Find the minimum value for each unique key without using a for loop

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I have a numpy array with keys (e.g. [1, 2, 2, 3, 3, 2]) and an array with values (e.g. [0.2, 0.6, 0.8, 0.4, 0.9, 0.3]). I want to find the minimum value associated with each unique key without using a for loop. In this example, the answer is {1: 0.2, 2: 0.3, 3: 0.4}. I asked ChatGPT and New Bing but they keep giving me the wrong answer. So, is it really possible to do this without a for loop?

Edit 1: What I'm trying to achieve is the fastest speed. Also, in my case, most keys are unique. I considered using np.unique to acquire every key and then compute the min value for every key, but clearly it requires a for loop and a quadratic time. I also considered sorting the arrays by keys and apply np.min on the values of each key, but I doubt its efficiency when most keys are unique. Additionally, according to the comments, pandas.DataFrame has a groupby method which might be helpful, but I'm not sure if it's the fastest (perhaps I'm going to try on my own).

Edit 2: I don't necessarily need a dict as the output; it can be an array of unique keys and an array of min values, and the order of keys doesn't matter.



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