一、引言
在arcgis打开一个图层的属性表,可以对属性表的某个字段进行计算,但是在平常一般都是使用arcgis提供的字段计算器的界面进行傻瓜式的简答的赋值操作,并没有使用到脚本对字段值进行逻辑的操作。由于最近一直在学python脚本,刚好又碰上一好基友需要我的助攻(使用arcgis制图),这就用上了。本以为能够轻松搞定的,没想到搬石头砸脚了,下面就来说我是如何被砸脚的吧。
二、问题描述:将test字段中值为“湖南”的变为“湖南省”。
这个逻辑是相当的简单,使用python写一个对应的方法为:
def cal(value): if(value=='湖南'): return value + '省' else: return value
在arcgis中运行python代码如下图:
注意:引用属性字段的值得方法为!test!
点击确定,结果弹出一个错误,没有提示具体是什么错误,最后在查找了一下资料,原来是python使用中文的时候一定要进行转码。
三、解决方案
于是将上面代码中有中文出现的地方对其进行转码就好了。更改后的代码如下:
def cal(value): if(value=='湖南'.decode('utf-8')): return value + '省'.decode('utf-8') else: return value
这下就没有错误了,需要更改的就是在字符串”湖南“和‘省”后面添加 decode('utf-8')方法对其进行编码。
这下就解决了在arcgis中python脚本处理中文的问题。
四、小结
在arcgis中使用python脚本,只要碰到中文就需要对其进行decode('utf-8')进行转码。
另外,在arcgis中提供的python脚本编辑器超级难用,所以可以先使用好用的python编辑,将逻辑代码编写好,然后复制进去,然后运行,又快又好。

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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.

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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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