# 公众号：Python 实用宝典
# 2021-05-04
from shodan import Shodan
api = Shodan('你的API KEY')
def search_shodan(keyword):
# 调用搜索接口
result = api.search(keyword)
# 显示所有IP
for service in result['matches']:
print(service['ip_str'])
search_shodan("Hikvision-Webs")
python3 event.py
function_1 called
function_2 called
function_3 called
function_1 called
function_2 called
function_3 called
function_1 called
function_2 called
function_3 called
df = pd.DataFrame([ ... [1, 2, 3, 4], ... [5, 6, 7, 8], ... [9, 10, 11, 12] ... ]).set_index([0, 1]).rename_axis(['a', 'b']) >>> df.columns = pd.MultiIndex.from_tuples([ ... ('c', 'e'), ('d', 'f') ... ], names=['level_1', 'level_2']) >>> df level_1 c d level_2 e f a b 1234 5678 9101112 >>> df.droplevel('a') level_1 c d level_2 e f b 234 678 101112 >>> df.droplevel('level2', axis=1) level_1 c d a b 1234 5678 9101112
>>> from jnius import autoclass
>>> autoclass('java.lang.System').out.println('Hello world')
Hello world
>>> Stack = autoclass('java.util.Stack')
>>> stack = Stack()
>>> stack.push('hello')
>>> stack.push('world')
>>> print(stack.pop())
world
>>> print(stack.pop())
hello
当你引入类后，你只需要按 Java 的函数操作即可，如上述代码中的 push 和 pop 函数。
最令人惊喜的是，你还能在安卓系统中利用这个模块使用Python调用Java类：
from time import sleep
from jnius import autoclass
Hardware = autoclass('org.renpy.android.Hardware')
print('DPI is', Hardware.getDPI())
Hardware.accelerometerEnable(True)
for x in xrange(20):
print(Hardware.accelerometerReading())
sleep(.1)
# 推荐写法，代码耗时：0.33秒 class DemoClass: def __init__(self, value: int): self.value = value # 避免不必要的属性访问器
def main(): size = 1000000 for i in range(size): demo_instance = DemoClass(size) value = demo_instance.value demo_instance.value = i
main()
4. 避免数据复制
4.1 避免无意义的数据复制
# 不推荐写法，代码耗时：6.5秒 def main(): size = 10000 for _ in range(size): value = range(size) value_list = [x for x in value] square_list = [x * x for x in value_list]
main()
上面的代码中value_list完全没有必要，这会创建不必要的数据结构或复制。
# 推荐写法，代码耗时：4.8秒 def main(): size = 10000 for _ in range(size): value = range(size) square_list = [x * x for x in value] # 避免无意义的复制
def main(): string_list = list(string.ascii_letters * 100) for _ in range(10000): result = concatString(string_list)
main()
5. 利用if条件的短路特性
# 不推荐写法，代码耗时：0.05秒 from typing import List
def concatString(string_list: List[str]) -> str: abbreviations = {'cf.', 'e.g.', 'ex.', 'etc.', 'flg.', 'i.e.', 'Mr.', 'vs.'} abbr_count = 0 result = '' for str_i in string_list: if str_i in abbreviations: result += str_i return result
def main(): for _ in range(10000): string_list = ['Mr.', 'Hat', 'is', 'Chasing', 'the', 'black', 'cat', '.'] result = concatString(string_list)
main()
if 条件的短路特性是指对if a and b这样的语句， 当a为False时将直接返回，不再计算b；对于if a or b这样的语句，当a为True时将直接返回，不再计算b。因此， 为了节约运行时间，对于or语句，应该将值为True可能性比较高的变量写在or前，而and应该推后。
# 推荐写法，代码耗时：0.03秒 from typing import List
def concatString(string_list: List[str]) -> str: abbreviations = {'cf.', 'e.g.', 'ex.', 'etc.', 'flg.', 'i.e.', 'Mr.', 'vs.'} abbr_count = 0 result = '' for str_i in string_list: if str_i[-1] == '.' and str_i in abbreviations: # 利用 if 条件的短路特性 result += str_i return result
def main(): for _ in range(10000): string_list = ['Mr.', 'Hat', 'is', 'Chasing', 'the', 'black', 'cat', '.'] result = concatString(string_list)
main()
6. 循环优化
6.1 用for循环代替while循环
# 不推荐写法。代码耗时：6.7秒 def computeSum(size: int) -> int: sum_ = 0 i = 0 while i < size: sum_ += i i += 1 return sum_
def main(): size = 10000 for _ in range(size): sum_ = computeSum(size)
main()
Python 的for循环比while循环快不少。
# 推荐写法。代码耗时：4.3秒 def computeSum(size: int) -> int: sum_ = 0 for i in range(size): # for 循环代替 while 循环 sum_ += i return sum_
def main(): size = 10000 for _ in range(size): sum_ = computeSum(size)
main()
6.2 使用隐式for循环代替显式for循环
针对上面的例子，更进一步可以用隐式for循环来替代显式for循环
# 推荐写法。代码耗时：1.7秒 def computeSum(size: int) -> int: return sum(range(size)) # 隐式 for 循环代替显式 for 循环
def main(): size = 10000 for _ in range(size): sum = computeSum(size)
main()
6.3 减少内层for循环的计算
# 不推荐写法。代码耗时：12.8秒 import math
def main(): size = 10000 sqrt = math.sqrt for x in range(size): for y in range(size): z = sqrt(x) + sqrt(y)
main()
上面的代码中sqrt(x)位于内侧for循环， 每次训练过程中都会重新计算一次，增加了时间开销。
# 推荐写法。代码耗时：7.0秒 import math
def main(): size = 10000 sqrt = math.sqrt for x in range(size): sqrt_x = sqrt(x) # 减少内层 for 循环的计算 for y in range(size): z = sqrt_x + sqrt(y)
