最近在自学python基础,发现很多js中的内置函数python中都存在,甚至比js更加简洁,下面说一下reduce()在js和python中的用法,做个对比便于记忆。

reduce()简介:

  reduce的原意为减少,缩小;在js中为数组的内置方法,在python中则是一个内置方法,可以对可迭代数据集合做处理。

js中的reduce:

reduce为数组中的每一个元素依次执行callback函数,不包括数组中被删除或从未被赋值的元素

语法:

  因为js中reduce为数组的内置方法,所以使用时要与数组一起使用,即:

arr.reduce(function(accumulator,currentValue,index,array){
  ...
}, initialValue);

参数说明:

  第一个参数为回调函数function,包含四个参数:

    accumulator

    累计器累计回调的返回值; 它是上一次调用回调时返回的累积值,或initialValue(见于下方)。

    currentValue:

    数组中正在处理的元素。

    index:(可选)

    数组中正在处理的当前元素的索引。 如果提供了initialValue,则起始索引号为0,否则从索引1起始。

    array:(可选)

    调用reduce()的数组

  第二个参数为初始值:

  initialValue(可选)

 返回值:

  函数累计处理的结果

注意:回调函数第一次执行时,accumulator 和currentValue的取值有两种情况:

  1)如果调用reduce()时提供了initialValueaccumulator取值为initialValuecurrentValue取数组中的第一个值;

  2)如果没有提供 initialValue,那么accumulator取数组中的第一个值,currentValue取数组中的第二个值。

如果数组为空且没有提供initialValue,会抛出TypeError 。

如果数组仅有一个元素(无论位置如何)并且没有提供initialValue, 或者有提供initialValue但是数组为空,那么此唯一值将被返回并且callback不会被执行。

举例:

  

[0, 1, 2, 3, 4].reduce(function(accumulator, currentValue, currentIndex, array){
return accumulator + currentValue;
});

callback 被调用四次,每次调用的参数和返回值如下表:

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" alt="" />

reduce返回的值将是最后一次回调返回值(10)。

更详细的讲解请参考:

https://developer.mozilla.org/zh-CN/docs/Web/JavaScript/Reference/Global_Objects/Array/Reduce

python中的reduce():

描述:

  reduce() 函数会对参数序列中元素进行累积。

  函数将一个可迭代的数据集合(链表,元组等)中的所有数据进行下列操作:用传给 reduce 中的函数 function(有两个参数)先对集合中的第 1、2 个元素进行操作,得到的结果再与第三个数据用 function 函数运算,最后得到一个结果。

语法:

  python中的reduce可处理的数据不仅仅局限于列表,所有可迭代数据均在处理范围之内

reduce(function, iterable[, initializer])

参数:

  function:回调函数,有两个参数,与js的前两个参数相同

    accumulator

    累计器累计回调的返回值; 它是上一次调用回调时返回的累积值,或initialValue(见于下方)。

    currentValue:

    数组中正在处理的元素。

  iterable:

    可迭代对象:类比与js中的arr

  initializer:(可选)

    初始参数:类比于js中的第二个参数

举例:

  

>>>def add(x, y) :            # 两数相加
... return x + y
...
>>> reduce(add, [1,2,3,4,5]) # 计算列表和:1+2+3+4+5
15
>>> reduce(lambda x, y: x+y, [1,2,3,4,5]) # 使用 lambda 匿名函数
15
reduce(add, [1,2,3,4,5]) ==> add( add( add( add(1, 2), 3), 4), 5)

以上是js和python的reduce函数整理,可以看出其实很像,区别在于使用形式不同,不得不承认的是python使用起来更加简单并且可处理的数据类型更加多,

如果js想用reduce想用来处理其他可迭代对象可能要使用到call,apply之类的方法才行了

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