Python-列表推导式、集合推导式、字典推导式

短命女 2023-06-25 06:04 42阅读 0赞

# Python-列表推导式、集合推导式、字典推导式 #

推导式comprehensions（又称解析式），是Python的一种独有特性。推导式是可以从一个数据序列构建另一个新的数据序列的结构体。共有三种推导式，在Python2和3中都有支持：

*  列表(list)推导式
 *  字典(dict)推导式
 *  集合(set)推导式

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## 1、列表推导式 ##

**1.1、基本格式**

[表达式 for 变量 in 列表] 或者 [表达式 for 变量 in 列表 if 条件]

`例1`: 过滤掉长度小于3的字符串列表，并将剩下的转换成大写字母

*  常规写法

names = ['Bob','Tom','alice','Jerry','Wendy','Smith']
    result = []
    for name in names:
    	if len(name)>3:
    		result.append(name.upper())
    print(result)

*  列表推导式写法（仅添加`判断语句`）

names = ['Bob','Tom','alice','Jerry','Wendy','Smith']
    result = [name.upper() for name in names if len(name)>3]
    
    print(result)

输出结果：

['ALICE', 'JERRY', 'WENDY', 'SMITH']

`例2`：生成间隔5分钟的时间列表序列：

*  常规写法

result = []
    for h in range(0,24):
    	for m in range(0,60,5):
    		result.append("%02d:%02d" %(h,m))
    
    print(result)

*  列表推导式写法(`两层循环语句`)

list1 = ["%02d:%02d" %(h,m) for h in range(0, 24) for m in range(0, 60, 5)]
    print(list1)

输出结果：

['00:00', '00:05', '00:10', '00:15', '00:20', '00:25', '00:30', '00:35', '00:40', '00:45', '00:50', '00:55', '01:00', '01:05', '01:10', '01:15', '01:20', '01:25', '01:30', '01:35', '01:40', '01:45', '01:50', '01:55', '02:00', '02:05', '02:10', '02:15', '02:20', '02:25', '02:30', '02:35', '02:40', '02:45', '02:50', '02:55', '03:00', '03:05', '03:10', '03:15', '03:20', '03:25', '03:30', '03:35', '03:40', '03:45', '03:50', '03:55', '04:00', '04:05', '04:10', '04:15', '04:20', '04:25', '04:30', '04:35', '04:40', '04:45', '04:50', '04:55', '05:00', '05:05', '05:10', '05:15', '05:20', '05:25', '05:30', '05:35', '05:40', '05:45', '05:50', '05:55', '06:00', '06:05', '06:10', '06:15', '06:20', '06:25', '06:30', '06:35', '06:40', '06:45', '06:50', '06:55', '07:00', '07:05', '07:10', '07:15', '07:20', '07:25', '07:30', '07:35', '07:40', '07:45', '07:50', '07:55', '08:00', '08:05', '08:10', '08:15', '08:20', '08:25', '08:30', '08:35', '08:40', '08:45', '08:50', '08:55', '09:00', '09:05', '09:10', '09:15', '09:20', '09:25', '09:30', '09:35', '09:40', '09:45', '09:50', '09:55', '10:00', '10:05', '10:10', '10:15', '10:20', '10:25', '10:30', '10:35', '10:40', '10:45', '10:50', '10:55', '11:00', '11:05', '11:10', '11:15', '11:20', '11:25', '11:30', '11:35', '11:40', '11:45', '11:50', '11:55', '12:00', '12:05', '12:10', '12:15', '12:20', '12:25', '12:30', '12:35', '12:40', '12:45', '12:50', '12:55', '13:00', '13:05', '13:10', '13:15', '13:20', '13:25', '13:30', '13:35', '13:40', '13:45', '13:50', '13:55', '14:00', '14:05', '14:10', '14:15', '14:20', '14:25', '14:30', '14:35', '14:40', '14:45', '14:50', '14:55', '15:00', '15:05', '15:10', '15:15', '15:20', '15:25', '15:30', '15:35', '15:40', '15:45', '15:50', '15:55', '16:00', '16:05', '16:10', '16:15', '16:20', '16:25', '16:30', '16:35', '16:40', '16:45', '16:50', '16:55', '17:00', '17:05', '17:10', '17:15', '17:20', '17:25', '17:30', '17:35', '17:40', '17:45', '17:50', '17:55', '18:00', '18:05', '18:10', '18:15', '18:20', '18:25', '18:30', '18:35', '18:40', '18:45', '18:50', '18:55', '19:00', '19:05', '19:10', '19:15', '19:20', '19:25', '19:30', '19:35', '19:40', '19:45', '19:50', '19:55', '20:00', '20:05', '20:10', '20:15', '20:20', '20:25', '20:30', '20:35', '20:40', '20:45', '20:50', '20:55', '21:00', '21:05', '21:10', '21:15', '21:20', '21:25', '21:30', '21:35', '21:40', '21:45', '21:50', '21:55', '22:00', '22:05', '22:10', '22:15', '22:20', '22:25', '22:30', '22:35', '22:40', '22:45', '22:50', '22:55', '23:00', '23:05', '23:10', '23:15', '23:20', '23:25', '23:30', '23:35', '23:40', '23:45', '23:50', '23:55']

