{"url":"https://www.wizard-notes.com/entry/python/matplotlib-twinx-pca-cr","provider_url":"https://hatena.blog","author_name":"Kurene","categories":["Python","matplotlib"],"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fwww.wizard-notes.com%2Fentry%2Fpython%2Fmatplotlib-twinx-pca-cr\" title=\"Matplotlib\u3067\u4e3b\u6210\u5206\u5206\u6790\u306e\u5bc4\u4e0e\u7387\u30fb\u7d2f\u7a4d\u5bc4\u4e0e\u7387\u3092\u5de6\u53f3\uff12\u8ef8\u30d7\u30ed\u30c3\u30c8 - Wizard Notes\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","title":"Matplotlib\u3067\u4e3b\u6210\u5206\u5206\u6790\u306e\u5bc4\u4e0e\u7387\u30fb\u7d2f\u7a4d\u5bc4\u4e0e\u7387\u3092\u5de6\u53f3\uff12\u8ef8\u30d7\u30ed\u30c3\u30c8","type":"rich","published":"2019-11-12 03:06:57","version":"1.0","blog_title":"Wizard Notes","blog_url":"https://www.wizard-notes.com/","height":"190","description":"\u591a\u5909\u91cf\u89e3\u6790\u306e\u4e00\u822c\u7684\u306a\u624b\u6cd5\u3067\u3042\u308b\u4e3b\u6210\u5206\u5206\u6790\u3092\u4f7f\u3063\u3066\u3044\u308b\u3068\u3001\u5bc4\u4e0e\u7387\u3068\u7d2f\u7a4d\u5bc4\u4e0e\u7387\u3092\u78ba\u8a8d\u3059\u308b\u3053\u3068\u304c\u591a\u3044\u3067\u3059\u3002 \u305d\u308c\u305e\u308c\u3092\u5225\u306b\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u3082\u3088\u3044\u306e\u3067\u3059\u304c\u3001 \uff11\u3064\u306e\u30d7\u30ed\u30c3\u30c8\u3067\u78ba\u8a8d\u3067\u304d\u305f\u307b\u3046\u304c\u4fbf\u5229\u3060\u3068\u601d\u3044\u3001matplotlib\u3067\u5b9f\u88c5\u3057\u3066\u307f\u307e\u3057\u305f\u3002 \u4e00\u3064\u306e\u30d7\u30ed\u30c3\u30c8\u3067\u5de6\u53f3\uff12\u3064\u306e\u8ef8\u3092\u63cf\u753b\u3059\u308b\u306b\u306f\u3001matplotlib\u3067\u306fax.twinx()\u3092\u3044\u3046\u95a2\u6570\u3092\u4f7f\u3046\u3053\u3068\u3067\u5b9f\u73fe\u3067\u304d\u307e\u3059\u3002 \u30d7\u30ed\u30c3\u30c8\u4f8b \u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9 import numpy as np import matplotlib.pyplot as plt n_sample = 1000 n_dim = 30 mean = np.random.normal(0.0,\u2026","width":"100%","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/K/Kurene/20191112/20191112025841.png","author_url":"https://blog.hatena.ne.jp/Kurene/","provider_name":"Hatena Blog"}