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内置集合数据类型

除了数字类和布尔类之外，Python 还有许多非常强大的内置集合类。 列表、字符串和元组是有序集合，它们在一般结构上非常相似，但具有特定的差异，必须理解这些差异才能正确使用它们。 集合和字典是无序集合。

列表

列表是零个或多个对 Python 数据对象的引用的有序集合。 列表被写为括在方括号中的逗号分隔值。 空列表就是[ ]。 列表是异构的，这意味着数据对象不必全部来自同一类，并且可以将集合分配给变量，如下所示。 以下片段显示了列表中的各种 Python 数据对象。

>>> [1, 3, True, 6.5]
[1, 3, True, 6.5]
>>> my_list = [1, 3, True, 6.5]
>>> my_list
[1, 3, True, 6.5]

请注意，当 Python 计算(evaluates)列表时，会返回列表本身。 但是，为了记住该列表以供以后处理，需要将其引用分配给一个变量。

由于列表被认为是按顺序排序的，因此它们支持许多可应用于任何 Python 序列的操作。 “表 2”回顾了这些操作，下面的课程给出了它们的使用示例。

表 2：Python 中任意序列的操作

运算符名称 运算符 解释
索引 [ ] 访问序列的元素
串联 + 将序列组合在一起
重复 * 连接重复次数
会员 in 询问某个项目是否在序列中
长度 len 询问序列中的项目数
切片 [ : ] 提取序列的一部分

请注意，列表（序列）的索引从 0 开始计数。切片操作 my_list[1:3] 返回一个项目列表，该列表以索引为 1 的项目开始，直到（但不包括）索引为的项目 3.

有时您会想要初始化一个列表。 这可以通过重复来快速完成。 例如，

>>> my_list = [0] * 6
>>> my_list
[0, 0, 0, 0, 0, 0]

与重复运算符相关的一个非常重要的问题是，结果是重复对序列中的数据对象的引用。 通过考虑以下会议可以最好地看出这一点：

参考文献的重复
my_list = [1, 2, 3, 4]
big_list = [my_list] * 3
print(big_list)
my_list[2] = 45
print(big_list)

变量big_list保存对原始列表my_list的三个引用的集合。 请注意，对 my_list 的一个元素的更改会出现在 big_list 中的所有三个事件中。

列表支持许多用于构建数据结构的方法。 “表 3”提供了摘要。 它们的使用示例如下。

表 3：Python 中列表提供的方法

方法名称 用法 解释
append a_list.append(item) 将新项目添加到列表末尾
insert a_list.insert(i,item) 在列表中的第 i 个位置插入一个项目
pop a_list.pop() 删除并返回列表中的最后一项
pop a_list.pop(i) 删除并返回列表中的第 i 个项目
sort a_list.sort() 对列表进行适当排序
reverse a_list.reverse() 将列表修改为倒序
del del a_list[i] 删除第 i 个位置的项目
index a_list.index(item) 返回第一次出现item的索引
count a_list.count(item) 返回item出现的次数
remove a_list.remove(item) 删除第一次出现的item
列表方法的示例
my_list = [1024, 3, True, 6.5]
my_list.append(False)
print(my_list)
my_list.insert(2,4.5)
print(my_list)
print(my_list.pop())
print(my_list)
print(my_list.pop(1))
print(my_list)
my_list.pop(2)
print(my_list)
my_list.sort()
print(my_list)
my_list.reverse()
print(my_list)
print(my_list.count(6.5))
print(my_list.index(4.5))
my_list.remove(6.5)
print(my_list)
del my_list[0]
print(my_list)

您可以看到某些方法，例如pop，返回一个值并修改列表。 其他的，例如reverse，只是简单地修改列表，没有返回值。 pop 将默认位于列表末尾，但也可以删除并返回特定项目。 这些方法再次使用从 0 开始的索引范围。 您还应该注意到熟悉的“点(.)”表示法，用于要求对象调用方法。 my_list.append(False) 可以理解为“要求对象 my_list 执行其 append 方法并向其发送值 False。 即使是简单的数据对象（例如整数）也可以通过这种方式调用方法。

