Subsections

# 5. 数据结构 Data Structures

This chapter describes some things you've learned about already in more detail, and adds some new things as well.

# 5.1 深入链表 More on Lists

The list data type has some more methods. Here are all of the methods of list objects:

 append( x)
Add an item to the end of the list; equivalent to `a[len(a):] = [x]`.

 extend( L)
Extend the list by appending all the items in the given list; equivalent to `a[len(a):] = L`.

 insert( i, 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)`.

 remove( x)
Remove the first item from the list whose value is x. It is an error if there is no such item.

 pop( [i])
Remove the item at the given position in the list, and return it. If no index is specified, `a.pop()` returns the last item in the list. The item is also removed from 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.)

 index( x)
Return the index in the list of the first item whose value is x. It is an error if there is no such item.

 count( x)
Return the number of times x appears in the list.

 sort( )
Sort the items of the list, in place.

 reverse( )
Reverse the elements of the list, in place.

An example that uses most of the list methods:

```>>> a = [66.6, 333, 333, 1, 1234.5]
>>> print a.count(333), a.count(66.6), a.count('x')
2 1 0
>>> a.insert(2, -1)
>>> a.append(333)
>>> a
[66.6, 333, -1, 333, 1, 1234.5, 333]
>>> a.index(333)
1
>>> a.remove(333)
>>> a
[66.6, -1, 333, 1, 1234.5, 333]
>>> a.reverse()
>>> a
[333, 1234.5, 1, 333, -1, 66.6]
>>> a.sort()
>>> a
[-1, 1, 66.6, 333, 333, 1234.5]
```

## 5.1.1 把链表当作堆栈使用 Using Lists as Stacks

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 把链表当作队列使用 Using Lists as Queues

You can also use a list conveniently as a queue, where the first element added is the first element retrieved (``first-in, first-out''). To add an item to the back of the queue, use append(). To retrieve an item from the front of the queue, use pop() with `0` as the index. For example:

```>>> queue = ["Eric", "John", "Michael"]
>>> queue.append("Terry")           # Terry arrives
>>> queue.append("Graham")          # Graham arrives
>>> queue.pop(0)
'Eric'
>>> queue.pop(0)
'John'
>>> queue
['Michael', 'Terry', 'Graham']
```

## 5.1.3 函数化编程工具 Functional Programming Tools

There are three built-in functions that are very useful when used with lists: filter(), map(), and reduce().

"filter(function, sequence)" returns a sequence (of the same type, if possible) consisting of those items from the sequence for which `function(item)` is true. For example, to compute some primes:

"filter(function, sequence)"返回一个序列（sequence），包括了给定序列中所有调用`function(item)`后返回值为true的元素。（如果可能的话，会返回相同的类型）。例如，以下程序可以计算部分素数：

```>>> def f(x): return x % 2 != 0 and x % 3 != 0
...
>>> filter(f, range(2, 25))
[5, 7, 11, 13, 17, 19, 23]
```

"map(function, sequence)" calls `function(item)` for each of the sequence's items and returns a list of the return values. For example, to compute some cubes:

"map(function, sequence)" 为每一个元素依次调用`function(item)`并将返回值组成一个链表返回。例如，以下程序计算立方：

```>>> def cube(x): return x*x*x
...
>>> map(cube, range(1, 11))
[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]
```

More than one sequence may be passed; the function must then have as many arguments as there are sequences and is called with the corresponding item from each sequence (or `None` if some sequence is shorter than another). For example:

```>>> seq = range(8)
>>> def add(x, y): return x+y
...
[0, 2, 4, 6, 8, 10, 12, 14]
```

"reduce(func, sequence)" returns a single value constructed by calling the binary function func on the first two items of the sequence, then on the result and the next item, and so on. For example, to compute the sum of the numbers 1 through 10:

"reduce(func, sequence)" 返回一个单值，它是这样构造的：首先以序列的前两个元素调用函数，再以返回值和第三个参数调用，依次执行下去。例如，以下程序计算1到10的整数之和：

```>>> def add(x,y): return x+y
...
55
```

If there's only one item in the sequence, its value is returned; if the sequence is empty, an exception is raised.

A third argument can be passed to indicate the starting value. In this case the starting value is returned for an empty sequence, and the function is first applied to the starting value and the first sequence item, then to the result and the next item, and so on. For example,

```>>> def sum(seq):
...
>>> sum(range(1, 11))
55
>>> sum([])
0
```

Don't use this example's definition of sum(): since summing numbers is such a common need, a built-in function `sum(sequence)` is already provided, and works exactly like this. New in version 2.3.

