I'm having a hard time finding a good, basic example of how to parse XML in python using Element Tree. From what I can find, this appears to be the easiest library to use for parsing XML. Here is a sample of the XML I'm working with: <timeSeriesResponse> <queryInfo> <locationParam>01474500</locationParam> <variableParam>99988</variab
我很难找到一个很好的基本示例，说明如何使用Element Tree解析python中的XML。 从我所能找到的，这似乎是用于解析XML的最简单的库。 以下是我正在使用的XML示例： <timeSeriesResponse> <queryInfo> <locationParam>01474500</locationParam> <variableParam>99988</variableParam> <timeParam> <beginDateTime>2009-09-24T15:15:55
I have an xml doc that I am trying to parse using Etree.lxml <Envelope xmlns="http://www.example.com/zzz/yyy"> <Header> <Version>1</Version> </Header> <Body> some stuff <Body> <Envelope> My code is: path = "path to xml file" from lxml import etree as ET parser = ET.XMLParser(ns_clean=True) dom = ET.parse(path, parser) dom.getroot(
我有一个XML文档，我试图用Etree.lxml解析 <Envelope xmlns="http://www.example.com/zzz/yyy"> <Header> <Version>1</Version> </Header> <Body> some stuff <Body> <Envelope> 我的代码是： path = "path to xml file" from lxml import etree as ET parser = ET.XMLParser(ns_clean=True) dom = ET.parse(path, parser) dom.getroot() 当我尝试获取dom.
I know the title to this may seem general, but I will try to be as specific as possible about what I am looking to accomplish. I would like OpenCV contours to detect geometric objects, closed contours (ie drawing a contour on a piece of paper), and objects that are highly concave such as stars and crosses. In addition, I would like it to detect alphanumerics. When I say closed contours I mean
我知道这个标题可能看起来很普遍，但我会尽可能具体地描述我期待完成的内容。 我希望OpenCV轮廓可以检测几何对象，封闭轮廓（即在一张纸上绘制轮廓）以及高度凹陷的对象，如星星和十字。 另外，我希望能够检测字母数字。 当我说封闭轮廓时，我的意思是一条封闭的绘制路径。 我已经尝试了多种过滤方法，例如： -Solidity = contour_area / hull_area->几何形状倾向于朝向1 - 等高线/围绕椭圆 - >这是一个猜测和检
I migrated away from Google App Engine several months ago. But I am still relying on it for authentication, because my users are identified by their user_id attribute on GAE. For this purpose my (now external) applications redirect the user to a Google App Engine application using a encrypted, signed and timestamped login request. The GAE application then performs the login using GAE's &q
几个月前，我从Google App Engine迁移出去。 但是我仍然依靠它进行身份验证，因为我的用户在GAE上通过其user_id属性进行标识。 为此，我的（现在是外部的）应用程序使用加密，签名和时间戳登录请求将用户重定向到Google App Engine应用程序。 GAE应用程序然后使用GAE的“用户”服务执行登录。 在GAE上成功登录后，用户将再次使用加密的，带符号和时间戳的响应重定向到我的外部应用程序。 基本的实现可以在这里和这里找到。
When playing around with the Python interpreter, I stumbled upon this conflicting case regarding the is operator: If the evaluation takes place in the function it returns True , if it is done outside it returns False . >>> def func(): ... a = 1000 ... b = 1000 ... return a is b ... >>> a = 1000 >>> b = 1000 >>> a is b, func() (False, True) Since
在与Python解释器一起玩时，我偶然发现了这个与is运算符相冲突的例子： 如果评估发生在函数中，则返回True ，如果在外部完成，则返回False 。 >>> def func(): ... a = 1000 ... b = 1000 ... return a is b ... >>> a = 1000 >>> b = 1000 >>> a is b, func() (False, True) 由于is运算符为所涉及的对象计算id() ，这意味着a和b在函数func内声明时指向同一个int实例，但相
