1. Set basic data type
a. Set collection is an unordered and non-repeating collection of elements
class set(object): """ set() -> new empty set object set(iterable) -> new set object Build an unordered collection of unique elements. """ def add(self, *args, **kwargs): # real signature unknown """ Add an element to a set,添加元素 This has no effect if the element is already present. """ pass def clear(self, *args, **kwargs): # real signature unknown """ Remove all elements from this set. 清楚内容""" pass def copy(self, *args, **kwargs): # real signature unknown """ Return a shallow copy of a set. 浅拷贝 """ pass def difference(self, *args, **kwargs): # real signature unknown """ Return the difference of two or more sets as a new set. A中存在,B中不存在 (i.e. all elements that are in this set but not the others.) """ pass def difference_update(self, *args, **kwargs): # real signature unknown """ Remove all elements of another set from this set. 从当前集合中删除和B中相同的元素""" pass def discard(self, *args, **kwargs): # real signature unknown """ Remove an element from a set if it is a member. If the element is not a member, do nothing. 移除指定元素,不存在不保错 """ pass def intersection(self, *args, **kwargs): # real signature unknown """ Return the intersection of two sets as a new set. 交集 (i.e. all elements that are in both sets.) """ pass def intersection_update(self, *args, **kwargs): # real signature unknown """ Update a set with the intersection of itself and another. 取交集并更更新到A中 """ pass def isdisjoint(self, *args, **kwargs): # real signature unknown """ Return True if two sets have a null intersection. 如果没有交集,返回True,否则返回False""" pass def issubset(self, *args, **kwargs): # real signature unknown """ Report whether another set contains this set. 是否是子序列""" pass def issuperset(self, *args, **kwargs): # real signature unknown """ Report whether this set contains another set. 是否是父序列""" pass def pop(self, *args, **kwargs): # real signature unknown """ Remove and return an arbitrary set element. Raises KeyError if the set is empty. 移除元素 """ pass def remove(self, *args, **kwargs): # real signature unknown """ Remove an element from a set; it must be a member. If the element is not a member, raise a KeyError. 移除指定元素,不存在保错 """ pass def symmetric_difference(self, *args, **kwargs): # real signature unknown """ Return the symmetric difference of two sets as a new set. 对称交集 (i.e. all elements that are in exactly one of the sets.) """ pass def symmetric_difference_update(self, *args, **kwargs): # real signature unknown """ Update a set with the symmetric difference of itself and another. 对称交集,并更新到a中 """ pass def union(self, *args, **kwargs): # real signature unknown """ Return the union of sets as a new set. 并集 (i.e. all elements that are in either set.) """ pass def update(self, *args, **kwargs): # real signature unknown """ Update a set with the union of itself and others. 更新 """ pass
b. Example of data type module
se = {11,22,33,44,55} be = {44,55,66,77,88} # se.add(66) # print(se) #添加元素,不能直接打印! # # # # se.clear() # print(se) #清除se集合里面所有的值,不能清除单个 # # # # ce=be.difference(se) #se中存在,be中不存在的值,必须赋值给一个新的变量 # print(ce) # # # se.difference_update(be) # print(se) #在se中删除和be相同的值,不能赋值给一个新的变量,先输入转换,然后打印,也不能直接打印! # se.discard(11) # print(se) #移除指定元素,移除不存在的时候,不会报错 # se.remove(11) # print(se) #移除指定的元素,移除不存在的会报错 # se.pop() # print(se) #移除随机的元素 # # # ret=se.pop() # print(ret) #移除元素,并且可以把移除的元素赋值给另一个变量 # ce = se.intersection(be) # print(ce) #取出两个集合的交集(相同的元素) # se.intersection_update(be) # print(se) #取出两个集合的交集,并更新到se集合中 # ret = se.isdisjoint(be) # print(ret) #判断两个集合之间又没有交集,如果有交集返回False,没有返回True # ret=se.issubset(be) # print(ret) #判断se是否是be集合的子序列,如果是返回True,不是返回Flase # ret = se.issuperset(be) # print(ret) #判断se是不是be集合的父序列,如果是返回True,不是返回Flase # ret=se.symmetric_difference(be) # print(ret) #对称交集,取出除了不相同的元素 # se.symmetric_difference_update(be) # print(se) #对称交集,取出不相同的元素并更新到se集合中 # ret = se.union(be) # print(ret) #并集,把两个元素集合并在一个新的变量中
##2. Dark and shallow copy
import copy # ######### 数字、字符串 ######### n1 = 123 # n1 = "i am alex age 10" print(id(n1)) # ## 赋值 ## n2 = n1 print(id(n2)) # ## 浅拷贝 ## n2 = copy.copy(n1) print(id(n2)) # ## 深拷贝 ## n3 = copy.deepcopy(n1) print(id(n3))
n1 = {"k1": "zhangyanlin", "k2": 123, "k3": ["Aylin", 456]} n2 = n1
import copy n1 = {"k1": "zhangyanlin", "k2": 123, "k3": ["aylin", 456]} n3 = copy.copy(n1)3. Deep copyDeep copy, recreate all the data in the memory (excluding the last layer, that is: Python's internal optimization of strings and numbers)
3. Function
Object-oriented: Classify and encapsulate functions to make development "faster, better and stronger...
Function name: The name of the function. The function will be called based on the function name in the future
Function body: A series of logical calculations are performed in the function, such as: sending emails, calculating [11,22,38,888, 2], etc...
Parameters: Provide data for the function body
Return value: When the function is executed, data can be returned to the caller.
def 发送短信(): 发送短信的代码... if 发送成功: return True else: return False while True: # 每次执行发送短信函数,都会将返回值自动赋值给result # 之后,可以根据result来写日志,或重发等操作 result = 发送短信() if result == False: 短信发送失败...The function has three different parameters: Normal parameters
# Define function
# name is called the formal parameter of function func, abbreviated as: formal parameter
def func(name):
# Execute function
# 'zhangyanlin' is called the actual parameter of function func, abbreviated as: actual parameter
func('zhangyanlin')
def func(name, age = 18):
print "%s:%s" %(name, age)
# Specify Parameters
func('zhangyanlin', 19)# Use default parameters
func('nick')Note: The default parameters need to be placed at the end of the parameter list
Dynamic parametersdef func(*args): print args # 执行方式一 func(11,33,4,4454,5) # 执行方式二 li = [11,2,2,3,3,4,54] func(*li)
def func(**kwargs): print args # 执行方式一 func(name='wupeiqi',age=18) # 执行方式二 li = {'name':'wupeiqi', age:18, 'gender':'male'} func(**li)
def func(*args, **kwargs): print args print kwargs
def email(p,j,k): import smtplib from email.mime.text import MIMEText from email.utils import formataddr set = True try: msg = MIMEText('j', 'plain', 'utf-8') #j 邮件内容 msg['From'] = formataddr(["武沛齐",'wptawy@126.com']) msg['To'] = formataddr(["走人",'424662508@qq.com']) msg['Subject'] = "k" #k主题 server = smtplib.SMTP("smtp.126.com", 25) server.login("wptawy@126.com", "WW.3945.59") server.sendmail('wptawy@126.com', [p], msg.as_string()) server.quit() except: set = False return True formmail = input("请你输入收件人邮箱:") zhuti = input("请您输入邮件主题:") neirong = input("请您输入邮件内容:") aa=email(formmail,neirong,zhuti) if aa: print("邮件发送成功!") else: print("邮件发送失败!")
####################More Python common knowledge points related Please pay attention to the PHP Chinese website for articles! ############

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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