Utilize appropriate data structures: Choosing the right data structure can reduce code complexity and improve execution efficiency, such as using dictionaries instead of lists for indexing operations, and using sets for removing duplicates.
Avoid using loops: Try to avoid using nested loops as much as possible, you can use list comprehensions, map, and other functions instead of loop operations.
By utilizing built-in functions and libraries, Python offers numerous ways to enhance code efficiency, such as using sorted() instead of manual sorting and using zip() for list merging operations.
Cache calculation results: Caching results for repeated calculations can help avoid redundant computations and improve efficiency.
Using a generator: Generators can save memory space and increase efficiency when iterating through large amounts of data.
Avoid frequent IO operations: IO operations are often one of the reasons for inefficient program performance, so efficiency can be improved by using methods such as batching file and database reads and writes.
Utilizing parallel processing: Python offers multi-threading, multiprocessing, and other parallel processing methods, which can speed up the execution of programs.
Optimize the code: Identify bottlenecks in the code through code analysis and performance testing, and optimize them to improve efficiency.