>>> (54).__add__(21)
75

在这个片段中，我们要求整数对象54执行其add方法（在Python中称为__add__）并将其传递21作为要添加的值。 结果是总和75。 当然，我们通常写成54+21。 我们将在本节后面详细介绍这些方法。

经常与列表一起讨论的一种常见 Python 函数是range函数。 range 生成一个表示值序列的范围对象。 通过使用list函数，可以将范围对象的值视为列表。 如下图所示。

>>> range(10)
range(0, 10)
>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> range(5, 10)
range(5, 10)
>>> list(range(5, 10))
[5, 6, 7, 8, 9]
>>> list(range(5, 10, 2))
[5, 7, 9]
>>> list(range(10, 1, -1))
[10, 9, 8, 7, 6, 5, 4, 3, 2]

range 对象表示一个整数序列。 默认情况下，它将从 0 开始。如果您提供更多参数，它将在特定点开始和结束，甚至可以跳过项目。 在我们的第一个示例中，range(10)，序列从 0 开始，一直上升到但不包括 10。在我们的第二个示例中，range(5, 10)从 5 开始，一直上升 到但不包括 10。range(5, 10, 2) 执行类似，但跳过两个（同样，不包括 10）。

字符串

字符串是零个或多个字母、数字和其他符号的连续集合。 我们将这些字母、数字和其他符号称为字符。 文字字符串值通过使用引号（单引号或双引号）与标识符区分开来。

>>> "David"
'David'
>>> my_name = "David"
>>> my_name[3]
'i'
>>> my_name * 2
'DavidDavid'
>>> len(my_name)
5

由于字符串是序列，因此上述所有序列操作都按您的预期工作。 此外，字符串还有许多方法，其中一些方法如“表 4”所示。

表 4：Python 中字符串提供的方法

方法名称 用法 解释
center a_string.center(w) 返回以大小为w的字段为中心的字符串
count a_string.count(item) 返回字符串中item出现的次数
ljust a_string.ljust(w) 返回在大小为w的字段中左对齐的字符串
lower a_string.lower() 返回全小写的字符串
rjust a_string.rjust(w) 返回在大小为w的字段中右对齐的字符串
find a_string.find(item) 返回第一次出现item的索引
split a_string.split(s_char) 将字符串拆分为 s_char 处的子字符串

其中，split对于处理数据非常有用。 split 将接受一个字符串并使用分割字符作为分割点返回一个字符串列表。 在下面的示例中，v是划分点。 如果未指定除法，则 split 方法将查找空白字符，例如制表符、换行符和空格。

>>> my_name
'David'
>>> my_name.upper()
'DAVID'
>>> my_name.center(10)
'  David   '
>>> my_name.find("v")
2
>>> my_name.split("v")
['Da', 'id']

列表和字符串之间的主要区别是列表可以修改，而字符串则不能。 这称为可变性。 列表是可变的； 字符串是不可变的。 例如，您可以使用索引和分配来更改列表中的项目。 对于不允许更改的字符串，如下所示。

>>> my_list
[1, 3, True, 6.5]
>>> my_list[0] = 2 ** 10
>>> my_list
[1024, 3, True, 6.5]
>>>
>>> my_name
'David'
>>> my_name[0] = "X"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'str' object does not support item assignment

元组

元组与列表非常相似，因为它们是异构数据序列。 不同之处在于元组是不可变的，就像字符串一样。 元组无法更改。 元组被写为括在括号中的逗号分隔值。 作为序列，它们可以使用上述任何操作。 例如，

>>> my_tuple = (2, True, 4.96)
>>> my_tuple
(2, True, 4.96)
>>> len(my_tuple)
3
>>> my_tuple[0]
2
>>> my_tuple * 3
(2, True, 4.96, 2, True, 4.96, 2, True, 4.96)
>>> my_tuple[0:2]
(2, True)

但是，如果您尝试更改元组中的项目，则会收到错误。 请注意，错误消息提供了问题的位置和原因。

>>> my_tuple[1] = False
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'tuple' object does not support item assignment

