For example, using a binary search algorithm instead of a linear search algorithm can improve performance
Use built-in functions and libraries
Python has many built-in functions and libraries that are optimized for performance. Instead of implementing a custom function, it may be faster to use a built-in one.
Avoid global variables
Global variables can slow down performance, especially in larger programs. It is better to use local variables or class attributes instead.
Optimize loops
Avoid nested loops where possible, as they can be slow. Also, try to use iterators instead of lists when iterating over large collections of data
Use comprehensions
Comprehensions are a concise way to create lists, sets, and dictionaries in Python. They are also faster than using loops in some cases.
Use list/dict comprehension
For operations involving list/dict manipulation like filtering, mapping, and aggregating, comprehensions are a faster option than using loops
Avoid unnecessary object creation
Creating objects in Python can be expensive. It is better to reuse existing objects where possible, especially in loops.
Use generators
If you’re processing large amounts of data, generators are a good way to avoid having to store all the data in memory at once.
Use NumPy and pandas
NumPy and pandas are two powerful libraries that are optimized for numerical and data manipulation operations in Python.
Use profiling tools
Profiling tools can help you identify which parts of your code are slowing down performance, so you can focus on optimizing those sections.