What can python do? What is a python crawler?

Factory mode is used to decouple the object creation process. The answer is: it encapsulates object creation logic through factory methods so that the client does not need to know the specific class. 1. Define the abstract base class Document and declare the generate method; 2. Create PDFDocument and WordDocument to implement generate; 3. Build the DocumentFactory class and use the static method create_document to return the corresponding instance according to the type; 4. The client calls the factory method to obtain the object and use it. The support type can be dynamically expanded through the registration mechanism without modifying the factory code. It is suitable for scenarios where object creation logic is complex or needs to be expanded flexibly. The final output is "GeneratingP"

This article details how to perform complex conditional grouping calculations in a data frame using Pandas, especially when the statistical results need to be filtered based on specific conditions within the group (such as unique months) and broadcast the calculated values ​​back to the original row. The tutorial will focus on explaining groupby() combined with transform() method, as well as how to cleverly use where() for conditional assignment, ultimately achieving efficient and flexible data processing.

This article aims to provide a detailed tutorial on accessing Google Firestore named databases in Python. We will focus on how to use the database parameters of the Google-cloud-firestore SDK to connect to non-default databases, and explore how it integrates with the firebase-admin SDK. Help developers efficiently manage and operate multiple database instances in Firestore with sample code and best practices.

InheritanceinPythonallowsasubclasstoinheritattributesandmethodsfromasuperclass,promotingcodereuseandestablishinghierarchy,asshownwithDog,Cat,andBirdinheritingfromAnimal.2.Thesuper()functionisusedtoextendparentfunctionality,ensuringproperinitializatio

When using patch, you must correctly specify the dependency path imported in the test module, such as patch('mymodule.requests.get') instead of patch('requests.get'); 2. You can simulate it through the decorator @patch or context manager withpatch; 3. Set the return value through mock.return_value, such as the return result of status_code or json(); 4. You can use assert_called_once_with and other methods to verify the call situation; 5. For clearer writing, patch.object can be used instead of string paths. Master these key points

This tutorial details how to completely disable the defaults defaults default channel in Conda environment management by modifying the environment.yml file. For commercial use or specific channel requirements, even when configuring a shared environment, it is possible to ensure that all packages only come from the specified channel, avoiding unexpected activation of the defaults channel, thereby achieving the purity and controllability of the environment.

This article aims to solve how Conda users completely disable the defaults channel when creating an environment using the environment.yml file. By adding nodefaults channels to the environment.yml file, you can ensure that only specified channels are used during the environment creation process, avoiding accidental use of defaults channels, thus ensuring the consistency and reproducibility of the environment, especially in commercial environments.

This article describes how to convert JSON data to DataFrame using Python and Pandas libraries. By parsing JSON strings and using the pd.DataFrame function, you can combine the data part and column name part in JSON data to quickly build a structured DataFrame, which facilitates subsequent data analysis and processing. The article provides detailed code examples to help readers understand and apply the method.