`例3`: 从1到9的序列中，奇数+1操作，偶数+2操作 (使用`三元表达式`)

list1 = [x+2 if x%2 ==0 else x+1 for x in range(1,10,1)]
    print(list1)

输出结果：

[2, 4, 4, 6, 6, 8, 8, 10, 10]

#### 列表推导式总共有两种形式： ####

[x for x in data if condition]

此处if主要起条件判断作用，data数据中只有满足if条件的才会被留下，最后统一生成为一个数据列表

[exp1 if condition else exp2 for x in data]

此处if…else主要起赋值作用，当data中的数据满足if条件时将其做exp1处理，否则按照exp2处理，最后统一生成为一个数据列表

**1.2、使用()生成generator**  
将上述`列表推导式`的\[\]改成()，即可得到`生成器`。

multiples = (i for i in range(30) if i%3 == 0)
    print(type(multiples))
    
    # <class 'generator'>

--------------------

## 2.字典推导式 ##

字典推导和列表推导的使用类似，只不过中括号改成大括号。

基本格式：

{
         key_expr: value_expr for value in collection if condition }

举例说明：

*  用字符串和其长度创建字典

strings = ['import','is','with','if','file','exception']
    D = {
        key: val for val,key in enumerate(strings)}

输出结果:

{
        'exception': 5, 'file': 4, 'if': 3, 'import': 0, 'is': 1, 'with': 2}

*  大小写key合并

mcase = {
        'a': 10, 'b': 34, 'A': 7, 'Z': 3}
    
    mcase_frequency = {
        
        k.lower(): mcase.get(k.lower(), 0) + mcase.get(k.upper(), 0)
        for k in mcase.keys()
        if k.lower() in ['a','b']
    }
    
    print(mcase_frequency)
    #  Output: {'a': 17, 'b': 34}

*  快速更换key和value

mcase = {
        'a': 10, 'b': 34}
    mcase_frequency = {
        v: k for k, v in mcase.items()}
    print(mcase_frequency)
    
    #  Output: {10: 'a', 34: 'b'}

--------------------

## 3. 集合推导式 ##

集合推导式跟列表推导式也是类似的。 唯一的区别在于它使用大括号\{ \}。

基本格式：

{
         expr for value in collection if condition }

举例说明：

*  集合推导式

squared = {
        x**2 for x in [1, 1, 2]}
    print(squared)
    
    # Output: set([1, 4])

*  用集合推导式，创建字符串长度的集合

strings = ['a','is','with','if','file','exception'] 
    
    {
        len(s) for s in strings}  # 有长度相同的会只留一个，这在实际上也非常有用 
    
    # {1, 2, 4, 9}

*  一个由男人列表和女人列表组成的嵌套列表，取出姓名中带有两个以上含字母e的姓名，组成列表

names = [['Tom','Billy','Jefferson','Andrew','Wesley','Steven','Joe'], ['Alice','Jill','Ana','Wendy','Jennifer','Sherry','Eva']]
    tmp = [] 
    
    # 用for循环实现
    for lst in names:
        for name in lst: 
            if name.count('e') >= 2: 
                tmp.append(name)
    
    print(tmp) 
    
    # 用嵌套列表实现
    [name for lst in names for name in lst if name.count('e')>=2] # 注意遍历顺序，这是实现的关键 
    
    # ['Jefferson', 'Wesley', 'Steven', 'Jennifer']

## 4. 参考文章 ##

[http://blog.51cto.com/6226001001/2059536][http_blog.51cto.com_6226001001_2059536]  
[https://blog.csdn.net/yjk13703623757/article/details/79490476][https_blog.csdn.net_yjk13703623757_article_details_79490476]

[http_blog.51cto.com_6226001001_2059536]: http://blog.51cto.com/6226001001/2059536%20%20%E7%82%B9%E8%B5%9E%202
[https_blog.csdn.net_yjk13703623757_article_details_79490476]: https://blog.csdn.net/yjk13703623757/article/details/79490476