[Python知识库]Python进阶

*args的用法

def test_var_args(f_arg, *args):
    print("f_arg："+f_arg)
    for a in args:
        print("args："+a)
if __name__ == '__main__':
    test_var_args("a","b","v","2")

### **kwargs的用法

def greet_me(**kwargs):
    for key, value in kwargs.items():
        print("{0} == {1}".format(key, value))
if __name__ == '__main__':
    greet_me(name = "yasob")

### 使用*args 和 **kwargs 来调用函数

def test_args_kwargs(arg1, arg2, arg3):
    print("arg1:", arg1)
    print("arg2:", arg2)
    print("arg3:", arg3)

#### 首先使用*args

agrs = {"1","2","3"}
test_args_kwargs(*agrs)

#### 现在使用**kwargs:

kwargs = {"arg1":1,"arg2":2,"arg3":3}
test_args_kwargs(**kwargs)

那么如果你想在函数?同时使?所有这三种参数， 顺序是这样的： some_func(fargs, *args, **kwargs)

生成器 Generators

可迭代对象(Iterable)

?个迭代器是任意?个对象，只要它定义了?个next(Python2) 或者__next__?法。就 这么简单。

迭代(Iteration)

?简单的话讲，它就是从某个地?（?如?个列表）取出?个元素的过程。当我们使?? 个循环来遍历某个东西时，这就叫?个迭代。

## 生成器 Generators
'''
生成器就是生成的数不会在内存中 只是生成了还没有用他 等用的时候才会放 节省资源  大多数生成器以函数形式出现
'''
def generator_function():
    for i in range(1,10):
        yield i
gen = generator_function()
print(next(gen))
print(next(gen))
print(next(gen))
print(next(gen))
print(next(gen))
print(next(gen))
print(next(gen))
print(next(gen))
print(next(gen))
print(next(gen)) # d到第十次报错 因为判断next已经没有数值了 但是for循环就不会报错  因为for循环会自动捕捉到这个异常并停止调用next()

for item in generator_function():
    print(item)

'''
下面是一个计算斐波那契数列的生成器：
'''
def fibon(n):
    a = b = 1
    for i in range(n):
        yield a
        a, b = b, a+b
for x in fibon(10000):
    print(x)

my_string = "Yasoob"
next(my_string)
# 会报错 这个异常说那个str对象不是个迭代器
# Traceback (most recent call last):
#   File "D:/python/pyText/GeneratorsDemo.py", line 34, in <module>
#     next(my_string)
# TypeError: 'str' object is not an iterator

# iter它将根据一个可迭代对象返回一个迭代器对象
my_string = "Yasoob"
my_iter = iter(my_string)
next(my_iter)

Map ， Filter， Set

'''
Map 和 Filter这两个函数能为函数式编程提供便利
'''


# items = [1,2,3,4,5]
# list = []
# for i in items:
#     list.append(i)
# print(list)
# print(type(list)) # <class 'list'>


# items = [1,2,3,4,5]
# squared = list(map(lambda x: x**2, items))
# print(squared) # [1, 4, 9, 16, 25]

def multiply(x):
    return x * x


def add(x):
    return x + x


funs = [multiply, add]
for i in range(5):
    value = list(map(lambda x: x(i), funs))
    print(value)
# [0, 0]
# [1, 2]
# [4, 4]
# [9, 6]
# [16, 8]

# Filter 相当于是一个过滤
number_list = [1, 2, 3, 4, 5, -9, -7, -56, -4]
result = list(filter(lambda x: x > 0, number_list))
print(result)  # [1, 2, 3, 4, 5]

### set
some_list = ['a', 'b', 'c', 'b', 'd', 'm', 'n', 'n']
duplicates = []
for value in some_list:
    if some_list.count(value) > 1:
        if value not in duplicates:
            duplicates.append(value)
print(duplicates)  # ['b', 'n']

'''
更优雅的解决方案  set无序
'''
some_list = ['a', 'b', 'c', 'b', 'd', 'm', 'n', 'n']
duplicates = set([x for x in some_list if some_list.count(x) > 1])
print(duplicates)  # {'b', 'n'}

#### 交集
valid = set(['yellow', 'red', 'blue', 'green', 'black'])
input_set = set(['red', 'brown'])
print(input_set.intersection(valid))  ### 输出: set(['red'])

#### 差集
valid = set(['yellow', 'red', 'blue', 'green', 'black'])
input_set = set(['red', 'brown'])
print(input_set.difference(valid))  ### 输出: set(['brown'])

三元运算符

#三元运算符通常在Python?被称为条件表达式，这些表达式基于真(true)/假(not)的条件判 断，在Python 2.4以上才有了三元操作

is_fat = True
state = "fat" if is_fat else "not fat"
print(state) # fat

枚举

some_list = ['apple', 'banana', 'grapes', 'pear']
for index, value in enumerate(some_list):
    print(index, value)
# 0 apple
# 1 banana
# 2 grapes
# 3 pear
my_list = ['apple', 'banana', 'grapes', 'pear']
counter_list = list(enumerate(my_list, 1))
print(counter_list)
# [(1, 'apple'), (2, 'banana'), (3, 'grapes'), (4, 'pear')]

列表推导式 字典推导式 集合推导式

# 它的结构是在?个中括号?包含?个表达式，然后是?个for语句，然后是0个或多个for 或者if语句。那个表达式可以是任意的，意思是你可以在列表中放?任意类型的对象。返 回结果将是?个新的列表，在这个以if和for语句为上下?的表达式运?完成之后产?。
multiples = [i for i in range(50) if i % 3 == 0]
print(multiples)
# [0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39, 42, 45, 48]

squared = []
for x in range(10):
    squared.append(x ** 2)

# 列表推导式简化
squared1 = [x ** 2 for x in range(10)]

# 字典推导式
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()}
print(mcase_frequency)
# mcase_frequency == {'a': 17, 'z': 3, 'b': 34}
# 在上?的例?中我们把同?个字母但不同??写的值合并起来了。

# 集合推导式
squared = {x ** 2 for x in [1, 1, 2]}
print(squared)
# {1, 4}

处理多个异常

try:
    file = open('test.txt', 'rb')
except Exception:
# 打印?些异常?志，如果你想要的话
      raise