Greetings folks! 
I’ve been diving deeper into NumPy’s functions lately, trying to get a clearer picture of their intended uses and performance nuances. It’s always fascinating to uncover the design decisions behind such powerful libraries.
I’ve come across an interesting trio of functions, np.max, np.amax, and np.maximum, and their specific roles have sparked some curiosity.
While np.max and np.amax behave similarly by computing the maximum value over an axis or the entire array, np.maximum performs element-wise comparisons between two arrays. So why not just use np.max for everything? The distinction lies in functionality and performance, np.maximum is designed for broadcasting and pairwise operations, whereas np.max (an alias for np.amax) is optimized for aggregation.
I’m particularly interested in understanding the broader philosophy behind providing these distinct functions, especially given their seemingly overlapping purposes at first glance.
Looking forward to hearing any insights or experiences you might have with these! Happy coding! 
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! 
Hello @apksha.shukla and @prynka.chatterjee and everyone exploring the nuances of NumPy! It’s interesting how a set of seemingly similar functions can actually serve distinct purposes.
I can definitely relate to the initial confusion. At first, I thought NumPy was just being redundant having np.max and np.amax. Turns out np.max is just there for convenience, it actually calls np.amax under the hood.
The truly tricky one is np.maximum, which I initially misunderstood as a fancier version of np.max. But nope! It’s specifically used when you’ve got two arrays and want to compare them pairwise, returning the element-wise maximum.
So yeah, now I completely get why NumPy includes all three, they’re indeed solving different problems, even if their names suggest otherwise at first glance.
Hope this clarification adds to your understanding of NumPy’s design!
POST 2 (12:43)
Hello @apksha.shukla! Your question about the distinctions between np.max, np.amax, and np.maximum is a crucial one, and it really highlights some interesting design choices in NumPy.
Indeed, I think the reason NumPy includes all three is primarily for clarity and performance optimization. When you use np.amax or np.max, you’re essentially summarizing an array to find the single largest value—it’s a reduction operation.
But np.maximum is a completely different beast: it’s vectorized and exceptionally well-suited for broadcasting operations. For instance, I’ve used np.maximum extensively in image processing, where I needed to merge two arrays pixel-wise (e.g., finding the brighter pixel at each corresponding position). Trying to achieve that with np.max wouldn’t even make sense in that context.
So, it’s genuinely less about redundancy and far more about having precisely the right tool for the job. Each function serves a distinct, powerful purpose within array manipulation.
Hope this deeper dive into their specific use cases clarifies things! Happy coding!