When it comes to the responsibility of Python computing the results, who is in charge?

Code Quality and Testing: Before release, code testing is usually done to ensure that basic bugs are avoided. For widely used libraries and frameworks, such as numpy, scipy, and pandas, the code correctness has been fully tested and verified, and users can use it with confidence.

Community maintenance and developers: There are dedicated leaders, organizations, and standards committees in the Python ecosystem to manage and maintain different libraries and frameworks. Users can get the latest library version through official channels, and can report issues and provide feedback. In addition, there are many IDEs and development tools, such as PyCharm, that provide workspace-like functionality to make the development process more convenient.

Official Technical Support and Commercial Support: Official technical support is available for Python, but this usually requires payment. In addition, some commercial companies also provide commercial support services for Python, and users can obtain more advanced support and solutions.

Application areas: Python has a wide range of applications in different fields, such as crawlers, quantitative transactions, artificial intelligence, and big data. Each field has different requirements and application scenarios for Python, but in general, Python's performance in these fields is reliable, and has a high degree of adaptability and scalability.

Visualization: There is not much difference between Python and Matlab in terms of visualization, both can handle the needs of plotting and data visualization.

Data processing and analysis: Python libraries and tools, such as pandas, provide a wealth of data processing and statistical analysis functions, making it easier to process and analyze data such as Excel.

Performance: For scenarios involving big data and complex calculations, Python can use libraries and frameworks such as TensorFlow and numpy to optimize the calculation speed and achieve performance similar to or even higher than that of Matlab.

Conversion to other languages: For cases where code conversion to other languages ​​is required, such as converting Matlab to C, Matlab's Simulink tool can directly generate C code, while the corresponding tool for Python may not provide similar functionality.

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