## 5.1.4 链表推导式 List Comprehensions

List comprehensions provide a concise way to create lists without resorting to use of map(), filter() and/or lambda. The resulting list definition tends often to be clearer than lists built using those constructs. Each list comprehension consists of 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.

```>>> freshfruit = ['  banana', '  loganberry ', 'passion fruit  ']
>>> [weapon.strip() for weapon in freshfruit]
['banana', 'loganberry', 'passion fruit']
>>> vec = [2, 4, 6]
>>> [3*x for x in vec]
[6, 12, 18]
>>> [3*x for x in vec if x > 3]
[12, 18]
>>> [3*x for x in vec if x < 2]
[]
>>> [[x,x**2] for x in vec]
[[2, 4], [4, 16], [6, 36]]
>>> [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)]
>>> 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]
```

List comprehensions are much more flexible than map() and can be applied to functions with more than one argument and to nested functions:

```>>> [str(round(355/113.0, i)) for i in range(1,6)]
['3.1', '3.14', '3.142', '3.1416', '3.14159']
```

# 5.2 del 语句

There is a way to remove an item from a list given its index instead of its value: the del statement. This can also be used to remove slices from a list (which we did earlier by assignment of an empty list to the slice). For example:

```>>> a = [-1, 1, 66.6, 333, 333, 1234.5]
>>> del a[0]
>>> a
[1, 66.6, 333, 333, 1234.5]
>>> del a[2:4]
>>> a
[1, 66.6, 1234.5]
```

del can also be used to delete entire variables:

del 也可以用于删除整个变量：

```>>> del a
```

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（Tuples）和序列（Sequences ）Tuples and Sequences

We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types. Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple.

```>>> 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))
```

As you see, on output tuples are alway 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).

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.

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',)
```

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
```

This is called, appropriately enough, sequence unpacking. Sequence unpacking requires that the list of variables on the left have the same number of elements as the length of the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking!

There is a small bit of asymmetry here: packing multiple values always creates a tuple, and unpacking works for any sequence.

# 5.4 Dictionaries 字典

Another useful data type built into Python is the dictionary. 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 their append() and extend() methods, as well as slice and indexed assignments.

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.

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.

The keys() method of a dictionary object returns a list of all the keys used in the dictionary, in random order (if you want it sorted, just apply the sort() method to the list of keys). To check whether a single key is in the dictionary, use the has_key() method of the dictionary.

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}
>>> tel.keys()
['guido', 'irv', 'jack']
>>> tel.has_key('guido')
True
```

The dict() constructor builds dictionaries directly from lists of key-value pairs stored as tuples. When the pairs form a pattern, list comprehensions can compactly specify the key-value list.

```>>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])
{'sape': 4139, 'jack': 4098, 'guido': 4127}
>>> dict([(x, x**2) for x in vec])     # use a list comprehension
{2: 4, 4: 16, 6: 36}
```

# 5.5 循环技巧 Looping Techniques

When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the iteritems() method.

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

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
```

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 %s?  It is %s.' % (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.
```

# 5.6 深入条件控制 More on Conditions

The conditions used in `while` and `if` statements above can contain other operators besides comparisons.

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.

`in``not in` 比较操作符审核值是否在一个区间之内。操作符 `is` `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。

Comparisons can be chained. For example, `a < b == c` tests whether `a` is less than `b` and moreover `b` equals `c`.

Comparisons may be combined by the Boolean operators `and` and `or`, and the outcome of a comparison (or of any other Boolean expression) may be negated with `not`. These all have lower priorities than comparison operators again; 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`. Of course, parentheses can be used to express the desired composition.

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`. In general, the return value of a short-circuit operator, when used as a general value and not as a Boolean, is the last evaluated argument.

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'
```

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.

# 5.7 比较序列和其它类型 Comparing Sequences and Other Types

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 ASCII ordering for individual characters. Some examples of comparisons between sequences with the same types:

```(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)
```

Note that comparing objects of different types is legal. The outcome is deterministic but arbitrary: the types are ordered by their name. Thus, a list is always smaller than a string, a string is always smaller than a tuple, etc. Mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc.5.1

#### Footnotes

... etc.5.1
The rules for comparing objects of different types should not be relied upon; they may change in a future version of the language.
...需要注意的是不同类型的对象比较是合法的。输出结果是确定而非任意的：类型按它们的名字排序。因而，一个链表（list）总是小于一个字符串（string），一个字符串（string）总是小于一个元组（tuple）等等。数值类型比较时会统一它们的数据类型，所以0等于0.0，等等。5.2