This article is made up entirely of Python coding best practices. It explores the tips and don ‘ts of writing high-quality Python code from eight aspects, including basic principles, idioms, syntax, libraries, design patterns, internals, development tools, and performance optimization. A total of 91 valuable suggestions are summarized. Each suggestion corresponds to a problem that a Python programmer might encounter.
Tip 1: Understand Pythonic concepts
Tip 2: Write Pythonic code
Tip 3: Understand the differences between Python and C
Tip 4: Add comments to your code as appropriate
Tip 5: Make your layout more elegant by adding empty lines
Tip 6: Four principles for writing functions
Tip 7: Gather constants in a file
Tip 8: Use assert statements to find problems
Recommendation 9: Do not use intermediate variables when exchanging values
Recommendation 10: Take advantage of Lazy Evaluation
Recommendation 11: Understand the pitfalls of enumeration alternative implementations
Recommendation 12: It is not recommended to use type for type checking
Tip 13: Try converting to float before doing division
Tip 14: Beware of eval() security holes
Suggestion 15: Use enumerate() to obtain the index and value of a sequence iteration
Recommendation 16: Distinguish between = and IS
Recommendation 17: Consider compatibility and use Unicode whenever possible
Recommendation 18: Build a reasonable package hierarchy to manage modules
Tip # 19: Use from… sparingly. The import statement
Recommendation 20: Use absolute Import to import modules first
Recommendation 21: I +=1 does not equal ++ I
Suggestion 22: Use with to automatically shut down resources
Tip 23: Use the else clause to simplify loops (exception handling)
Recommendation 24: Follow some basic exception handling principles
Tip 25: Avoid pitfalls that can occur in finally
Tip 26: Understand None and check if the object is empty
Recommendation 27: Join strings should be preferred over +
Recommendation 28: Use. Format instead of % when formatting strings
Suggestion 29: Treat mutable objects differently from immutable objects
Recommendation 30: [], (), and {} : consistent container initialization forms
Tip 31: Remember that function parameters are neither values nor references
Tip 32: Be aware of potential problems with default parameters
Recommendation 33: Use variable-length parameters with caution
Recommendation 34: Understand the difference between STR () and repr()
Tip 35: Distinguish between staticMethod and classMethod scenarios
Recommendation 36: Master the basic usage of strings
Tip 37: Select sort() or sorted() as needed
Suggestion 38: Use the copy module to deeply copy objects
Recommendation 39: Use Counter for counting statistics
Recommendation 40: Go deep with ConfigParser
Recommendation 41: Use argparse to handle command line arguments
Recommendation 42: Use pandas to process large CSV files
Recommendation 43: Use ElementTree to parse XML in general
Recommendation 44: Understand the pros and cons of module pickling
Tip 45: Another good option for serialization – JSON
Recommendation 46: Use traceback to obtain stack information
Recommendation 47: Use logging to record log information
Tip 48: Write multithreaded programs using the threading module
Tip 49: Use Queue to make multithreaded programming safer
Recommendation 50: Implement the singleton pattern with modules
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Tip 51: Use mixin patterns to make your application more flexible
Recommendation 52: Loose coupling with a publish-subscribe pattern
Tip 53: Beautify your code with state patterns
Recommendation 54: Understand build-in Objects
Recommendation 55: Init () is not a constructor
Recommendation 56: Understand the name lookup mechanism
Recommendation 57: Why do I need the self parameter
Recommendation 58: Understand MRO and multiple inheritance
Tip 59: Understand the descriptor mechanism
Recommendation 60: Distinguish between getattr() and getAttribute () methods
Recommendation 61: Use a more secure property
Recommendation 62: Master metaclass
Recommendation 63: Be familiar with the Python object protocol
Recommendation 64: Use operator overloading for infix syntax
Tip 65: Be familiar with Python’s iterator protocol
Tip 66: Be familiar with Python generators
Recommendation 67: Generator-based coroutines and greenlets
Recommendation 68: Understand the limitations of the GIL
Recommendation 69: Object management and garbage collection
Recommendation 70: Install the package from PyPI
Recommendation 71: Install and manage packages using PIP and technique
Recommendation 72: Do paster to create a package
Recommendation 73: Understand unit testing concepts
Recommendation 74: Write unit tests for packages
Tip 75: Use test-driven development to improve the testability of your code
Tip 76: Use Pylint to check code style
Recommendation 77: Conduct effective code reviews
Recommendation 78: Publish packages to PyPI
Recommendation 79: Understand the basic principles of code optimization
Recommendation 80: Use performance tuning tools
Recommendation 81: Use cProfile to locate performance bottlenecks
Recommendation 82: Use Memory_profiler and Objgrash to profile memory usage
Recommendation 83: Try to reduce algorithm complexity
Recommendation 84: Master the basics of loop optimization
Tip 85: Use generators for efficiency
Recommendation 86: Optimize performance with different data structures
Recommendation 87: Take advantage of set
Recommendation 88: Use Meltiprocessing to overcome GIL defects
Recommendation 89: Use thread pools for efficiency
Recommendation 90: Use C/C++ module extensions to improve performance
Recommendation 91: Write extension modules using Cython
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