《Intermediate Python》Python进阶

短命女 2022-05-14 04:34 221阅读 0赞

[gitbook][]

\*args 和 \*\*kwargs 的用法

def test_var_args(f_arg, *args):
        print('first normal arg: ', f_arg)
        for ar in args:
            print('another arg through *args:', ar)
    test_var_args('one', 'two', 'three', 'four')
    # first normal arg:  one
    # another arg through *args: two
    # another arg through *args: three
    # another arg through *args: four
    
    # **kwargs 允许不定长度的键值对， 作为参数传入给一个函数
    def test_kwargs(**kwargs):
        for key, value in kwargs.items():
            print('%s : %s'%(key, value))
    test_kwargs(name ='jack')
    # name : jack

使用\*args, \*\*kwargs 调用参数为列表或者字典的函数

def test_args_kwargs(arg1, arg2, arg3):
        print('arg1:', arg1)
        print('arg2:', arg2)
        print('arg3:', arg3)
    
    args = ('one', 1, 2)
    test_args_kwargs(*args)
    # arg1: one
    # arg2: 1
    # arg3: 2
    kwargs = {'arg1':'one', 'arg2': 3, 'arg3': 5}
    test_args_kwargs(**kwargs)
    # arg1: one
    # arg2: 3
    # arg3: 5

生成器（generators）：也是一种迭代器，只能对其迭代一次。大多时候通过函数来实现，它们并不是返回一个值，而是yild(生出)一个值

#生成器的简单例子
    def generator_function():
        for i in range(10):
            yield i 
    for item in generator_function():
        print(item, end= ' ,')  # 0 ,1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,
    gen = generator_function()
    print()
    print(next(gen))    # 0 通过内置函数nex获取下一个元素
    #生成器的使用场景是：不想同一时间将所有计算出来的大量结果集分配 到内存当中
    #斐波那契数列
    def fibon(n):
        a= b = 1
        for i in range(n):
            yield a 
            a, b = b, a+b
    f = fibon(5)
    for i in f:
        print(i, end=' ')  #1 1 2 3 5 
    #iter 根据一个迭代对象返回一个迭代器
    my_string = 'hello'
    my_iter = iter(my_string)
    # while True:
    #     print(next(my_iter))    #StopIteration, 有报错
    while True:
        try:
            i = next(my_iter)
        except StopIteration:break
        print(i, end=',')   # h,e,l,l,o,

三元运算符：条件表达式 传统(a>b)?a:b

condition = True
    fitness = ('skinny', 'fat')[condition]#由于True代表1， False代表 0,
    print(fitness)  #fat 
    #一般使用方法：
    print('fat' if condition else 'skinny') # fat

对象可变性

def add_to(num, target=[]):
        target.append(num)
        return target 
    print(add_to(1))    #[1]
    print(add_to(2))    #[1, 2]
    #将原先的数不进行保存
    def add_to1(num, target=[]):
        if target is not None:
            target = []
        target.append(num)
        return target 
    print(add_to1(1))    #[1]
    print(add_to1(2))    #[2]
    #另一种方法
    def add_to2(num, target=None):
        if target is None:
            target=[]
        target.append(num)
        return target
    print(add_to2(1, [1,3]))   # [1, 3, 1]
    print(add_to2(2, [1,3]))   # [1, 3, 2]

python的模块collections 包含许多容器数据类型

from collections import defaultdict
    colours = (('yahoo', 'yellow'),
               ('ali', 'black'),
               ('ali', 'red')
               )
    favorite_colours = defaultdict(list)
    for name, colour in colours:
        favorite_colours[name].append(colour)
    print(favorite_colours)
    # defaultdict(<class 'list'>, {'yahoo': ['yellow'], 'ali': ['black', 'red']})
    print(favorite_colours['ali'])  # ['black', 'red']
    
    import json
    # some_dict = {}
    # some_dict['colours']['favorite'] = 'red'
    # print(some_dict)    # KeyError: 'colours' 会报错
    #使用defaultdict
    tree = lambda: defaultdict(tree)
    some_dict = tree()
    some_dict['colours']['favorite'] = 'red'
    print(some_dict)
    #需要使用dupms 函数 打印出
    print(json.dumps(some_dict))    # {"colours": {"favorite": "red"}}

