I've got the following code to run a continuous loop to fetch some content from a website: from http.cookiejar import CookieJar from urllib import request cj = CookieJar() cp = request.HTTPCookieProcessor(cj) hh = request.HTTPHandler() opener = request.build_opener(cp, hh) while True: # build url req = request.Request(url=url) p = opener.open(req) c = p.read() # process
我有下面的代码来运行一个连续的循环来从网站获取一些内容： from http.cookiejar import CookieJar from urllib import request cj = CookieJar() cp = request.HTTPCookieProcessor(cj) hh = request.HTTPHandler() opener = request.build_opener(cp, hh) while True: # build url req = request.Request(url=url) p = opener.open(req) c = p.read() # process c p.close() # check for abort
如果我创建一个包含4个worker的池并将它们设置为执行一些任务（使用pool.apply_async(..) ），那么我可以使用multiprocessing.current_process().name从内部访问每个进程的multiprocessing.current_process().name ，但是如何设置来自父进程的名称（这主要是用于日志记录）？ Process.name is just a setter, you can freely assign to it. The Pool takes an initializer argument. This can be any callable, and it'll
如果我创建一个包含4个worker的池并将它们设置为执行一些任务（使用pool.apply_async(..) ），那么我可以使用multiprocessing.current_process().name从内部访问每个进程的multiprocessing.current_process().name ，但是如何设置来自父进程的名称（这主要是用于日志记录）？ Process.name只是一个setter，你可以自由分配给它。 池接受一个initializer参数。 这可以是任何可调用的，并且在每个子进程启动时它将被调用一次。
In my experience programming with Java, I have become quite fond of the stack traces it generates when my code goes awry, but I feel that the traces generated by python are a bit lacking by comparison. For example, a trace in java might look like this: java.lang.RuntimeException at test.package.Example.c(Example.java:20) at test.package.Example.b(Example.java:15) at test.package.Exa
在我用Java编程的经验中，我非常喜欢它在代码出错时产生的堆栈跟踪，但是我觉得python生成的跟踪比较有点欠缺。 例如，java中的跟踪可能如下所示： java.lang.RuntimeException at test.package.Example.c(Example.java:20) at test.package.Example.b(Example.java:15) at test.package.Example.a(Example.java:10) 而python跟踪可能如下所示： Traceback (most recent call last): File "example.py", line 1
在Python中，如何从方法内打印当前调用堆栈（用于调试目的）。 Here's an example of getting the stack via the traceback module, and printing it: import traceback def f(): g() def g(): for line in traceback.format_stack(): print(line.strip()) f() # Prints: # File "so-stack.py", line 10, in <module> # f() # File "so-stack.py", line 4, in f # g() # File "so-stack.p
在Python中，如何从方法内打印当前调用堆栈（用于调试目的）。 以下是通过追溯模块获取堆栈并打印它的示例： import traceback def f(): g() def g(): for line in traceback.format_stack(): print(line.strip()) f() # Prints: # File "so-stack.py", line 10, in <module> # f() # File "so-stack.py", line 4, in f # g() # File "so-stack.py", line 7, in g # for line in tracebac
I have a simple test case of a function which returns a df that can potentially contain NaN. I was testing if the output and expected output were equal. >>> output Out[1]: r t ts tt ttct 0 2048 30 0 90 1 1 4096 90 1 30 1 2 0 70 2 65 1 [3 rows x 5 columns] >>> expected Out[2]: r t ts tt ttct 0 2048 30 0 90 1 1 409
我有一个函数的简单测试用例，它返回一个可能包含NaN的df。 我正在测试产量和预期产量是否相等。 >>> output Out[1]: r t ts tt ttct 0 2048 30 0 90 1 1 4096 90 1 30 1 2 0 70 2 65 1 [3 rows x 5 columns] >>> expected Out[2]: r t ts tt ttct 0 2048 30 0 90 1 1 4096 90 1 30 1 2 0 70 2 65 1 [3 rows x 5 col
