步骤2:定义一个函数来获取进程信息 接下来,我们定义一个名为get_running_processes的函数,来获取并返回当前正在运行的进程列表。 defget_running_processes():# 获取当前运行的进程列表process_list=[]forprocinpsutil.process_iter(['pid','name']):# 遍历当前进程process_list.append(proc.info)# 将进程信息加...
Here is how to get the process list: >>> processes = WMI.InstancesOf('Win32_Process') >>> len(processes) 41 >>> [process.Properties_('Name').Value for process in processes] # get the process names [u'System Idle Process', u'System', u'SMSS.EXE', u'CSRSS.EXE', u'WINLOGON.EX...
Here is how to get the process list: >>> processes = WMI.InstancesOf('Win32_Process') >>> len(processes) 41 >>> [process.Properties_('Name').Value for process in processes] # get the process names [u'System Idle Process', u'System', u'SMSS.EXE', u'CSRSS.EXE', u'WINLOGON.EX...
首先是获取特定进程对象,可以使用Process.GetProcesses()方法来获取系统中运行的所有进程,或者使用Process.GetCurrentProcess()方法来获取当前程序所对应的进程对象。当有了进程对象后,可以通过进程对象名称来创建PerformanceCounter类型对象,通过设定PerformanceCounter构造函数的参数实现获取特定进程的CPU和内存使用情况。 具体实例...
class MyProcess(Process): #继承Process类 def __init__(self,name): super(MyProcess,self).__init__() self.name = name def run(self): print('测试%s多进程' % self.name) if __name__ == '__main__': process_list = [] for i in range(5): #开启5个子进程执行fun1函数 ...
(res.get())withPool(processes=5)asp:res=[p.apply_async(func2,args=(a,b,))fora,binzip(range(1,10),range(10,20))]print('非阻塞')print([i.get()foriinres])# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]# [-9, -9, -9...
response = requests.get(url) response.raise_for_status() # 如果响应状态不是200 ,这里会抛出HTTPError except requests.RequestException: return None data = fetch_data('http://example.com/data') and process_data(data) 这段代码中,只有当fetch_data成功获取数据(即没有抛出异常)时,才会执行process_da...
``` # Python script to manage system processes import psutil def get_running_processes(): return [p.info for p in psutil.process_iter(['pid', 'name', 'username'])] def kill_process_by_name(process_name): for p in psutil.process_iter(['pid', 'name', 'username']): if p.info[...
s.get_value())print(s.get_value())s.release()print(s.get_value())s.release()output：23348.数据共享共享数据类型可以直接通过进程模块来设置：数值型：m.Value() 数组性：m.Array() 字典型：m.dict() 列表型:m.list()也可以通过进程的Manager模块来实现：Manager().dict()Manager.list()...
首先,我将使用该 get_dummies 方法为分类变量创建虚拟列。 dataset = pd.get_dummies(df, columns = ['sex', 'cp','fbs','restecg','exang', 'slope','ca', 'thal'])from sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerstandardScaler = StandardScaler(...