1. Decorators(装饰器) 2.Acquiring Function Properties(获取功能属性) 3. Other Callables() 4. Methods and Functions(方法和功能) 5. Acquiring Function Properties for Decorators(获取装饰器的函数属性) 6. Comparison() 7. Caching(缓存) 8. Reducing a Data Set() 9. Generic Functions(通用函数) Deco...
Ageneric functionis composed of multiple functions implementing the same operation for different types. Which implementation should be used during a call is determined by the dispatch algorithm. When the implementation is chosen based on the type of a single argument, this is known assingle dispatch...
def _(data): """Handle list objects.""" print(f"Processing a list of length: {len(data)}") # Testing thegenericfunction process(42) # Outputs: Processing an integer: 42 process("hello") # Outputs: Processing a string: hello process([1, 2, 3]) # Outputs: Processing a list of l...
Python 3.4新增的functools.singledispatch装饰器可以把整体方案拆分成多个模块,甚至可以为你无法修改的类提供专门的函数。使用@singledispatch装饰的普通函数会变成泛函数(generic function):根据第一个参数的类型,以不同方式执行相同操作的一组函数。 只要可能,注册的专门函数应该处理抽象基类(如numbers.Integral和abc.Mutable...
# Using the generic function with different types display_info(39) display_info(3.19) display_info("Hello World!") display_info([2, 4, 6]) 输出: 复制 Received an integer: 39 Received a float: 3.19 Received a string: Hello World!
Yeah, contextmanager seems to bring out the worst in mypy, because it's a decorator and wraps a generator. :-( Note this comment in contextlib.pyi: # TODO this doesn't capture the relationship that the returned function's args are the same as func's. def contextmanager(func: Callable...
generic function -- 泛型函数为不同的类型实现相同操作的多个函数所组成的函数。在调用时会由调度算法来确定应该使用哪个实现。 另请参见 single dispatch 术语表条目、functools.singledispatch() 装饰器以及 PEP 443。 GIL参见 global interpreter lock。
Ageneric functionis composed of multiple functions implementing the same operation for different types. Which implementation should be used during a call is determined by the dispatch algorithm. When the implementation is chosen based on the type of a single argument, this is known assingle dispatch...
#Generic function for making a classification model and def classification_model(model, data, predictors, outcome): #Fit the model: model.fit(data[predictors],data[outcome]) on training set: predictions = model.predict(data[predictors])
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