集合

集合 是零个或多个不可变 Python 数据对象的无序集合。 集合不允许重复，并且被写为用大括号括起来的逗号分隔值。 空集由set()表示。 集合是异构的，集合可以分配给变量，如下所示。

>>> {3, 6, "cat", 4.5, False}
{False, 3, 4.5, 6, 'cat'}
>>> my_set = {3, 6, "cat", 4.5, False}
>>> my_set
{False, 3, 4.5, 6, 'cat'}

尽管集合不被认为是连续的，但它们确实支持前面介绍的一些熟悉的操作。 表 5 回顾了这些操作，下面的会议给出了它们的使用示例。

表 5：Python 中集合的操作

运算符名称 运算符 解释
项 in 时候为集合中的项
长度 len 返回集合的长度
| a_set | other_set 返回一个新集合，其中包含两个集合中的所有元素
& a_set & other_set 返回一个新集合，其中仅包含两个集合共有的元素
- a_set - other_set 返回一个新集合，其中包含第一个集合中不属于第二个集合中的所有项目
<= a_set <= other_set 询问第一个集合的所有元素是否都在第二个集合中 
>>> my_set
{False, 3, 4.5, 6, 'cat'}
>>> len(my_set)
5
>>> False in my_set
True
>>> "dog" in my_set
False

集合支持许多方法，那些在数学环境中使用过集合的人应该熟悉这些方法。 表 6 提供了摘要。 它们的使用示例如下。 请注意，union、intersection、issubset和difference都具有可以使用的运算符。

表 6：Python 中集合提供的方法

方法名称 用法 解释
union a_set.union(other_set) 返回一个新集合，其中包含两个集合中的所有元素
intersection a_set.intersection(other_set) 返回一个新集合，其中仅包含两个集合共有的元素
difference a_set.difference(other_set) 返回一个新集合，其中包含第一个集合中不属于第二个集合中的所有项目
issubset a_set.issubset(othe_rset) 询问一个集合的所有元素是否都在另一个集合中
add a_set.add(item) 将项目添加到集合中
remove a_set.remove(item) 从集合中删除项目
pop a_set.pop() 从集合中删除任意元素
clear a_set.clear() 从集合中删除所有元素 
>>> my_set
{False, 3, 4.5, 6, 'cat'}
>>> your_set = {99, 3, 100}
>>> my_set.union(your_set)
{False, 3, 4.5, 'cat', 6, 99, 100}
>>> my_set | your_set
{False, 3, 4.5, 'cat', 6, 99, 100}
>>> my_set.intersection(your_set)
{3}
>>> my_set & your_set
{3}
>>> my_set.difference(your_set)
{False, 'cat', 4.5, 6}
>>> my_set - your_set
{False, 'cat', 4.5, 6}
>>> {3, 100}.issubset(your_set)
True
>>> {3, 100} <= your_set
True
>>> my_set.add("house")
>>> my_set
{False, 'house', 3, 4.5, 6, 'cat'}
>>> my_set.remove(4.5)
>>> my_set
{False, 'house', 3, 6, 'cat'}
>>> my_set.pop()
False
>>> my_set
{'house', 3, 6, 'cat'}
>>> my_set.clear()
>>> my_set
set()

我们最终的 Python 集合是一个无序结构，称为 字典。 字典是相关项目对的集合，其中每对都包含一个键和一个值。 该键值对通常写为键：值。 字典以逗号分隔的键：值对形式编写，并括在大括号中。 例如，

>>> capitals = {"Iowa": "Des Moines", "Wisconsin": "Madison"}
>>> capitals
{'Iowa': 'Des Moines', 'Wisconsin': 'Madison'}

我们可以通过键访问值或添加另一个键值对来操作字典。 访问的语法看起来很像序列访问，只不过我们不使用项目的索引，而是使用键值。 添加新值也是类似的。

Using a Dictionary
capitals = {"Iowa": "Des Moines", "Wisconsin": "Madison"}
print(capitals["Iowa"])
capitals["Utah"] = "Salt Lake City"
print(capitals)
capitals["California"] = "Sacramento"
print(len(capitals))
for k in capitals:
    print(capitals[k],"is the capital of", k)

