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=-5. 数据结构 Data Structures（已翻译，尚未校对）=
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本章深入讲述一些你已经学习过的东西，并且还加入了新的内容。
==-5.1.补充列表==
链表类型有很多方法，这里是链表类型的所有方法： The list data type has some more methods. Here are all of the methods of list objects:

list.'''append'''(''x'')
#把一个元素添加到链表的结尾，相当于 a[len(a):] = [x]。Add an item to the end of the list; equivalent to a[len(a):] = [x].

list.'''extend'''(''L'')
#通过添加指定链表的所有元素来扩充链表，相当于 a[len(a):] = L。 Extend the list by appending all the items in the given list; equivalent to a[len(a):] = L.

list.'''insert'''(''i, x'')
#在指定位置插入一个元素。第一个参数是准备插入到其前面的那个元素的索引，例如 a.insert(0, x) 会插入到整个链表之前，而 a.insert(len(a), x) 相当于 a.append(x) 。
Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x) inserts at the front of the list, and a.insert(len(a), x) is equivalent to a.append(x).

list.'''remove'''(''x'')
#删除链表中值为 x 的第一个元素。如果没有这样的元素，就会返回一个错误。
Remove the first item from the list whose value is x. It is an error if there is no such item.

list.'''pop'''([''i''])
#从链表的指定位置删除元素，并将其返回。如果没有指定索引，``a.pop()`` 返回最后一个元素。元素随即从链表中被删除。（方法中 i 两边的方括号表示这个参数是可选的，而不是要求你输入一对方括号，你会经常在Python 库参考手册中遇到这样的标记。）
Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in the list. (The square brackets around the i in the method signature denote that the parameter is optional, not that you should type square brackets at that position. You will see this notation frequently in the Python Library Reference.)

list.'''index'''(''x'')
#返回链表中第一个值为 x 的元素的索引。如果没有匹配的元素就会返回一个错误。
Return the index in the list of the first item whose value is x. It is an error if there is no such item.

list.'''count'''(''x'')
#返回 x 在链表中出现的次数。
Return the number of times x appears in the list.

list.'''sort'''()
#对链表中的元素进行“就地”排序。
Sort the items of the list, in place.

list.'''reverse'''()
#“就地”倒排链表中的元素。
Reverse the elements of the list, in place.

下面这个示例演示了链表的大部分方法：
An example that uses most of the list methods:

 >>> a = [66.25, 333, 333, 1, 1234.5]
 >>> print(a.count(333), a.count(66.25), a.count('x'))
 2 1 0
 >>> a.insert(2, -1)
 >>> a.append(333)
 >>> a
 [66.25, 333, -1, 333, 1, 1234.5, 333]
 >>> a.index(333)
 1
 >>> a.remove(333)
 >>> a
 [66.25, -1, 333, 1, 1234.5, 333]
 >>> a.reverse()
 >>> a
 [333, 1234.5, 1, 333, -1, 66.25]
 >>> a.sort()
 >>> a
 [-1, 1, 66.25, 333, 333, 1234.5]

===-5.1.1. 将列表当做堆栈使用 Using Lists as Stacks===
链表方法使得链表可以很方便的做为一个堆栈来使用，堆栈作为特定的数据结构，最先进入的元素最后一个被释放（后进先出）。用 append() 方法可以把一个元素添加到堆栈顶。用不指定索引的 pop() 方法可以把一个元素从堆栈顶释放出来。例如：
The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (“last-in, first-out”). To add an item to the top of the stack, use append(). To retrieve an item from the top of the stack, use pop() without an explicit index. For example:

 >>> stack = [3, 4, 5]
 >>> stack.append(6)
 >>> stack.append(7)
 >>> stack
 [3, 4, 5, 6, 7]
 >>> stack.pop()
 7
 >>> stack
 [3, 4, 5, 6]
 >>> stack.pop()
 6
 >>> stack.pop()
 5
 >>> stack
 [3, 4]

===-5.1.2. 将列表当作队列使用 Using Lists as Queues===

It is also possible to use a list as a queue, where the first element added is the first element retrieved (“first-in, first-out”); however, lists are not efficient for this purpose. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be shifted by one).

