最近开发了一个 Google Analytics 相关的应用,但需要在 Windows 下部署,结合网上的相关经验,最终选择了 apache+mod_wsgi 这样的配置。
修改python应用
代码如下:
Note that mod_wsgi requires that the WSGI application entry point be called 'application'. If you want to call it something else then you would need to configure mod_wsgi explicitly to use the other name.
(via: wiki)
因为 mod_wsgi 默认要求入口名称为 application 所以我们需要对自己的 python web 应用做一些修改。
假设我们使用flask 搭建的应用,而默认的入口名称为 app, 建立一个 wsgi_handler.wsgi
import sys, os sys.path.insert(0, os.path.dirname(__file__)) from application import app as application
下载安装 httpd
应用的入口修改好之后,就需要安装 apache 和 mod_wsgi 了,我使用的是32位的系统,64位系统下载的安装包可能 与32位的不同。
打开页面 http://apache.dataguru.cn//httpd/binaries/win32/,下载 httpd-2.2.22-win32-x86-no_ssl.msi, 下载后运行程序,按提示安装,具体过程这里不详述。
安装并配置 mod_wsgi
目前 Windows 下对 python 支持的最好的应该就是 [mod_wsgi][mw] 了。
下载 https://code.google.com/p/modwsgi/downloads/detail?name=mod_wsgi-win32-ap22py27-3.3.so
将下载的文件重命名为 mod_wsgi.so 后移动到 apache 的 modules 目录:
在 conf/httpd.conf 中加入如下配置
代码如下:
LoadModule wsgi_module modules/mod_wsgi.so
配置应用 vhost
在 conf/httpd.conf 中启用 vhosts 配置文件
代码如下:
# Virtual hosts
Include conf/extra/httpd-vhosts.conf
编辑 conf\extra\httpd-vhosts.conf 删除无效的示例代码,并加入应用的配置
代码如下:
NameVirtualHost *:5000
ServerName localhost
WSGIScriptAlias / E:\Projects\ga-data\wsgi_handler.wsgi
Order deny,allow
Allow from all
其中 E:\Projects\ga-data 替换成应用真实的路径,尽量避免将应用放在中文或者有包含空格的路径中
接下来启动 Apache 并访问 http://localhost:5000 即可。

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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