Yoda Response: Certainly, Avi. If a Python script is running slower than expected, I would take the following steps to diagnose and resolve the performance issues:

1. **Profiling**: Use profiling tools like cProfile or line_profiler to identify the parts of the code that are taking the most time. This helps in pinpointing the exact functions or lines that are causing the slowdown.

2. **Unnecessary Function Calls**: Review the code for any unnecessary or redundant function calls that might be adding to the execution time.

3. **Efficient Libraries**: Ensure that the libraries being used are optimized for performance. For example, using NumPy for numerical operations can be significantly faster than using pure Python loops.

4. **Network Interactions**: If the script involves network interactions, check for excessive or inefficient API calls. Batch requests where possible and reduce the number of network interactions.

5. **Data Structures**: Optimize data structures. For example, using sets or dictionaries for lookups instead of lists can improve performance.

6. **Garbage Collection**: Monitor the garbage collection process. Sometimes, manual garbage collection using the `gc` module can help in managing memory more efficiently.

7. **Concurrency**: If applicable, use concurrency techniques like multiprocessing or threading to parallelize tasks and make better use of CPU resources.

8. **Algorithm Optimization**: Review and optimize the algorithms being used. Sometimes a more efficient algorithm can drastically reduce execution time.

For instance, during my project on the ML Preprocessor CLI tool, I encountered performance issues with large datasets. By profiling the code, I identified bottlenecks in data loading and processing. I optimized these by using more efficient data structures and parallel processing, which significantly improved the script's performance.