Common bottlenecks include inefficient algorithms, excessive memory usage, and I/O operations. Profile your code to identify slow sections and optimize algorithms and data structures.

Identifying performance bottlenecks in Python applications is crucial for optimizing efficiency and responsiveness. Common bottlenecks often arise from inefficient algorithms that lead to excessive time complexity, such as using nested loops for operations that could be streamlined with better algorithms or data structures. Memory usage can also be a significant factor; large data structures can consume substantial memory, leading to slower performance. Profiling your code is essential to pinpoint these bottlenecks; tools like cProfile, line_profiler, and memory_profiler can provide insights into function call times and memory usage. Once identified, consider optimizing algorithms, such as switching from O(n²) to O(n log n) where possible. Additionally, be mindful of I/O operations, as reading and writing data can significantly slow down applications; batch processing or asynchronous I/O can help alleviate these issues. By understanding and addressing these common bottlenecks, you can enhance the performance of your Python applications.