Python Programming skills improve work efficiency , In the process of learning and working, mastering some small skills can greatly improve the efficiency of work , Next, I will introduce programming idioms 、 Basic usage 、 Library usage 、 Internal mechanism 、 Using tools to assist project development 、 Performance analysis and optimization programming skills .
One 、Python Program introduction
1、 understand Pythonic Concept , See Python Medium 《Python zen 》
2、 To write Pythonic Code
(1) Avoid irregular code , For example, only case sensitive variables 、 Use confusing variable names 、 Fear of too long variable names, etc . Sometimes a long variable name makes the code more readable .
(2) Learn more Python Related knowledge , For example, language features 、 Library characteristics, etc , such as Python Evolution process, etc . Learn one or two industry recognized Pythonic Code base for , such as Flask etc. .
3： understand Python And C The difference , Like indenting and {}, Single quotation mark and double quotation mark , ternary operator ?, Switch-Case Statement etc. .
4： Add comments to your code as appropriate
5： Adding empty exercise code layout is more reasonable
6： Write the 4 Principles
(1) Function design should be as short as possible , Nesting level should not be too deep
(2) Function declaration should be reasonable 、 Simple 、 Easy to use
(3) Function parameter design should consider downward compatibility
(4) A function does only one thing , Try to ensure the consistency of function granularity
7： Centralize constants in one file , And try to use all capital letters for constant names
Two 、 Programming idioms
8： utilize assert Statements to find problems , But should pay attention to , Assertion assert Will affect efficiency
9： Temporary variables are not recommended for data exchange values , Instead of directly a, b = b, a
10： Make full use of inert computing (Lazy evaluation) Characteristics of , So as to avoid unnecessary calculation
11： Understand the pitfalls of enumerating alternative implementations ( The latest version Python Enumeration feature has been added to )
12： It is not recommended to use type To do type checking , Because sometimes type The results are not necessarily reliable . If there is a need , Use isinstance Instead of a function
13： Try to convert the variable to floating-point type before division (Python3 Don't worry about it later )
14： alert eval() Security vulnerability of function , It's kind of like SQL Inject
15： Use enumerate() Get index and value of sequence iteration at the same time
16： Distinguish between == and is The applicable scenarios of , Especially when comparing immutable variables such as strings ( See comments for details )
17： Use as much as possible Unicode. stay Python2 Middle coding is a headache , but Python3 You don't have to think about it
18： Build a reasonable package hierarchy to manage Module
3、 ... and 、 Basic usage
19： Moderate use from…import sentence , Prevent namespace pollution
20： priority of use absolute import To import modules (Python3 Has been removed from relative import)
21：i+=1 It's not equal to ++i, stay Python in ,++i The plus sign in front only indicates positive , No operation
22： Habitual use with Close resources automatically , Especially in document reading and writing
23： Use else Clause simplify loop ( exception handling )
24： Following the basic principles of exception handling
(1) Pay attention to the abnormal granularity ,try Write as little code as possible in the block
(2) Use individual carefully except sentence , or except Exception sentence , But to specific exceptions
(3) Pay attention to the order of exception capture , Handle exceptions at the right level
(4) Use more friendly exception information , Follow the specification of abnormal parameters
25： avoid finally Potential pitfalls in
26： In depth understanding of None, Correctly judge whether the object is empty .
27： Connection strings should take precedence join function , instead of + operation
28： Format strings as much as possible format function , instead of % form
29： Differentiate between mutable and immutable objects , Especially as a function parameter
30：[], {} and ()： Consistent container initialization . Using list parsing makes the code clearer , More efficient at the same time
31： Function parameters , Neither value nor reference , It's a reference to a passing object
32： Be alert to potential problems of default parameters , Especially when the default parameter is mutable
33： Careful use of variable length parameter in function args and kargs
(1) It's too flexible to use , So that the function signature is not clear enough , Poor readability
(2) If we use variable length parameters to simplify the function definition because of too many function parameters , In general, the function can be reconstructed
34： In depth understanding of str() and repr() The difference between
(1) Different goals between the two ：str Mainly for customers , Its purpose is readability , The return form is a string form with high user friendliness and readability ; and repr It's for Python Interpreter or Python Developer , Its purpose is accuracy , Its return value represents Python Definition inside the interpreter
(2) Entering variables directly in the interpreter , Default call repr function , and print(var) Default call str function
(3)repr The return value of a function can be eval Function to restore an object
(4) Both call the built-in function of the object __str__ () and __repr__ ()
35： Distinguish static method staticmethod And class methods classmethod Usage scenarios of
Four 、 Library usage
36： Master the basic usage of string
37： Select on demand sort() and sorted() function
sort() Is the list sorted in place , So you can't sort immutable types like tuples .