I'm working on a documentation API for Python I'm calling Python Docs, and I've noticed that almost all built-ins can't be accessed by my static analysis suite, because they are almost exclusively C modules. As far as I can remember, I can't think of any exceptions to this rule. The first part of my question is simply, does being a built-in module presuppose being a C-exten
我正在为Python编写一个文档API我打电话给Python Docs，我注意到几乎所有的内置函数都不能被我的静态分析套件访问，因为它们几乎都是C模块。 据我记忆，我不能想到这个规则的任何例外。 我的问题的第一部分很简单，是一个内置的模块是否预先假定为CPython中的C扩展？ 假设这可能是这样的话，我编译了一个新的pypy运行时，并尝试使用我的项目与来自pypy的内置插件。 我很惊讶地发现这也不起作用。 如果PyPy模块是纯Python
This question already has an answer here: Why does comparing strings in Python using either '==' or 'is' sometimes produce a different result? 14 answers For all built-in Python objects (like strings, lists, dicts, functions, etc.), if x is y, then x==y is also True. Not always. NaN is a counterexample. But usually, identity ( is ) implies equality ( == ). The converse i
这个问题在这里已经有了答案： 为什么使用'=='或'is'比较Python中的字符串有时会产生不同的结果？ 14个答案 对于所有内置的Python对象（如字符串，列表，字典，函数等），如果x是y，那么x == y也是True。 不总是。 NaN是一个反例。 但通常，身份（ is ）意味着平等（ == ）。 反过来是不正确的：两个不同的对象可以具有相同的值。 另外，即使在比较int或布尔值时，默认情况下只使用'=='通常
How can I do case insensitive string comparison in Python? I would like to encapsulate comparison of a regular strings to a repository string using in a very simple and Pythonic way. I also would like to have ability to look up values in a dict hashed by strings using regular python strings. 假设ASCII字符串： string1 = 'Hello' string2 = 'hello' if string1.lower() == string2.lower(): prin
我如何在Python中进行不区分大小写的字符串比较？ 我想用一种非常简单的Pythonic方式将常规字符串与存储库字符串进行比较。 我也希望能够使用普通的python字符串来查看由字符串散列的字典中的值。 假设ASCII字符串： string1 = 'Hello' string2 = 'hello' if string1.lower() == string2.lower(): print "The strings are the same (case insensitive)" else: print "The strings are not the same (case insensitiv
In Python Pandas, what's the best way to check whether a DataFrame has one (or more) NaN values? I know about the function pd.isnan , but this returns a DataFrame of booleans for each element. This post right here doesn't exactly answer my question either. jwilner's response is spot on. I was exploring to see if there's a faster option, since in my experience, summing flat a
在Python Pandas中，检查DataFrame是否具有一个（或多个）NaN值的最佳方法是什么？ 我知道函数pd.isnan ，但是这会为每个元素返回一个布尔值的DataFrame。 这篇文章在这里并不完全回答我的问题。 jwilner的回应是现货。 我正在探索是否有更快的选择，因为根据我的经验，总结平面阵列（奇怪）比计数快。 这段代码似乎更快： df.isnull().values.any() 例如： In [2]: df = pd.DataFrame(np.random.randn(1000,1000)) In
I have written a little program which emulates a bluetooth keyboard (executed by a RPi) in Python 3 using the built-in socket library. It works fine despite one feature: When a real Bluetooth keyboard connects to a device, both the device and the keyboard somehow remember the other device. when the keyboard then is shut down and restarted, it automatically reconnects to the devece without hav
我已经使用内置套接字库在Python 3中编写了一个模拟蓝牙键盘（由RPi执行）的小程序。 尽管有一个特点，它仍可以正常工作 当真正的蓝牙键盘连接到设备时，设备和键盘都会以某种方式记住其他设备。 当键盘被关闭并重新启动时，它会自动重新连接到启动，而不必明确触发它。 （我试图再次连接到设备[没有绑定，听，接受]，但它不工作） 那么，我该如何实现这样的行为呢？ 启动脚本时，它应该在设备站点上没有手动连接请求的