Python programming skills to improve work efficiency

SXXpenguin 2020-11-12 16:11:09
python programming skills improve work


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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