sorted() Can sort any type of iteration , Without changing the original variable itself .
38： Use copy Module deep copy object , Distinguish shallow copy (shallow copy) And deep copy (deep copy)
39： Use Counter Count statistics ,Counter Is a subclass of the dictionary class , stay collections Module
40： In depth ConfigParse
41： Use argparse Module processing command line parameters
42： Use pandas Handling large CSV file
Python Provide a CSV File processing module , And provide reader、writer Such as function .
Pandas Available in blocks 、 Consolidation, etc , Suitable for large data volume , And it is more convenient for two-dimensional data operation .
43： Use ElementTree analysis XML
44： Understanding module pickle The advantages and disadvantages of
advantage ： The interface is simple 、 Common to all platforms 、 Wide range of data types supported 、 Extensibility is strong
Inferiority ： Atomicity of data operation is not guaranteed 、 There are security issues 、 Incompatibility between different languages
45： Another option for serialization JSON modular ：load and dump operation
46： Use traceback Get stack information
47： Use logging Logging information
48： Use threading Module programming multithreaded program
49： Use Queue Module makes multithreaded programming safer
5、 ... and 、 Design patterns
50： Using module to realize single instance mode
51： use mixin Patterns make programs more flexible
52： Publish with - Subscription mode for loose coupling
53： Beautify code with state mode
6、 ... and 、 Internal mechanism
54： understand build-in object
55：__init__ () Not a construction method , understand __new__ () The difference with it
56： Understand the search mechanism of variables , Scope
Local scope
Global scope
Nested scope
Built-in scope
57： Why self Parameters
58： understand MRO( Method parsing order ) And multiple inheritance
59： Understanding the descriptor mechanism
60： difference __getattr__ () And __getattribute__ () Differences between methods
61： Use safer property
62： Master metaclass metaclass
63： be familiar with Python Object protocol
64： Using operator overloading to implement infix Syntax
65： be familiar with Python Iterator protocol
66： be familiar with Python The generator
67： Generator based coroutine sum greenlet, Understanding the process 、 Multithreading 、 Differences between multiprocesses
68： understand GIL The limitations of
69： Object management and garbage collection
7、 ... and 、 Using tools to assist project development
70： from PyPI Install third party package
71： Use pip and yolk install 、 Management package
72： do paster Create a package
73： Understand the concept of unit testing
74： Writing unit tests for packages
75： Using test driven development (TDD) Improve code testability
76： Use Pylint Check code style
Code style review
Code error checking
Duplicate and unreasonable code found , Easy refactoring
Highly configurable and customizable
Support a wide variety of IDE Integration with editor
Can be based on Python Code generation UML chart
Can and Jenkins And other continuous integration tools , Support automatic code review
77： Conduct efficient code review
78： Publish package to PyPI
8、 ... and 、 Performance analysis and optimization
79： Understand the basic principles of code optimization
80： With performance optimization tools
81： utilize cProfile Locate performance bottlenecks
82： Use memory_profiler and objgraph Analyze memory usage
83： Try to reduce the complexity of algorithm
84： Master the basic skills of cycle optimization
Reduce the calculation inside the cycle
Change explicit loop to implicit loop , Of course, this will sacrifice the readability of the code
Try to reference local variables in the loop
Focus on inner nested loops
85： Using generators to improve efficiency
86： Use different data structures to optimize performance
87： make the best of set The advantages of
88： Use multiprocessing Module overcome GIL defects
89： Use thread pool to improve efficiency
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