Python 入门（九）迭代
什么是迭代
在Python中，如果给定一个list或tuple，我们可以通过for循环来遍历这个list或tuple，这种遍历我们成为迭代（Iteration）。
在Python中，迭代是通过 for ... in 来完成的，而很多语言比如C或者Java，迭代list是通过下标完成的，比如Java代码：
for (i=0; i<list.length; i++) {
n = list[i];
}
可以看出，Python的for循环抽象程度要高于Java的for循环。
因为 Python 的 for循环不仅可以用在list或tuple上，还可以作用在其他任何可迭代对象上。
因此，迭代操作就是对于一个集合，无论该集合是有序还是无序，我们用 for 循环总是可以依次取出集合的每一个元素。
注意: 集合是指包含一组元素的数据结构，我们已经介绍的包括：
1. 有序集合：list，tuple，str和unicode；
2. 无序集合：set
3. 无序集合并且具有 key-value 对：dict
而迭代是一个动词，它指的是一种操作，在Python中，就是 for 循环。
迭代与按下标访问数组最大的不同是，后者是一种具体的迭代实现方式，而前者只关心迭代结果，根本不关心迭代内部是如何实现的。
任务
请用for循环迭代数列 1-100 并打印出7的倍数。
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索引迭代
Python中，迭代永远是取出元素本身，而非元素的索引。
对于有序集合，元素确实是有索引的。有的时候，我们确实想在 for 循环中拿到索引，怎么办？
方法是使用 enumerate() 函数：
>>> L = ['Adam', 'Lisa', 'Bart', 'Paul']
>>> for index, name in enumerate(L):
... print index, '-', name
...
0 - Adam
1 - Lisa
2 - Bart
3 - Paul
使用 enumerate() 函数，我们可以在for循环中同时绑定索引index和元素name。但是，这不是 enumerate() 的特殊语法。实际上，enumerate() 函数把：
['Adam', 'Lisa', 'Bart', 'Paul']
变成了类似：
[(0, 'Adam'), (1, 'Lisa'), (2, 'Bart'), (3, 'Paul')]
因此，迭代的每一个元素实际上是一个tuple：
for t in enumerate(L):
index = t[0]
name = t[1]
print index, '-', name
如果我们知道每个tuple元素都包含两个元素，for循环又可以进一步简写为：
for index, name in enumerate(L):
print index, '-', name
这样不但代码更简单，而且还少了两条赋值语句。
可见，索引迭代也不是真的按索引访问，而是由 enumerate() 函数自动把每个元素变成 (index, element) 这样的tuple，再迭代，就同时获得了索引和元素本身。
任务
zip()函数可以把两个 list 变成一个 list：
>>> zip([10, 20, 30], ['A', 'B', 'C'])
[(10, 'A'), (20, 'B'), (30, 'C')]
在迭代 ['Adam', 'Lisa', 'Bart', 'Paul'] 时，如果我们想打印出名次 - 名字（名次从1开始)，请考虑如何在迭代中打印出来。
提示：考虑使用zip()函数和range()函数
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Python的zip函数
zip函数接受任意多个（包括0个和1个）序列作为参数，返回一个tuple列表。具体意思不好用文字来表述，直接看示例：
1.示例1：
x = [1, 2, 3] y = [4, 5, 6] z = [7, 8, 9] xyz = zip(x, y, z) print xyz
运行的结果是：
[(1, 4, 7), (2, 5, 8), (3, 6, 9)]
从这个结果可以看出zip函数的基本运作方式。
2.示例2：
x = [1, 2, 3]
y = [4, 5, 6, 7]
xy = zip(x, y)
print xy
运行的结果是：
[(1, 4), (2, 5), (3, 6)]
从这个结果可以看出zip函数的长度处理方式。
3.示例3：
x = [1, 2, 3]
x = zip(x)
print x
运行的结果是：
[(1,), (2,), (3,)]
从这个结果可以看出zip函数在只有一个参数时运作的方式。
4.示例4：
x = zip()
print x
运行的结果是：
[]
从这个结果可以看出zip函数在没有参数时运作的方式。
5.示例5：
x = [1, 2, 3] y = [4, 5, 6] z = [7, 8, 9] xyz = zip(x, y, z) u = zip(*xyz) print u
运行的结果是：
[(1, 2, 3), (4, 5, 6), (7, 8, 9)]
