前提条件:
需要安装easy-install模块,这是一个python的模块打包工具。
首先下载easy_setup.py的源代码,下载地址:
http://pypi.python.org/pypi/setuptools
自己用记事本存放源代码用.py后缀名,在命令行执行即可,这样你就可以在python的安装目录下Python\Scripts这个目录看到有多好几个关于easy_install的文件,说明这个easy_install安装好了,那么应该如何使用这个这个安装方法呢?
打开cmd,命令行,进入到Python\Scripts目录下,这个目录下执行easy_install python-dateutil,既可以安装dateutil这个模块,easy_install pyparsing就可以安装pyparsing这个模块了。
我的机子就是:
E:\ANZHUANG\Python\Scripts>easy_install python-dateutil E:\ANZHUANG\Python\Scripts>easy_install pyparsing E:\ANZHUANG\Python\Scripts>easy_install chardet
这样执行就ok了!
py2exe模块安装:
http://prdownloads.sourceforge.net/py2exe下载对应版本的安装包。
样例代码:
新建test.py文件,内容如下:
print "show me"
新建一个mysetup.py编译文件,内容如下:
from distutils.core import setup import py2exe setup(console=["test.py"]) #注意test.py与前面新建的是一致的
运行如下命令:
>>python mysetup.py py2exe
运行结果:
当前目录下新增一个dist目录里面包含不等数目的如下类型文件:
- .exe 其中包含一个目录文件
- .pyd 已编译的py文件
- .dll 需要用到的外部DLL文件,其中包含一个pythonxx.dll
- .zip 需要用到的库文件,里面打包了所有需要库文件的编译文件
查看帮助:
python mysetup.py py2exe --help
样例目录:
安装py2exe模块后,其自带的样例存放在:lib\site-packages\py2exe\samples
说明:
编译不同类型的二进制所需要的参数是不一样的,比如:命令行程序参数名是console,而GUI程序的参数名是Windows;具体可以先参考样例或帮助命令脚本先!
用后感:
原本以为是转换完以后就只剩一个exe文件,用过才发现并没有想想象中那样把所有的内容都进行了二进制编码。呵呵,有时候想的东西太理想了,毕竟这样时最容易实现的可行方案!

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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