To implement a queue, use collections.deque which was designed to have fast appends and pops from both ends. For example:

 >>> from collections import deque
 >>> queue = deque(["Eric", "John", "Michael"])
 >>> queue.append("Terry")           # Terry arrives
 >>> queue.append("Graham")          # Graham arrives
 >>> queue.popleft()                 # The first to arrive now leaves
 'Eric'
 >>> queue.popleft()                 # The second to arrive now leaves
 'John'
 >>> queue                           # Remaining queue in order of arrival
 deque(['Michael', 'Terry', 'Graham'])

===-5.1.3. 列表推导式 List Comprehensions===
列表推导式提供了从序列创建列表的简单途径。通常应用程序将一些操作应用于某个序列的每个元素，用其获得的结果作为生成新列表的元素，或者根据确定的判定条件创建子序列。
List comprehensions provide a concise way to create lists from sequences. Common applications are to make lists where each element is the result of some operations applied to each member of the sequence, or to create a subsequence of those elements that satisfy a certain condition.

每个列表推导式都在 for 之后跟一个表达式，然后有零到多个 for 或 if 子句。返回结果是一个根据表达从其后的 for 和 if 上下文环境中生成出来的列表。如果希望表达式推导出一个元组，就必须使用括号。
A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. The result will be a list resulting from evaluating the expression in the context of the for and if clauses which follow it. If the expression would evaluate to a tuple, it must be parenthesized.

这里我们将列表中每个数值乘三，获得一个新的列表：
Here we take a list of numbers and return a list of three times each number:

 >>> vec = [2, 4, 6]
 >>> [3*x for x in vec]
 [6, 12, 18]

现在我们玩一点小花样：
Now we get a little fancier:

 >>> [[x, x**2] for x in vec]
 [[2, 4], [4, 16], [6, 36]]

这里我们对序列里每一个元素逐个调用某方法：
Here we apply a method call to each item in a sequence:

 >>> freshfruit = ['  banana', '  loganberry ', 'passion fruit  ']
 >>> [weapon.strip() for weapon in freshfruit]
 ['banana', 'loganberry', 'passion fruit']

我们可以用 `if` 子句作为过滤器：
Using the if clause we can filter the stream:

 >>> [3*x for x in vec if x > 3]
 [12, 18]
 >>> [3*x for x in vec if x < 2]
 []

元组经常可以不使用括号就创建出来，不过这里不行：
Tuples can often be created without their parentheses, but not here:

>>> [x, x**2 for x in vec]  # error - parens required for tuples
  File "<stdin>", line 1, in ?
    [x, x**2 for x in vec]
               ^
 SyntaxError: invalid syntax
 >>> [(x, x**2) for x in vec]
 [(2, 4), (4, 16), (6, 36)]

这里有一些关于循环和其它技巧的演示：
Here are some nested for loops and other fancy behavior:

 >>> vec1 = [2, 4, 6]
 >>> vec2 = [4, 3, -9]
 >>> [x*y for x in vec1 for y in vec2]
 [8, 6, -18, 16, 12, -36, 24, 18, -54]
 >>> [x+y for x in vec1 for y in vec2]
 [6, 5, -7, 8, 7, -5, 10, 9, -3]
 >>> [vec1[i]*vec2[i] for i in range(len(vec1))]
 [8, 12, -54]

链表推导式可以使用复杂表达式或嵌套函数：
List comprehensions can be applied to complex expressions and nested functions:

 >>> [str(round(355/113, i)) for i in range(1, 6)]
 ['3.1', '3.14', '3.142', '3.1416', '3.14159']

===-5.1.4. Nested List Comprehensions===

If you’ve got the stomach for it, list comprehensions can be nested. They are a powerful tool but – like all powerful tools – they need to be used carefully, if at all.

