Struggling with optimal array slicing techniques in NumPy for large datasets
I'm updating my dependencies and I'm stuck on something that should probably be simple. After trying multiple solutions online, I still can't figure this out. During code review, I noticed that our current implementation for processing large datasets in NumPy could be more efficient, especially in terms of array slicing. We are utilizing a significant number of temporary arrays in our operations, which I suspect may be impacting performance. To illustrate, here’s a snippet from the existing code: ```python import numpy as np def process_data(data): result = [] for i in range(data.shape[0]): temp = data[i, 0:5] # Slicing the first 5 columns result.append(np.sum(temp)) return np.array(result) ``` This approach slices the array inside a loop, creating temporary arrays each iteration. While it works, I believe we can improve it by avoiding the loop altogether and using NumPy’s vectorized operations. I tried the following alternative: ```python def optimized_process_data(data): return np.sum(data[:, 0:5], axis=1) ``` This version seems cleaner and should, theoretically, perform better due to NumPy’s internal optimizations. However, I still find the execution time is not significantly reduced, and I’m curious if there’s a better way to handle slicing, especially when scaling with larger datasets. Additionally, I’ve read about using `np.ix_()` for advanced indexing. Would that approach provide any performance benefits in this context? What other best practices should I consider for slicing large NumPy arrays efficiently? Any insights or recommendations on alternative methods, including potential pitfalls, would be greatly appreciated. For context: I'm using Python on Windows. This is part of a larger API I'm building. Is there a better approach? For context: I'm using Python on Ubuntu 20.04. I'd love to hear your thoughts on this.