一般认为这是一个unzip的过程，它的运行机制是这样的：
在运行zip(*xyz)之前，xyz的值是：[(1, 4, 7), (2, 5, 8), (3, 6, 9)]
那么，zip(*xyz) 等价于 zip((1, 4, 7), (2, 5, 8), (3, 6, 9))
所以，运行结果是：[(1, 2, 3), (4, 5, 6), (7, 8, 9)]
注：在函数调用中使用*list/tuple的方式表示将list/tuple分开，作为位置参数传递给对应函数（前提是对应函数支持不定个数的位置参数）
6.示例6：
x = [1, 2, 3]
r = zip(* [x] * 3)
print r
运行的结果是：
[(1, 1, 1), (2, 2, 2), (3, 3, 3)]
它的运行机制是这样的：
[x]生成一个列表的列表，它只有一个元素x
[x] * 3生成一个列表的列表，它有3个元素，[x, x, x]
zip(* [x] * 3)的意思就明确了，zip(x, x, x)
迭代dict的value
我们已经了解了dict对象本身就是可迭代对象，用 for 循环直接迭代 dict，可以每次拿到dict的一个key。
如果我们希望迭代 dict 对象的value，应该怎么做？
dict 对象有一个 values() 方法，这个方法把dict转换成一个包含所有value的list，这样，我们迭代的就是 dict的每一个 value：
d = { 'Adam': 95, 'Lisa': 85, 'Bart': 59 }
print d.values()
# [85, 95, 59]
for v in d.values():
print v
# 85
# 95
# 59
如果仔细阅读Python的文档，还可以发现，dict除了values()方法外，还有一个 itervalues() 方法，用 itervalues() 方法替代 values() 方法，迭代效果完全一样：
d = { 'Adam': 95, 'Lisa': 85, 'Bart': 59 }
print d.itervalues()
# <dictionary-valueiterator object at 0x106adbb50>
for v in d.itervalues():
print v
# 85
# 95
# 59
那这两个方法有何不同之处呢？
1. values() 方法实际上把一个 dict 转换成了包含 value 的list。
2. 但是 itervalues() 方法不会转换，它会在迭代过程中依次从 dict 中取出 value，所以 itervalues() 方法比 values() 方法节省了生成 list 所需的内存。
3. 打印 itervalues() 发现它返回一个 <dictionary-valueiterator> 对象，这说明在Python中，for 循环可作用的迭代对象远不止 list，tuple，str，unicode，dict等，任何可迭代对象都可以作用于for循环，而内部如何迭代我们通常并不用关心。
如果一个对象说自己可迭代，那我们就直接用 for 循环去迭代它，可见，迭代是一种抽象的数据操作，它不对迭代对象内部的数据有任何要求。
任务
给定一个dict：
d = { 'Adam': 95, 'Lisa': 85, 'Bart': 59, 'Paul': 74 }
请计算所有同学的平均分。
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" alt="" />
迭代dict的key和value
我们了解了如何迭代 dict 的key和value，那么，在一个 for 循环中，能否同时迭代 key和value？答案是肯定的。
首先，我们看看 dict 对象的 items() 方法返回的值：
>>> d = { 'Adam': 95, 'Lisa': 85, 'Bart': 59 }
>>> print d.items()
[('Lisa', 85), ('Adam', 95), ('Bart', 59)]
可以看到，items() 方法把dict对象转换成了包含tuple的list，我们对这个list进行迭代，可以同时获得key和value：
>>> for key, value in d.items():
... print key, ':', value
...
Lisa : 85
Adam : 95
Bart : 59
和 values() 有一个 itervalues() 类似， items() 也有一个对应的 iteritems()，iteritems() 不把dict转换成list，而是在迭代过程中不断给出 tuple，所以， iteritems() 不占用额外的内存。
任务
请根据dict：
d = { 'Adam': 95, 'Lisa': 85, 'Bart': 59, 'Paul': 74 }
打印出 name : score，最后再打印出平均分 average : score。
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" alt="" />
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