Consider the following example of a 3x3 matrix held as a list containing three lists, one list per row:

 >>> mat = [
 ...        [1, 2, 3],
 ...        [4, 5, 6],
 ...        [7, 8, 9],
 ...       ]

Now, if you wanted to swap rows and columns, you could use a list comprehension:

 >>> print([[row[i] for row in mat] for i in [0, 1, 2]])
 [[1, 4, 7], [2, 5, 8], [3, 6, 9]]

Special care has to be taken for the nested list comprehension:

To avoid apprehension when nesting list comprehensions, read from right to left.

A more verbose version of this snippet shows the flow explicitly:

 for i in [0, 1, 2]:
     for row in mat:
         print(row[i], end="")
     print()

In real world, you should prefer built-in functions to complex flow statements. The zip() function would do a great job for this use case:

 >>> list(zip(*mat))
 [(1, 4, 7), (2, 5, 8), (3, 6, 9)]

See Unpacking Argument Lists for details on the asterisk in this line.

==-5.2. del 语句 The del statement==
使用 del 语句可以从一个列表中依索引而不是值来删除一个元素。这与使用 pop() 返回一个值不同。可以用 del 语句从列表中删除一个切割，或清空整个列表（我们以前介绍的方法是给该切割赋一个空列表）。例如：
There is a way to remove an item from a list given its index instead of its value: the del statement. This differs from the pop() method which returns a value. The del statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example:

 >>> a = [-1, 1, 66.25, 333, 333, 1234.5]
 >>> del a[0]
 >>> a
 [1, 66.25, 333, 333, 1234.5]
 >>> del a[2:4]
 >>> a
 [1, 66.25, 1234.5]
 >>> del a[:]
 >>> a
 []

也可以用 del 删除实体变量：
del can also be used to delete entire variables:

 >>> del a

此后引用 a 命名就是一个错误（至少在另一个值赋给它之前）。我们会在后面找到 del 的其它用途。
Referencing the name a hereafter is an error (at least until another value is assigned to it). We’ll find other uses for del later.

==-5.3. 元组和序列 Tuples and Sequences==
我们知道列表和字符串有很多通用的属性，例如索引和切割操作。它们是序列类型中的两种（参见 Sequence Types — str, bytes, bytearray, list, tuple, range ）。困为 Python 是一个在不断进化的语言，也可能会加入其它的序列类型。这里我们介绍另一个标准序列类型： tuple （元组）
We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types (see Sequence Types — str, bytes, bytearray, list, tuple, range). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple.

元组由若干逗号分隔的值组成，例如：
A tuple consists of a number of values separated by commas, for instance:

 >>> t = 12345, 54321, 'hello!'
 >>> t[0]
 12345
 >>> t
 (12345, 54321, 'hello!')
 >>> # Tuples may be nested:
 ... u = t, (1, 2, 3, 4, 5)
 >>> u
 ((12345, 54321, 'hello!'), (1, 2, 3, 4, 5))

如你所见，元组在输出时总是有括号的，以便于正确表达嵌套结构。在输入时可能有或没有括号， 不过括号通常是必须的（如果元组是更大的表达式的一部分）。
As you see, on output tuples are always enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression).

元组有很多用途。例如(x, y)坐标点，数据库中的员工记录等等。元组就像字符串，不可改变：不能给元组的一个独立的元素赋值（尽管你可以通过联接和切片来模仿）。也可以通过包含可变对象来创建元组，例如链表。
Tuples have many uses. For example: (x, y) coordinate pairs, employee records from a database, etc. Tuples, like strings, are immutable: it is not possible to assign to the individual items of a tuple (you can simulate much of the same effect with slicing and concatenation, though). It is also possible to create tuples which contain mutable objects, such as lists.

