Python programmers can optimize their code for better performance by following several key practices. First, they can utilize built-in functions and libraries, such as map(), filter(), and numpy, which are highly optimized and can significantly speed up code execution compared to writing custom loops. Second, programmers should avoid unnecessary operations and aim to reduce the complexity of their algorithms, which can help reduce the time and space complexity of their code. Additionally, using data structures like dictionaries and sets can offer faster lookups compared to lists.

Moreover, Python programmers can benefit from using tools like profiling to identify bottlenecks in their code and prioritize optimizations effectively. They should also consider leveraging concurrency and parallelism using libraries like multiprocessing or asyncio to make better use of multi-core processors. Lastly, staying updated with the latest best practices and techniques through continuous learning, such as enrolling in a Python certification course, can help programmers stay at the forefront of optimization strategies and improve their code's performance.

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