def functionname( parameters ): """comments""" function_suite return [expression] 1. 2. 3. 4. 实例: def func(parameter): """打印传入的字符到显示设备上""" print(parameter) return parameter 1. 2. 3. 4. 二:函数调用 定义一个函数只给了函数一个名称,指定了函数里包含的参数,和代码块结构。
51CTO博客已为您找到关于python中def和function的相关内容,包含IT学习相关文档代码介绍、相关教程视频课程,以及python中def和function问答内容。更多python中def和function相关解答可以来51CTO博客参与分享和学习,帮助广大IT技术人实现成长和进步。
logging.basicConfig(level=logging.INFO)deflog_message(message):logging.info(message)deflog_function_call(func):defwrapper(*args,**kwargs):log_message(f"Calling {func.__name__} with args={args} kwargs={kwargs}")result=func(*args,**kwargs)log_message(f"{func.__name__} returned {result...
b= int(input("请输入第二个数:"))print("你输入的两个数的和是:",myadd(a,b))#3.写一个函数,input_number#def input_number():#...# 此处自己实现,此函数返回列表#此函数用来获取用户循环输入往返整数,当用户输入负数时结束输入#将用户输入的数字以列表的形式返回,再用内建函数max,min,sum#求出用户...
def apply_complex_function(x): return complex_function(x['col1']) df['col1'] = df.apply(apply_complex_function, axis=1) There is a lot going on in this solution that needs to be explained. The apply() function works on pd.Series and pd.DataFrame. But you cannot use df['col1...
defminimize(prediction_function,which_args_to_optimize,error_function,data): # 1: guess initial parameters # 2: apply prediction function with current parameters to data to compute predictions # 3: use error function to compute error between predictions and data ...
def heart_function(t, shrink_ratio: float = IMAGE_ENLARGE): """ “爱心函数生成器” :param shrink_ratio: 放大比例 :param t: 参数 :return: 坐标 """ # 基础函数 x = 16 * (sin(t) ** 3) y = -(13 * cos(t) - 5 * cos(2 * t) - 2 * cos(3 * t) - cos(4 * t)) ...
defhello_world():print("Hello, World!") 现在,我们想要在每次调用这个函数时,都能自动记录一条日志信息。传统的方法是直接修改函数的内部实现,但这会破坏函数的原始结构。而使用装饰器,我们可以这样做: deflog_decorator(func):defwrapper():print("Log: Function is called.") ...
unstack # move multi-level rows back to column lambda function # take in two and return 1 def IVs(price_series, isCall, S, K_series): ttm = price_series.name # rt = r(ttm) # dt = d(ttm) print(price_series) result = pd.DataFrame({'price': price_series, 'strike':K_series}...
def forward(self, x): r = torch.sqrt(x[:, 0:1]**2 + x[:, 1:2]**2) value = torch.where(r <= 1, torch.sqrt(1.-r**2), -1) return value This causes a problem with NaNs when it comes to taking gradients. To circumvent this, one can use: def ...