一个特殊的问题是构造包含零个或一个元素的元组：为了适应这种情况，语法上有一些额外的改变。一对空的括号可以创建空元组；要创建一个单元素元组可以在值后面跟一个逗号（在括号中放入一个单值是不够的）。丑陋，但是有效。例如：
A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:

 >>> empty = ()
 >>> singleton = 'hello',    # <-- note trailing comma
 >>> len(empty)
 0
 >>> len(singleton)
 1
 >>> singleton
 ('hello',)

语句 t = 12345, 54321, ‘hello!’ 是元组封装（sequence packing）的一个例子：值 12345， 54321 和 ‘hello!’ 被封装进元组。其逆操作可能是这样：
The statement t = 12345, 54321, 'hello!' is an example of tuple packing: the values 12345, 54321 and 'hello!' are packed together in a tuple. The reverse operation is also possible:

 >>> x, y, z = t

称其为序列拆封非常合适。序列拆封要求左侧的变量数目与序列的元素个数相同。要注意的是可变参数（multiple assignment ）其实只是元组封装和序列拆封的一个结合！
This is called, appropriately enough, sequence unpacking and works for any sequence on the right-hand side. Sequence unpacking requires that there are as many variables on the left side of the equals sign as there are elements in the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.

==-5.4. 集合 Sets==
Python 还包含了一个数据类型set（集合）。集合是一个无序不重复元素的集。基本功能包括关系测试和消除重复元素。集合对象还支持 union（联合），intersection（交），difference（差）和 sysmmetric difference（对称差集）等数学运算。
Python also includes a data type for sets. A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.

大括号可以用于创建集合。注意：如果要创建一个空集合，你必须用 set() 而不是 {} ；后者创建一个空的字典，下一节我们会介绍这个数据结构。
Curly braces or the set() function can be used to create sets. Note: To create an empty set you have to use set(), not {}; the latter creates an empty dictionary, a data structure that we discuss in the next section.

以下是一个简单的演示：
Here is a brief demonstration:

 >>> basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
 >>> print(basket)                      # show that duplicates have been removed
 {'orange', 'banana', 'pear', 'apple'}
 >>> 'orange' in basket                 # fast membership testing
 True
 >>> 'crabgrass' in basket
 False

 >>> # Demonstrate set operations on unique letters from two words
 ...
 >>> a = set('abracadabra')
 >>> b = set('alacazam')
 >>> a                                  # unique letters in a
 {'a', 'r', 'b', 'c', 'd'}
 >>> a - b                              # letters in a but not in b
 {'r', 'd', 'b'}
 >>> a | b                              # letters in either a or b
 {'a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'}
 >>> a & b                              # letters in both a and b
 {'a', 'c'}
 >>> a ^ b                              # letters in a or b but not both
 {'r', 'd', 'b', 'm', 'z', 'l'}

Like for lists, there is a set comprehension syntax:

 >>> a = {x for x in 'abracadabra' if x not in 'abc'}
 >>> a
 {'r', 'd'}

==-5.5. 字典 Dictionaries==
另一个非常有用的 Python 内建数据类型是*字典*（参见 :ref:`typesmapping`）。字典在某些语言中可能称为“关联存储”（``associative memories’‘）或“关联数组”（``associative arrays’‘）。序列是以连续的整数为索引，与此不同的是，字典以*关键字*为索引，关键字可以是任意不可变类型，通常用字符串或数值。如果元组中只包含字符串、数字和元组，它可以做为关键字，如果它直接或间接的包含了可变对象，就不能当做关键字。不能用链表做关键字，因为链表可以用索引、切割或者 append() 和 extend() 等方法改变。
Another useful data type built into Python is the dictionary (see Mapping Types — dict). Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend().

理解字典的最佳方式是把它看做无序的*键：值*对集合，关键字必须是互不相同的（在同一个字典之内）。一对大括号创建一个空的字典：``{}``。初始化链表时，在大括号内放置一组逗号分隔的关键字：值对，这也是字典输出的方式。
It is best to think of a dictionary as an unordered set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: {}. Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.

字典的主要操作是依据关键字来存储和析取值。也可以用 del 来删除键：值对。如果你用一个已经存在的关键字存储值，以前为该关键字分配的值就会被遗忘。试图从一个不存在的关键字中读取值会导致错误。
The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with del. If you store using a key that is already in use, the old value associated with that key is forgotten. It is an error to extract a value using a non-existent key.