值得注意的是，在 Python 3.6 之前，字典的维护顺序与键没有特定的顺序。 添加的第一对 ("Utah": "Salt Lake City") 将放在字典中的第一位，添加的第二对 ("California": "Sacramento") 将放在最后。 键的放置取决于 散列 的思想，这将在第 5 章中更详细地解释。自 Python 3.6 以来，字典确实保持顺序，因此在上面的示例中，对按照它们在字典中出现的顺序出现。 添加。 我们还展示了长度函数，其作用与其他集合相同。

字典既有方法又有运算符。 “表 7”和“表 8”对它们进行了描述，会议展示了它们的实际应用。 keys、values和items方法都返回包含感兴趣值的对象。 您可以使用list函数将它们转换为列表。 您还将看到get方法有两种变体。 如果字典中不存在该键，get将返回None。 但是，第二个可选参数可以指定返回值。

表 7：Python 中字典提供的运算符

运算符 用法 解释
[] a_dict[k] 返回与k关联的值，否则错误
in key in a_dict 如果键在字典中则返回True，否则返回False
del del a_dict[key] 从字典中删除条目

表 8：Python 中字典提供的方法

方法名称 用法 解释
keys a_dict.keys() 返回 dict_keys 对象中字典的键
values a_dict.values() 返回 dict_values 对象中字典的值
items a_dict.items() 返回 dict_items 对象中的键值对
get a_dict.get(k) 返回与k相关的值，否则返回None
get a_dict.get(k, alt) 否则返回与k、alt关联的值 
>>> phone_ext={"david": 1410, "brad": 1137, "roman": 1171}
>>> phone_ext
{'david': 1410, 'brad': 1137, 'roman': 1171}
>>> phone_ext.keys()
dict_keys(['david', 'brad', 'roman'])
>>> list(phone_ext.keys())
['david', 'brad', 'roman']
>>> phone_ext.values()
dict_values([1410, 1137, 1171])
>>> list(phone_ext.values())
[1410, 1137, 1171]
>>> phone_ext.items()
dict_items([('david', 1410), ('brad', 1137), ('roman', 1171)])
>>> list(phone_ext.items())
[('david', 1410), ('brad', 1137), ('roman', 1171)]
>>> phone_ext.get("kent")
>>> phone_ext.get("kent", "NO ENTRY")
'NO ENTRY'

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原文

Built-in Collection Data Types

In addition to the numeric and Boolean classes, Python has a number of very powerful built-in collection classes. Lists, strings, and tuples are ordered collections that are very similar in general structure but have specific differences that must be understood for them to be used properly. Sets and dictionaries are unordered collections.

A list is an ordered collection of zero or more references to Python data objects. Lists are written as comma-delimited values enclosed in square brackets. The empty list is simply [ ]. Lists are heterogeneous, meaning that the data objects need not all be from the same class and the collection can be assigned to a variable as below. The following fragment shows a variety of Python data objects in a list.

>>> [1, 3, True, 6.5]
[1, 3, True, 6.5]
>>> my_list = [1, 3, True, 6.5]
>>> my_list
[1, 3, True, 6.5]

Note that when Python evaluates a list, the list itself is returned. However, in order to remember the list for later processing, its reference needs to be assigned to a variable.

Since lists are considered to be sequentially ordered, they support a number of operations that can be applied to any Python sequence. Table 2 reviews these operations and the following session gives examples of their use.

Table 2: Operations on Any Sequence in Python

Operation Name Operator Explanation
indexing [ ] Access an element of a sequence
concatenation + Combine sequences together
repetition * Concatenate a repeated number of times
membership in Ask whether an item is in a sequence
length len Ask the number of items in the sequence
slicing [ : ] Extract a part of a sequence

Note that the indices for lists (sequences) start counting with 0. The slice operation my_list[1:3] returns a list of items starting with the item indexed by 1 up to---but not including---the item indexed by 3.