Note: this is fixed in 1.4.3 or later I use the Seaborn plotting package and I just upgraded to the newest version of Matplotlib. Now, plots with dot symbols no longer render. Code that was functional before now creates blank plots, but only when Seaborn is imported. Here's some sample code: import matplotlib.pyplot as plt import matplotlib import numpy as np print matplotlib.__version
注意：这在1.4.3或更高版本中是固定的 我使用Seaborn绘图软件包，并将其升级到最新版本的Matplotlib。 现在，点符号的绘图不再呈现。 现在的代码现在可以创建空白图，但只有在导入Seaborn时才会生效。 以下是一些示例代码： import matplotlib.pyplot as plt import matplotlib import numpy as np print matplotlib.__version__ Matplotlib版本： 1.4.2 创建一个没有seaborn的情节： x = np.linspace(0,2,101) y = np
I have a pandas DataFrame with log data: host service 0 this.com mail 1 this.com mail 2 this.com web 3 that.com mail 4 other.net mail 5 other.net web 6 other.net web And I want to find the service on every host that gives the most errors: host service no 0 this.com mail 2 1 that.com mail 1 2 other.net web 2 The only solu
我有一个带有日志数据的熊猫DataFrame： host service 0 this.com mail 1 this.com mail 2 this.com web 3 that.com mail 4 other.net mail 5 other.net web 6 other.net web 我想在每个出现最多错误的主机上找到该服务： host service no 0 this.com mail 2 1 that.com mail 1 2 other.net web 2 我发现的唯一解决方案是按主机和服务进行分组，
I'm just trying to streamline one of my classes and have introduced some functionality in the same style as the flyweight design pattern. However, I'm a bit confused as to why __init__ is always called after __new__ . I wasn't expecting this. Can anyone tell me why this is happening and how I can implement this functionality otherwise? (Apart from putting the implementation into
我只是想简化我的一个类，并且以轻量级设计模式的相同风格引入了一些功能。 不过，我有点困惑，为什么__init__总是在__new__之后__new__ 。 我并不期待这一点。 任何人都可以告诉我为什么会发生这种情况，以及我如何才能实现此功能？ （除了把这个实现放到__new__里面，这感觉很__new__ 。） 这是一个例子： class A(object): _dict = dict() def __new__(cls): if 'key' in A._dict: print
How does Django write the date field when the field is marked with auto_now_add attribute? Is it like datetime.now().date() or timezone.now().date() ? In other words, which timezone does it use to get the current date? Looks like it uses datetime.date.today() , which would be the local date of the system: db/models/fields/__init__.py : class DateField(Field): ... def pre_save(sel
当字段用auto_now_add属性标记时，Django如何写日期字段？ 它是否像datetime.now().date()或timezone.now().date() ？ 换句话说，它使用哪个时区来获取当前日期？ 看起来像它使用datetime.date.today() ，这将是系统的本地日期： db / models / fields / __ init__.py ： class DateField(Field): ... def pre_save(self, model_instance, add): if self.auto_now or (self.auto_now_add and add):
I have a Pandas DataFrame as below. df A B date_time 2014-07-01 06:03:59.614000 62.1250 NaN 2014-07-01 06:03:59.692000 62.2500 NaN 2014-07-01 06:13:34.524000 62.2500 241.0625 2014-07-01 06:13:34.602000 62.2500 241.5000 2014-07-01 06:15:05.399000 62.2500 241.3750 2014-07-01 06:15:05.399000 62.2500 24
我有一个熊猫DataFrame如下。 df A B date_time 2014-07-01 06:03:59.614000 62.1250 NaN 2014-07-01 06:03:59.692000 62.2500 NaN 2014-07-01 06:13:34.524000 62.2500 241.0625 2014-07-01 06:13:34.602000 62.2500 241.5000 2014-07-01 06:15:05.399000 62.2500 241.3750 2014-07-01 06:15:05.399000 62.2500 241.2500 2