字典的 keys() 方法返回由所有关键字组成的链表，该链表的顺序不定（如果你需要它有序，只能调用关键字链表的 sort() 方法）。使用 in 关键字可以检查字典中是否存在某一关键字。
Performing list(d.keys()) on a dictionary returns a list of all the keys used in the dictionary, in arbitrary order (if you want it sorted, just use sorted(d.keys()) instead). [1] To check whether a single key is in the dictionary, use the in keyword.

这是一个字典运用的简单例子：
Here is a small example using a dictionary:

 >>> tel = {'jack': 4098, 'sape': 4139}
 >>> tel['guido'] = 4127
 >>> tel
 {'sape': 4139, 'guido': 4127, 'jack': 4098}
 >>> tel['jack']
 4098
 >>> del tel['sape']
 >>> tel['irv'] = 4127
 >>> tel
 {'guido': 4127, 'irv': 4127, 'jack': 4098}
 >>> list(tel.keys())
 ['irv', 'guido', 'jack']
 >>> sorted(tel.keys())
 ['guido', 'irv', 'jack']
 >>> 'guido' in tel
 True
 >>> 'jack' not in tel
 False

构造函数 dict() 直接从键值对元组列表中构建字典。如果有固定的模式，列表推导式指定特定的键值对：
The dict() constructor builds dictionaries directly from sequences of key-value pairs:

 >>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])
 {'sape': 4139, 'jack': 4098, 'guido': 4127}

In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:

 >>> {x: x**2 for x in (2, 4, 6)}
 {2: 4, 4: 16, 6: 36}

如果关键字只是简单的字符串，使用关键字参数指定键值对有时候更方便：
When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:

 >>> dict(sape=4139, guido=4127, jack=4098)
 {'sape': 4139, 'jack': 4098, 'guido': 4127}

==-5.6.遍历技巧 Looping Techniques==
在字典中遍历时，关键字和对应的值可以使用 items() 方法同时解读出来：
When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the items() method.

 >>> knights = {'gallahad': 'the pure', 'robin': 'the brave'}
 >>> for k, v in knights.items():
 ...     print(k, v)
 ...
 gallahad the pure
 robin the brave

在序列中遍历时，索引位置和对应值可以使用 enumerate() 函数同时得到：
When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the enumerate() function.

 >>> for i, v in enumerate(['tic', 'tac', 'toe']):
 ...     print(i, v)
 ...
 0 tic
 1 tac
 2 toe

同时遍历两个或更多的序列，可以使用 zip() 组合：
To loop over two or more sequences at the same time, the entries can be paired with the zip() function.

 >>> questions = ['name', 'quest', 'favorite color']
 >>> answers = ['lancelot', 'the holy grail', 'blue']
 >>> for q, a in zip(questions, answers):
 ...     print('What is your {0}?  It is {1}.'.format(q, a))
 ...
 What is your name?  It is lancelot.
 What is your quest?  It is the holy grail.
 What is your favorite color?  It is blue.

要反向遍历一个序列，首先指定这个序列，然后调用 reversesd() 函数：
To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the reversed() function.

 >>> for i in reversed(range(1, 10, 2)):
 ...     print(i)
 ...
 9
 7
 5
 3
 1

要按顺序遍历一个序列，使用 sorted() 函数返回一个已排序的序列，将原有的放一边：
To loop over a sequence in sorted order, use the sorted() function which returns a new sorted list while leaving the source unaltered.

 >>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
 >>> for f in sorted(set(basket)):
 ...     print(f)
 ...
 apple
 banana
 orange
 pear

==-5.7. 深入条件控制 More on Conditions==
用于 while 和 if 语句的条件包括了比较之外的操作符。
The conditions used in while and if statements can contain any operators, not just comparisons.

