Verify email address: Use verification_email library for validity checks

This article aims to solve the problem of installation failure due to Bcolz compilation errors when installing Zipline. By lowering the Cython version and installing pip with get-pip.py, you can effectively avoid compilation errors. At the same time, for possible blosc error: conflicting types for ‘_xgetbv’ error, a solution to replace the basic image is provided to ensure the smooth installation of Zipline.

This article discusses the problem that when using Python multi-process in Windows environment, dynamically created classes cannot be correctly serialized and deserialized by child processes. By analyzing the causes of errors, this article provides a solution to ensure that dynamically created classes can be defined in the parent process and used safely in the child process, while avoiding the performance losses caused by repeated creation.

Absoluteimportsspecifythefullpathfromthetop-levelpackage,whilerelativeimportsusedotstoreferencemodulesrelativetothecurrentpackage;1.Absoluteimportsareclearerandpreferredforreadability;2.Relativeimportsareusefulfornestedpackagesandrefactoring;3.Relati

First, use the requests library to send an HTTP request to obtain JSON data, and then parse the response into a Python dictionary or list through the response.json() method; 1. Make sure that the requests library is installed before sending the request and use try-except to handle network exceptions; 2. Check response.status_code or use response.raise_for_status() to catch HTTP errors; 3. When parsing data using response.json(), you need to capture JSONDecodeError to prevent invalid JSON; 4. Use the .get() method to avoid errors that do not exist when accessing data;

Using module-level instances is the simplest and most in line with Python habits. By defining class instances in modules, using the feature of Python module loading only once to ensure global uniqueness, such as creating a config.py file and defining a config instance in it. Other modules share the same object when importing. This method is simple, readable and thread-safe, and is suitable for most practical scenarios. In addition, it can also be implemented by rewriting the __new__ method, using decorators, metaclasses, etc., where the __new__ method controls the uniqueness of the instance through class variables, but pays attention to thread safety issues. The decorator method can be reused across classes but may affect garbage collection. The metaclass method supports inheritance and centralized control but replication.

The most efficient way to find common elements of two or more lists is to use the intersection operation of the set. 1. Convert the list to a set and use the & operator or .intersection() method to find the intersection, for example, common=list(set(list1)&set(list2)); 2. For multiple lists, you can use set(list1).intersection(set(list2), set(list3)) or set.intersection(*map(set,lists)) to achieve dynamic processing; 3. Pay attention to the disordered and automatic deduplication. If you need to maintain the order, you can traverse the original list and combine the set judgment.

Run pipinstall-rrequirements.txt to install the dependency package. It is recommended to create and activate the virtual environment first to avoid conflicts, ensure that the file path is correct and that the pip has been updated, and use options such as --no-deps or --user to adjust the installation behavior if necessary.

This article aims to provide a simple and straightforward tutorial on how to convert byte stream data from a smart meter into a hexadecimal string in Python 3. By using the bytes.hex() method, it is easy to convert byte data into a readable hexadecimal format and solve encoding problems that may be encountered during the Python 2 to Python 3 migration. The article will provide sample code and considerations to help readers better understand and apply this technology.