Python code running assistant allows you to enter Python code online and then execute the code through a Python script that runs natively. The principle is as follows:
Enter the code on the webpage:
Click the Run button and the code is sent to the Python code running assistant running on the local machine;
Python code running assistant saves the code as a temporary file, and then calls the Python interpreter to execute the code;
The web page displays the code execution results:
Download
Right click and save the target as: learning.py
Alternate download address: learning.py
Full code:
#!/usr/bin/env python3 # -*- coding: utf-8 -*- r''' learning.py A Python 3 tutorial from http://www.liaoxuefeng.com Usage: python3 learning.py ''' import sys def check_version(): v = sys.version_info if v.major == 3 and v.minor >= 4: return True print('Your current python is %d.%d. Please use Python 3.4.' % (v.major, v.minor)) return False if not check_version(): exit(1) import os, io, json, subprocess, tempfile from urllib import parse from wsgiref.simple_server import make_server EXEC = sys.executable PORT = 39093 HOST = 'local.liaoxuefeng.com:%d' % PORT TEMP = tempfile.mkdtemp(suffix='_py', prefix='learn_python_') INDEX = 0 def main(): httpd = make_server('127.0.0.1', PORT, application) print('Ready for Python code on port %d...' % PORT) httpd.serve_forever() def get_name(): global INDEX INDEX = INDEX + 1 return 'test_%d' % INDEX def write_py(name, code): fpath = os.path.join(TEMP, '%s.py' % name) with open(fpath, 'w', encoding='utf-8') as f: f.write(code) print('Code wrote to: %s' % fpath) return fpath def decode(s): try: return s.decode('utf-8') except UnicodeDecodeError: return s.decode('gbk') def application(environ, start_response): host = environ.get('HTTP_HOST') method = environ.get('REQUEST_METHOD') path = environ.get('PATH_INFO') if method == 'GET' and path == '/': start_response('200 OK', [('Content-Type', 'text/html')]) return [b'<html><head><title>Learning Python</title></head><body><form method="post" action="/run"><textarea name="code" style="width:90%;height: 600px"></textarea><p><button type="submit">Run</button></p></form></body></html>'] if method == 'GET' and path == '/env': start_response('200 OK', [('Content-Type', 'text/html')]) L = [b'<html><head><title>ENV</title></head><body>'] for k, v in environ.items(): p = '<p>%s = %s' % (k, str(v)) L.append(p.encode('utf-8')) L.append(b'</html>') return L if host != HOST or method != 'POST' or path != '/run' or not environ.get('CONTENT_TYPE', '').lower().startswith('application/x-www-form-urlencoded'): start_response('400 Bad Request', [('Content-Type', 'application/json')]) return [b'{"error":"bad_request"}'] s = environ['wsgi.input'].read(int(environ['CONTENT_LENGTH'])) qs = parse.parse_qs(s.decode('utf-8')) if not 'code' in qs: start_response('400 Bad Request', [('Content-Type', 'application/json')]) return [b'{"error":"invalid_params"}'] name = qs['name'][0] if 'name' in qs else get_name() code = qs['code'][0] headers = [('Content-Type', 'application/json')] origin = environ.get('HTTP_ORIGIN', '') if origin.find('.liaoxuefeng.com') == -1: start_response('400 Bad Request', [('Content-Type', 'application/json')]) return [b'{"error":"invalid_origin"}'] headers.append(('Access-Control-Allow-Origin', origin)) start_response('200 OK', headers) r = dict() try: fpath = write_py(name, code) print('Execute: %s %s' % (EXEC, fpath)) r['output'] = decode(subprocess.check_output([EXEC, fpath], stderr=subprocess.STDOUT, timeout=5)) except subprocess.CalledProcessError as e: r = dict(error='Exception', output=decode(e.output)) except subprocess.TimeoutExpired as e: r = dict(error='Timeout', output='执行超时') except subprocess.CalledProcessError as e: r = dict(error='Error', output='执行错误') print('Execute done.') return [json.dumps(r).encode('utf-8')] if __name__ == '__main__': main()
Run
Run the command in the directory where learning.py is stored:
C:UsersmichaelDownloads> python learning.py
If you see Ready for Python code on port 39093..., it means the operation is successful. Do not close the command line window, just minimize it and run it in the background:
Try the effect
Requires a browser that supports HTML5:
IE>= 9
Firefox
Chrome
Sarafi

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.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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