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  • Python Anonymous Functions and lambda, apply, filter,map,reduce

    Core Python Programming Language : Page 477

    Anonymous Functions and lambda

    Python allows one to create anonymous functions using the lambda keyword. They are "anonymous"
    because they are not declared in the standard manner, i.e., using the def statement. (Unless assigned
    to a local variable, such objects do not create a name in any namespace either.) However, as functions,
    they may also have arguments. An entire lambda "statement" represents an expression, and the body of
    that expression must also be given on the same line as the declaration. We now present the syntax for
    anonymous functions:
    lambda [arg1[, arg2, ... argN]]: expression
    Arguments are optional, and if used, are usually part of the expression as well.


    Core Note: lambda expression returns callable function object
    Calling lambda with an appropriate expression yields a function object
    that can be used like any other function. They can be passed to other
    functions, aliased with additional references, be members of container
    objects, and as callable objects, be invoked (with any arguments, if
    necessary). When called, these objects will yield a result equivalent to
    the same expression if given the same arguments. They are
    indistinguishable from functions that return the evaluation of an
    equivalent expression.

    One final word on lambda: Although it appears that lambda is a one-line version of a function, it is not
    equivalent to an "inline" statement in C++, whose purpose is bypassing function stack allocation during
    invocation for performance reasons. A lambda expression works just like a function, creating a frame
    object when called.

    11.7.2. Built-in Functions: apply(), filter(), map(), reduce()


    In this section, we will look at the apply(), filter(), map(), and reduce() built-in functions as well as
    give some examples to show how they can be used. These functions provide the functional programming
    features found in Python. A summary of these functions is given in Table 11.2. All take a function object
    to somehow invoke.

    example:

    #lambda默认参数与或变参数
    
    >>> lfun=lambda x,y=10,*z: x+y+sum(z)
    >>> lfun(1)
    11
    >>> lfun(1,2)
    3
    >>> lfun(1,2,3)
    6
    >>> lfun(1,2,3,4,5)
    15
    

      

    #apply:应用方法
    >>> apply(lambda x:x*2, [1])
    2
    >>> apply(lambda x,y,z:x+y+z,[1,2,3])
    6
    
    #filter:过滤序列
    >>> l
    [24, 32, 41, 12, 22, 45, 50, 30, 44]
    >>> filter(lambda x:x%2 , l)
    [41, 45]
    
    #list单个序列做参数
    >>> list1 = [49, 65, 82, 24, 44, 90, 100, 61, 88]
    >>> map(lambda x: x/2,list1)
    [24, 32, 41, 12, 22, 45, 50, 30, 44]
    
    #map多个序列做参数
    >>> map(lambda x,y,z:x+y+z,[1,2,3],[10,20,30],[100,200,300])
    [111, 222, 333]
    >>> zip([1,2,3],[10,20,30],[100,200,300])
    [(1, 10, 100), (2, 20, 200), (3, 30, 300)]
    >>> map(None,[1,2,3],[10,20,30],[100,200,300])
    [(1, 10, 100), (2, 20, 200), (3, 30, 300)]
    
    #reduce使用
    >>> reduce(lambda x,y:x + y, [1,2,3,4,5])
    15
    >>> reduce(lambda x,y:x + y, '12345')
    '12345'
    >>> reduce(lambda x,y:x + y, ('1','2','3','4','5'))
    '12345'
    >>> reduce(lambda x,y:x + y, (1,2,3,4,5))
    15
    

      

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  • 原文地址:https://www.cnblogs.com/wucg/p/2338121.html
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