If your Python code is slow with large datasets, consider using data structures optimized for performance, such as NumPy arrays. Profiling your code can help identify bottlenecks, and you might also look into parallel processing options.

Running Python code with large datasets can lead to significant performance issues if not managed effectively. When dealing with large amounts of data, the choice of data structures is crucial; using built-in lists can lead to inefficiencies, especially when performing complex operations. Instead, consider leveraging optimized libraries like NumPy, which provides powerful n-dimensional arrays designed for high performance and efficient memory usage. NumPy's operations are implemented in C, making them considerably faster than native Python loops for numerical computations. Profiling your code using tools like cProfile can help identify bottlenecks—specific sections of code that take the most time to execute. Once identified, focus your optimization efforts on these areas, whether it be through algorithmic improvements or more efficient data handling. Additionally, if your tasks are CPU-bound, explore parallel processing options using libraries like multiprocessing or concurrent.futures to distribute workloads across multiple CPU cores. By implementing these strategies, you can significantly improve the performance of your Python applications when working with large datasets.