{"image_url":null,"url":"https://www.shigemk2.com/entry/2018/08/14/181330","author_name":"shigemk2","version":"1.0","description":"argmax\u3067\u3001\u6700\u5927\u5024\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u3068\u308b >>> import numpy as np >>> a = np.array([1,2,3,4,5]) >>> np.argmax(a) 4 \u6700\u5927\u5024\u306a\u5024\u304c\u8907\u6570\u3042\u3063\u305f\u3089\u3001\u6700\u521d\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u3068\u308b >>> b = np.array([3,3,1,2,0]) >>> np.argmax(b) 0 numpy.argmax \u2014 NumPy v1.15 Manual","width":"100%","published":"2018-08-14 18:13:30","blog_url":"https://www.shigemk2.com/","title":"numpy argmax","height":"190","blog_title":"by shigemk2","type":"rich","provider_url":"https://hatena.blog","provider_name":"Hatena Blog","author_url":"https://blog.hatena.ne.jp/shigemk2/","categories":["Python"],"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fwww.shigemk2.com%2Fentry%2F2018%2F08%2F14%2F181330\" title=\"numpy argmax - by shigemk2\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>"}