Sometimes you will want to initialize a list. This can quickly be accomplished by using repetition. For example,

>>> my_list = [0] * 6
>>> my_list
[0, 0, 0, 0, 0, 0]

One very important aside relating to the repetition operator is that the result is a repetition of references to the data objects in the sequence. This can best be seen by considering the following session:

Repetition of References
my_list = [1, 2, 3, 4]
big_list = [my_list] * 3
print(big_list)
my_list[2] = 45
print(big_list)

The variable big_list holds a collection of three references to the original list called my_list. Note that a change to one element of my_list shows up in all three occurrences in big_list.

Lists support a number of methods that will be used to build data structures. Table 3 provides a summary. Examples of their use follow.

Table 3: Methods Provided by Lists in Python

Method Name Use Explanation
append a_list.append(item) Adds a new item to the end of a list
insert a_list.insert(i,item) Inserts an item at the ith position in a list
pop a_list.pop() Removes and returns the last item in a list
pop a_list.pop(i) Removes and returns the ith item in a list
sort a_list.sort() Sorts a list in place
reverse a_list.reverse() Modifies a list to be in reverse order
del del a_list[i] Deletes the item in the ith position
index a_list.index(item) Returns the index of the first occurrence of item
count a_list.count(item) Returns the number of occurrences of item
remove a_list.remove(item) Removes the first occurrence of item
Examples of List Methods
my_list = [1024, 3, True, 6.5]
my_list.append(False)
print(my_list)
my_list.insert(2,4.5)
print(my_list)
print(my_list.pop())
print(my_list)
print(my_list.pop(1))
print(my_list)
my_list.pop(2)
print(my_list)
my_list.sort()
print(my_list)
my_list.reverse()
print(my_list)
print(my_list.count(6.5))
print(my_list.index(4.5))
my_list.remove(6.5)
print(my_list)
del my_list[0]
print(my_list)

You can see that some of the methods, such as pop, return a value and also modify the list. Others, such as reverse, simply modify the list with no return value. pop will default to the end of the list but can also remove and return a specific item. The index range starting from 0 is again used for these methods. You should also notice the familiar “dot” notation for asking an object to invoke a method. my_list.append(False) can be read as “ask the object my_list to perform its append method and send it the value False.” Even simple data objects such as integers can invoke methods in this way.

>>> (54).__add__(21)
75

In this fragment we are asking the integer object 54 to execute its add method (called __add__ in Python) and passing it 21 as the value to add. The result is the sum, 75. Of course, we usually write this as 54+21. We will say much more about these methods later in this section.

One common Python function that is often discussed in conjunction with lists is the range function. range produces a range object that represents a sequence of values. By using the list function, it is possible to see the value of the range object as a list. This is illustrated below.

>>> range(10)
range(0, 10)
>>> list(range(10))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> range(5, 10)
range(5, 10)
>>> list(range(5, 10))
[5, 6, 7, 8, 9]
>>> list(range(5, 10, 2))
[5, 7, 9]
>>> list(range(10, 1, -1))
[10, 9, 8, 7, 6, 5, 4, 3, 2]

The range object represents a sequence of integers. By default, it will start with 0. If you provide more parameters, it will start and end at particular points and can even skip items. In our first example, range(10), the sequence starts with 0 and goes up to but does not include 10. In our second example, range(5, 10) starts at 5 and goes up to but does not include 10. range(5, 10, 2) performs similarly but skips by twos (again, 10 is not included).

Strings are sequential collections of zero or more letters, numbers, and other symbols. We call these letters, numbers, and other symbols characters. Literal string values are differentiated from identifiers by using quotation marks (either single or double).

>>> "David"
'David'
>>> my_name = "David"
>>> my_name[3]
'i'
>>> my_name * 2
'DavidDavid'
>>> len(my_name)
5

Since strings are sequences, all of the sequence operations described above work as you would expect. In addition, strings have a number of methods, some of which are shown in Table 4.

Table 4: Methods Provided by Strings in Python

Method Name Use Explanation
center a_string.center(w) Returns a string centered in a field of size w
count a_string.count(item) Returns the number of occurrences of item in the string
ljust a_string.ljust(w) Returns a string left-justified in a field of size w
lower a_string.lower() Returns a string in all lowercase
rjust a_string.rjust(w) Returns a string right-justified in a field of size w
find a_string.find(item) Returns the index of the first occurrence of item
split a_string.split(s_char) Splits a string into substrings at s_char

Of these, split will be very useful for processing data. split will take a string and return a list of strings using the split character as a division point. In the example below, “v” is the division point. If no division is specified, the split method looks for whitespace characters such as tab, newline, and space.