Counter：Counter 是一个计数器，可以帮助我们进行计数

from collections import Counter
    colours = (('yahoo', 'yellow'),
               ('ali', 'black'),
               ('ali', 'red'),
               ('bb', 'red')
               )
    name = Counter(name for name, colour in colours)
    print(name) #Counter({'ali': 2, 'yahoo': 1, 'bb': 1})
    print(name['ali'])  #2
    colour = Counter(colour for name, colour in colours)
    print(colour)   # Counter({'red': 2, 'yellow': 1, 'black': 1})

defaultdict 函数和 Counter 函数：

#使用defaultdict的方法
    from collections import defaultdict 
    values = ['name', 'dict', 'name', 'dict', 'none']
    counter_values = defaultdict(int)
    for value in values:
        counter_values[value] += 1
    print(counter_values.items()) #dict_items([('name', 2), ('none', 1), ('dict', 2)])
    print(counter_values['name']) #2
    #使用counter的方法
    from collections import Counter 
    counter_values1 = Counter(values)
    print(counter_values1)#Counter({'name': 2, 'dict': 2, 'none': 1})
    counter_values2 = Counter(value for value in values)
    print(counter_values2)#Counter({'name': 2, 'dict': 2, 'none': 1})
    
    demo = (('name', 'age'),
            ('name1', 'age'),
            ('name', 'age1')
            )
    counter_demo = Counter(demo)
    print(counter_demo) #Counter({('name', 'age1'): 1, ('name1', 'age'): 1, ('name', 'age'): 1})
    counter_demo1 = Counter(name for name, age in demo)
    print(counter_demo1)#Counter({'name': 2, 'name1': 1})

nametuple 与元组类似，都不能修改元组中的数据，但可以不用像元组一样使用整数作为索引， 可以类似于字典访问数据

from collections import namedtuple
    Animal = namedtuple('Animal', 'name age type')  #有2个必须的参数 元组名称和字段名称
    dog = Animal(name = 'dog_name', age=12, type='dog')
    print(dog.name) # dog_name
    print(dog[0])   # dog_name 同样也可以还是要整数进行索引

枚举对象，属于enum模块

from enum import Enum
    class Species(Enum):
        cat = 1
        dog = 2
        horse = 3
        
    print(Species.dog)  # Species.dog 不输出对应的数字
    tom = Animal(name = 'chaile', age=12, type = Species.cat)
    jerry = Animal(name ='jerry', age=12, type = Species.dog)
    dog = Animal(name = 'dog_name', age=12, type=Species.dog)
    print(dog.type == jerry.type)   # True
    print(tom.type) #Species.cat
    
    #枚举enum是python中的内置函数，它允许我们遍历数据并自动计数
    some_list = ['red', 'yellow', 'black', 'white']
    for counter, value in enumerate(some_list):
        print(counter, value)
    # 0 red
    # 1 yellow
    # 2 black
    # 3 white
    for counter, value in enumerate(some_list, 10): #输入参数从10开始进行计数
        print(counter, value)
    # 10 red
    # 11 yellow
    # 12 black
    # 13 white

对象自省：程序运行时来判断一个对象的类型的能力

my_list = [1,2,3]
    print(dir(my_list)) #输出列表对象所拥有的属性和方法
    # ['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', '__dir__', 
    # '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', 
    # '__hash__', '__iadd__', '__imul__', '__init__', '__iter__', '__le__', '__len__', '__lt__', 
    # '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', 
    # '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 
    # 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse',
    #  'sort']

> **for\_else 语句**：else语句会在循环正常结束后执行  
> 用 break 关键字终止当前循环就不会执行当前的 else 语句(break 可以阻止 else 语句块的执行。)，而使用 continue 关键字快速进入下一轮循环，或者没有使用其他关键字，循环的正常结束后，就会触发 else 语句。

for n in range(2, 17):
        for x in range(2, int(n/2)):
            if n%x == 0:
                print(n, 'equals', x, '*', n/x)
                break   # 如果条件成立，则不执行下面的else语句
        else:
            print(n, 'is a prime number')
    # 2 is a prime number
    # 3 is a prime number
    # 4 is a prime number
    # 5 is a prime number
    # 6 equals 2 * 3.0
    # 7 is a prime number
    # 8 equals 2 * 4.0
    # 9 equals 3 * 3.0
    # 10 equals 2 * 5.0
    # 11 is a prime number
    # 12 equals 2 * 6.0
    # 13 is a prime number
    # 14 equals 2 * 7.0
    # 15 equals 3 * 5.0
    # 16 equals 2 * 8.0
    for i in range(10): #如果循环有break的话， 只会输出一个结果
        if i > 15:
            print('>5')
            break
    else:
        print('hello')  # hello

[gitbook]: https://eastlakeside.gitbooks.io/interpy-zh/content/