1.isinstance function: In addition to taking a type as a parameter, it can also take a type tuple as a parameter.
isinstance(obj,basestring)===isinstance(obj,(str,unicode))
2.getattr function: You can give a default value to avoid triggering errors.
writte=getattr(obj,'write',sys.stdout.write)
3.type function: You can get the type of an object or create a new type directly from it:
>>> Point=type('Point',(object,),{'x':0,'y':0}) >>> p=Point() >>> p.x,p.y (0, 0) >>> p=Point(3,8) Traceback (most recent call last): File "<pyshell#55>", line 1, in <module> p=Point(3,8) TypeError: object() takes no parameters >>> pprint.pprint(dir(Point)) ['__class__', '__delattr__', '__dict__', '__doc__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'x', 'y'] >>> p.name='source point' >>> p.name 'source point' >>> pprint.pprint(dir(p)) ['__class__', '__delattr__', '__dict__', '__doc__', '__format__', '__getattribute__', '__hash__', '__init__', '__module__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'name', 'x', 'y'] >>> def tostr(self): return '(%s,%s)'%(self.x,self.y) >>> Point.__str__=tostr >>> print p (0,0) >>> def init(self,x,y): self.x,self.y=x,y >>> Point.__init__=init >>> p2=Point(6,8) >>> print p2 (6,8) >>>
4.issubclass(bool,int)==True
5.numbers. Number is the base class of all numerical types
6.type(None)==NoneType, None is a constant
7.In addition to the iter(object) form, the iter function also returns an iterator object (callable, sentinel)
>>> def getrand(): import random return random.randint(1,100) >>> for i in iter(getrand,50):print i,#获取第一次得到50之前的所有1-100的随机数 32 19 82 28 30 41 100 39 71 29 45 30 94 77 62 26 25 19 82 20 55 20 43 73 >>> for i in iter(getrand,50):print i,#获取第一次得到50之前的所有1-100的随机数 22 54 14 25 60 65 16 80 61 5 48 61 2 30 90 98 70 10 55 45 23 72 87 39 70 3 84 85 >>>
8.BaseException is the base class of all exceptions, Exception is just the base class of all exceptions that do not exit
9.locals/globals/vars/dir:
[1]locals/globals is very simple, Is a local/global object dict relative to the current scope;
[2]vars()==locals(), vars(obj)==obj.__dict__
[3] has no parameters, set(dir()) ==set(locals().keys()); if hasattr(obj,'__dir__')=>dir(obj)==obj.__dir__(); Otherwise, if obj is a module object, dir(obj) returns are all attributes of the module; if obj is a class object, dir(obj) returns all the attributes of the class, and then the attributes inherited from the base class; if obj is an instance object, dir(obj) returns the instance object Some attributes, attributes of the class to which it belongs, and attributes inherited from the base class of the class to which it belongs. [Any modification to a class object will be reflected on its instance object; any modification to the base class will also be reflected on the derived class. Of course, except for the case of attribute masking. 】
10.enumerate function: enumerate(obj,[start]), if start is defined, the ordinal will start from start, not from the default zero.
>>> for i,name in enumerate(['C','C++','CSharp','Java','Python'],1): print '%d.%s'%(i,name) 1.C 2.C++ 3.CSharp 4.Java 5.Python >>>

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.

Python is suitable for rapid development and data processing, while C is suitable for high performance and underlying control. 1) Python is easy to use, with concise syntax, and is suitable for data science and web development. 2) C has high performance and accurate control, and is often used in gaming and system programming.

The time required to learn Python varies from person to person, mainly influenced by previous programming experience, learning motivation, learning resources and methods, and learning rhythm. Set realistic learning goals and learn best through practical projects.

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.


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