>>> my_name
'David'
>>> my_name.upper()
'DAVID'
>>> my_name.center(10)
'  David   '
>>> my_name.find("v")
2
>>> my_name.split("v")
['Da', 'id']

A major difference between lists and strings is that lists can be modified while strings cannot. This is referred to as mutability. Lists are mutable; strings are immutable. For example, you can change an item in a list by using indexing and assignment. With a string that change is not allowed, as shown below.

>>> my_list
[1, 3, True, 6.5]
>>> my_list[0] = 2 ** 10
>>> my_list
[1024, 3, True, 6.5]
>>>
>>> my_name
'David'
>>> my_name[0] = "X"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'str' object does not support item assignment

Tuples are very similar to lists in that they are heterogeneous sequences of data. The difference is that a tuple is immutable, like a string. A tuple cannot be changed. Tuples are written as comma-delimited values enclosed in parentheses. As sequences, they can use any operation described above. For example,

>>> my_tuple = (2, True, 4.96)
>>> my_tuple
(2, True, 4.96)
>>> len(my_tuple)
3
>>> my_tuple[0]
2
>>> my_tuple * 3
(2, True, 4.96, 2, True, 4.96, 2, True, 4.96)
>>> my_tuple[0:2]
(2, True)

However, if you try to change an item in a tuple, you will get an error. Note that the error message provides the location and reason for the problem.

>>> my_tuple[1] = False
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'tuple' object does not support item assignment

A set is an unordered collection of zero or more immutable Python data objects. Sets do not allow duplicates and are written as comma-delimited values enclosed in curly braces. The empty set is represented by set(). Sets are heterogeneous, and the collection can be assigned to a variable as below.

>>> {3, 6, "cat", 4.5, False}
{False, 3, 4.5, 6, 'cat'}
>>> my_set = {3, 6, "cat", 4.5, False}
>>> my_set
{False, 3, 4.5, 6, 'cat'}

Even though sets are not considered to be sequential, they do support a few of the familiar operations presented earlier. :ref:Table 5 <tab_setops> reviews these operations and the following session gives examples of their use.

Table 5: Operations on a Set in Python

Operation Name Operator Explanation
membership in Set membership
length len Returns the cardinality of the set
| a_set | other_set Returns a new set with all elements from both sets
& a_set & other_set Returns a new set with only those elements common to both sets
- a_set - other_set Returns a new set with all items from the first set not in the second
<= a_set <= other_set Asks whether all elements of the first set are in the second 
>>> my_set
{False, 3, 4.5, 6, 'cat'}
>>> len(my_set)
5
>>> False in my_set
True
>>> "dog" in my_set
False

Sets support a number of methods that should be familiar to those who have worked with them in a mathematics setting. :ref:Table 6 <tab_setmethods> provides a summary. Examples of their use follow. Note that union, intersection, issubset, and difference all have operators that can be used as well.

Table 6: Methods Provided by Sets in Python

Method Name Use Explanation
union a_set.union(other_set) Returns a new set with all elements from both sets
intersection a_set.intersection(other_set) Returns a new set with only those elements common to both sets
difference a_set.difference(other_set) Returns a new set with all items from the first set not in the second
issubset a_set.issubset(othe_rset) Asks whether all elements of one set are in the other
add a_set.add(item) Adds item to the set
remove a_set.remove(item) Removes item from the set
pop a_set.pop() Removes an arbitrary element from the set
clear a_set.clear() Removes all elements from the set 
>>> my_set
{False, 3, 4.5, 6, 'cat'}
>>> your_set = {99, 3, 100}
>>> my_set.union(your_set)
{False, 3, 4.5, 'cat', 6, 99, 100}
>>> my_set | your_set
{False, 3, 4.5, 'cat', 6, 99, 100}
>>> my_set.intersection(your_set)
{3}
>>> my_set & your_set
{3}
>>> my_set.difference(your_set)
{False, 'cat', 4.5, 6}
>>> my_set - your_set
{False, 'cat', 4.5, 6}
>>> {3, 100}.issubset(your_set)
True
>>> {3, 100} <= your_set
True
>>> my_set.add("house")
>>> my_set
{False, 'house', 3, 4.5, 6, 'cat'}
>>> my_set.remove(4.5)
>>> my_set
{False, 'house', 3, 6, 'cat'}
>>> my_set.pop()
False
>>> my_set
{'house', 3, 6, 'cat'}
>>> my_set.clear()
>>> my_set
set()

