


Detailed graphic and text explanation of how to handle cross-domain request error issues in Python's bottle framework
This article mainly introduces the bottle framework of python How to deal with cross-domain request error reporting. Friends in need can refer to it
Using python When developing the bottle framework, when the front-end uses ajax for cross-domain access, the js code always fails to enter success, but enters error, and the returned status is 200. It is normal to access the url directly in the browser. After pressing F12 in the browser, you will find the following error message
XMLHttpRequest cannot load http://192.168.0.118:8081/get_mobile_number/? id=1. No 'Access-Control-Allow-Origin' header is present on the requested resource. Origin 'null' is therefore not allowed access.
If you query errors through search engines, you will find that almost all the answers you find are cross-domain problems. You only need to add the following to the code of the main file. Many solutions on foreign websites explain this
@hook('after_request') def enable_cors(): response.headers['Access-Control-Allow-Origin'] = '*'
In fact, after adding according to the found solution, an error still occurred. Looking at the http header output by the browser, we did not see the Access-Control-Allow-Origin:* we just added, as shown below:
Through DEBUG, enter the bottle source code to view
I have tested that this problem exists in the bottle framework corresponding to python2 and python3 For this problem, we change it to:
class HTTPResponse(Response, BottleException): def init(self, body='', status=None, headers=None, **more_headers): super(HTTPResponse, self).init(body, status, headers, **more_headers) def apply(self, response): response._status_code = self._status_code response._status_line = self._status_line if self._headers: if response._headers: response._headers.update(self._headers) else: response._headers = self._headers response._cookies = self._cookies response.body = self.body
When you run the code again, you can see that the ajax code is normal
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Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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