To optimize Python code for performance, use built-in functions, avoid global variables, and minimize repetitive computations. Profiling your code with tools like cProfile can also help identify bottlenecks for targeted improvements.

Optimizing Python code for performance is crucial for building efficient applications, especially when dealing with large datasets or computationally intensive tasks. One of the first steps in optimization is to utilize Python's built-in functions and libraries, as they are often implemented in C and optimized for performance. For example, list comprehensions are generally faster than using traditional loops for constructing lists. Avoid using global variables, as they can slow down access times; instead, use function arguments and return values for better performance. Additionally, be mindful of repetitive computations; if a calculation can be stored or reused, consider caching the results to avoid unnecessary recalculations. Profiling your code is essential for identifying bottlenecks; use tools like cProfile or timeit to analyze which parts of your code consume the most time and focus your optimization efforts there. Consider using libraries such as NumPy or Pandas for data manipulation, as they provide optimized performance for array-based computations. Lastly, always remember that optimization should not come at the cost of code readability; aim for a balance between performance and maintainability. By implementing these strategies, you can enhance the performance of your Python applications effectively.