Hello there @apksha.shukla! What you asked is a brilliant question and a common point of confusion. Thanks for bringing it up; it really helps clarify things for everyone!
I ran into this when cleaning up some time series data. np.max and np.amax are pretty much the same, np.max is just a shorthand alias. They both return a single maximum value or max along an axis.
But np.maximum was a game-changer for me. It’s for element-wise comparisons across two arrays. I use it when I want to compute something like np.maximum(arr1, arr2) to compare daily metrics between two models. It’s a completely different use case from aggregating a single array!
It’s fantastic how NumPy offers such specific tools for distinct computational patterns. Hopefully, this real-world application helps illustrate the difference.
Best regards and happy coding! 