比较操作符 in 和 not in 审核值是否在一个区间之内。操作符 is 和 is not 和比较两个对象是否相同；这只和诸如链表这样的可变对象有关。所有的比较操作符具有相同的优先级，低于所有的数值操作。
The comparison operators in and not in check whether a value occurs (does not occur) in a sequence. The operators is and is not compare whether two objects are really the same object; this only matters for mutable objects like lists. All comparison operators have the same priority, which is lower than that of all numerical operators.

比较操作符可以串联。例如： a < b == c 测试是否 a 小于 b 并且 b 等于 ``c``。
Comparisons can be chained. For example, a < b == c tests whether a is less than b and moreover b equals c.

比较操作（或其它任何逻辑表达式）可以通过逻辑操作符 and 和 or 组合，比较的结果可以用 not 来取反义。这些操作符的优先级又低于比较操作符，在它们之中，``not`` 具有最高的优先级， or 优先级最低，所以 A and not B or C 等于 (A and (not B)) or C 。当然，表达式可以用期望的方式表示。
Comparisons may be combined using the Boolean operators and and or, and the outcome of a comparison (or of any other Boolean expression) may be negated with not. These have lower priorities than comparison operators; between them, not has the highest priority and or the lowest, so that A and not B or C is equivalent to (A and (not B)) or C. As always, parentheses can be used to express the desired composition.

逻辑操作符 and 和 or 也称作*短路操作符*：它们的参数从左向右解析，一旦结果可以确定就停止。例如，如果 A 和 C 为真而 B 为假， A and B and C 不会解析 ``C``。作用于一个普通的非逻辑值时，短路操作符的返回值通常是最后一个变量。
The Boolean operators and and or are so-called short-circuit operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C. When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument.

可以把比较或其它逻辑表达式的返回值赋给一个变量，例如：
It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,

 >>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance'
 >>> non_null = string1 or string2 or string3
 >>> non_null
 'Trondheim'

需要注意的是 Python 与 C 不同，在表达式内部不能赋值。 C 程序员经常对此抱怨，不过它避免了一类在 C 程序中司空见惯的错误：想要在解析式中使 == 时误用了 = 操作符。
Note that in Python, unlike C, assignment cannot occur inside expressions. C programmers may grumble about this, but it avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.

==-5.8. Comparing Sequences and Other Types==
序列对象可以与相同类型的其它对象比较。比较操作按*字典*序进行：首先比较前两个元素，如果不同，就决定了比较的结果；如果相同，就比较后两个元素，依此类推，直到所有序列都完成比较。如果两个元素本身就是同样类型的序列，就递归字典序比较。如果两个序列的所有子项都相等，就认为序列相等。如果一个序列是另一个序列的初始子序列，较短的一个序列就小于另一个。字符串的字典序按照单字符的 ASCII 顺序。下面是同类型序列之间比较的一些例子:
Sequence objects may be compared to other objects with the same sequence type. The comparison uses lexicographical ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the Unicode codepoint number to order individual characters. Some examples of comparisons between sequences of the same type:

 (1, 2, 3)              < (1, 2, 4)
 [1, 2, 3]              < [1, 2, 4]
 'ABC' < 'C' < 'Pascal' < 'Python'
 (1, 2, 3, 4)           < (1, 2, 4)
 (1, 2)                 < (1, 2, -1)
 (1, 2, 3)             == (1.0, 2.0, 3.0)
 (1, 2, ('aa', 'ab'))   < (1, 2, ('abc', 'a'), 4)

需要注意的是用 <or> 比较不同类型的对象是合法的。参与比较的对象要提供适当的比较方法。例如，不同数值类型比较时会统一它们的值大小，所以0等于0.0，等等。另一方面，如果没有确定的排序方法，解释器会抛出 TypeError 异常。
Note that comparing objects of different types with < or > is legal provided that the objects have appropriate comparison methods. For example, mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc. Otherwise, rather than providing an arbitrary ordering, the interpreter will raise a TypeError exception.

'''Footnotes'''
#[1]	Calling d.keys() will return a dictionary view object. It supports operations like membership test and iteration, but its contents are not independent of the original dictionary – it is only a view.

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