Our final Python collection is an unordered structure called a dictionary. Dictionaries are collections of associated pairs of items where each pair consists of a key and a value. This key-value pair is typically written as key:value. Dictionaries are written as comma-delimited key:value pairs enclosed in curly braces. For example,

>>> capitals = {"Iowa": "Des Moines", "Wisconsin": "Madison"}
>>> capitals
{'Iowa': 'Des Moines', 'Wisconsin': 'Madison'}

We can manipulate a dictionary by accessing a value via its key or by adding another key-value pair. The syntax for access looks much like a sequence access except that instead of using the index of the item, we use the key value. To add a new value is similar.

Using a Dictionary
capitals = {"Iowa": "Des Moines", "Wisconsin": "Madison"}
print(capitals["Iowa"])
capitals["Utah"] = "Salt Lake City"
print(capitals)
capitals["California"] = "Sacramento"
print(len(capitals))
for k in capitals:
    print(capitals[k],"is the capital of", k)

It is important to note that prior to Python 3.6 dictionaries were maintained in no particular order with respect to the keys. The first pair added ("Utah": "Salt Lake City") would be placed first in the dictionary and the second pair added ("California": "Sacramento") would be placed last. The placement of a key is dependent on the idea of hashing, which will be explained in more detail in Chapter 5. Dictionaries do maintain the order since Python 3.6, so in the example above the pairs appear in the dictionary in the order they were added. We also show the length function performing the same role as with other collections.

Dictionaries have both methods and operators. Table 7 and Table 8 describe them, and the session shows them in action. The keys, values, and items methods all return objects that contain the values of interest. You can use the list function to convert them to lists. You will also see that there are two variations on the get method. If the key is not present in the dictionary, get will return None. However, a second, optional parameter can specify a return value instead.

Table 7: Operators Provided by Dictionaries in Python

Operator Use Explanation
[] a_dict[k] Returns the value associated with k, otherwise its an error
in key in a_dict Returns True if key is in the dictionary, False otherwise
del del a_dict[key] Removes the entry from the dictionary

Table 8: Methods Provided by Dictionaries in Python

Method Name Use Explanation
keys a_dict.keys() Returns the keys of the dictionary in a dict_keys object
values a_dict.values() Returns the values of the dictionary in a dict_values object
items a_dict.items() Returns the key-value pairs in a dict_items object
get a_dict.get(k) Returns the value associated with k, None otherwise
get a_dict.get(k, alt) Returns the value associated with k, alt otherwise 
>>> phone_ext={"david": 1410, "brad": 1137, "roman": 1171}
>>> phone_ext
{'david': 1410, 'brad': 1137, 'roman': 1171}
>>> phone_ext.keys()
dict_keys(['david', 'brad', 'roman'])
>>> list(phone_ext.keys())
['david', 'brad', 'roman']
>>> phone_ext.values()
dict_values([1410, 1137, 1171])
>>> list(phone_ext.values())
[1410, 1137, 1171]
>>> phone_ext.items()
dict_items([('david', 1410), ('brad', 1137), ('roman', 1171)])
>>> list(phone_ext.items())
[('david', 1410), ('brad', 1137), ('roman', 1171)]
>>> phone_ext.get("kent")
>>> phone_ext.get("kent", "NO ENTRY")
'NO ENTRY'

Note

This workspace is provided for your convenience. You can use this activecode window to try out anything you like.

最后更新: 2023年10月15日
创建日期: 2023年10月15日