution and parallel processing, and is suitable for big data scenarios. 1. Can process data beyond memory; 2. Use multi-core CPU to improve speed; 3. Support gradual migration to distributed environments; 4. DaskDataFrame is similar to Pandas, but requires call .compute() to execute; 5. Avoid frequent compute(), reasonable partitioning, and optimize time series queries; 6. Optional schedulers include threads, processes or clusters; 7. Performance optimization includes controlling partition size, column cropping, using Parquet format and cache intermediate results.

Jul 29, 2025 am 01:23 AM

python multiprocessing queue example

Use multiprocessing.Queue to safely pass data between multiple processes. 1. The producer process sends data through queue.put(), and the consumer process receives it through queue.get(); 2. None can be used as an end signal to notify consumers to stop. Multiple producers need to send the corresponding number of None; 3. Setting timeout in queue.get() can avoid infinite blocking and improve program robustness; 4. Queue automatically handles lock mechanism, supports serializable objects, and is an effective way to cross-platform IPC. End logic needs to be reasonably managed to prevent deadlocks. The program exits normally after all processes end.

Jul 29, 2025 am 01:23 AM

python multi-Progress

python strategy pattern example

The policy pattern is a behavioral design pattern, which allows the specific implementation of algorithms or behaviors to be selected at runtime; 1. Define the abstract policy class DiscountStrategy and declare the apply_discount method; 2. Create specific policy classes such as NoDiscount, MemberDiscount, VIPDiscount and HolidayDiscount, each implementing different discount logic; 3. Create a context class Order, receive a policy instance and call its apply_discount method to calculate the price; 4. Dynamically switch the strategy through the set_strategy method to achieve flexible expansion; this mode complies with the principle of opening and closing, and new policies do not need to be modified.

Jul 29, 2025 am 01:16 AM

php java programming

Optimizing Python for Parallel Data Processing

Pythoncanhandleparalleldataprocessingeffectivelybyusingtherighttoolsandapproaches.First,usemultiprocessinginsteadofthreadingforCPU-boundtaskstobypasstheGlobalInterpreterLock(GIL).Second,leveragemultiprocessing.Poolforparallelmap/reducepatternswhilebe

Jul 29, 2025 am 01:11 AM