Optimizing Python code depends on application requirements and processing speed, with trade-offs often made between code scalability, code performance, debugging, and time taken to write the code. There are some ways to optimize your Python code:
1. Use Built-in functions and libraries: Python provides many built-in functions and libraries. These functions are written in a way that they provide optimum speed.
1. Use Local Variables: When performing calculations or doing operations, use local variables wherever possible, these are loaded faster than global variables.
1. Use “in” if you want to check the existence of a variable: Python’s “in” can look up items very fast, even in large lists.
1. Use “while 1” for an endless loop.
1. Avoid Dot Notation: Each dot-notation requires an attribute look-up, which costs much more than a local variable look-up.
1. Use List Comprehension & Generator Expressions: List comprehensions are significantly faster than for loops and can simplify syntax and improve readability.
1. Choose Appropriate Data Structure: Implementing the right data structure can greatly optimize your code. Consistently using lists when a dictionary or set would be faster can lead to problems.
1. Use of Python’s built-in decorators: Python’s built-in decorators help in time estimation of functions.
1. Use of Pandas module for large data sets: If you are dealing with large data sets use a pandas data frame. This module is optimized for performance.
1. Profiling Python Code: Python’s standard library comes with two profiling modules, cProfile, and profile, they can be used to find bottlenecks in your application.
But, the simple rule as stated by Donald Knuth is that “Premature optimization is the root of all evils”, as it can lead to more harm than good. So, optimize only those portions of code that need optimization.
