{"id":1821,"date":"2023-07-24T20:30:21","date_gmt":"2023-07-24T20:30:21","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%81%ae%e6%a8%99%e6%ba%96%e5%8c%96python\/"},"modified":"2023-07-24T20:30:21","modified_gmt":"2023-07-24T20:30:21","slug":"%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%81%ae%e6%a8%99%e6%ba%96%e5%8c%96python","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%81%ae%e6%a8%99%e6%ba%96%e5%8c%96python\/","title":{"rendered":"Python \u3067\u30c7\u30fc\u30bf\u3092\u6a19\u6e96\u5316\u3059\u308b\u65b9\u6cd5: \u4f8b\u4ed8\u304d"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e<strong>\u6a19\u6e96\u5316\u3068\u306f\u3001<\/strong>\u5e73\u5747\u5024\u304c 0\u3001\u6a19\u6e96\u504f\u5dee\u304c 1 \u306b\u306a\u308b\u3088\u3046\u306b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u3059\u3079\u3066\u306e\u5024\u3092\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3059\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u5f0f\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u5024\u3092\u6b63\u898f\u5316\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>x<sub>\u65b0\u3057\u3044<\/sub>= (x <sub>i<\/sub> \u2013 <span style=\"text-decoration: overline;\">x<\/span> ) \/ s<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>x <sub>i<\/sub><\/strong> : \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e<sup>i \u756a\u76ee\u306e<\/sup>\u5024<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong><span style=\"text-decoration: overline;\">x<\/span><\/strong> : \u30b5\u30f3\u30d7\u30eb\u306e\u610f\u5473<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>s<\/strong> : \u30b5\u30f3\u30d7\u30eb\u306e\u6a19\u6e96\u504f\u5dee<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001Python \u306e pandas DataFrame \u5185\u306e\u3059\u3079\u3066\u306e\u5217\u3092\u3059\u3070\u3084\u304f\u6b63\u898f\u5316\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>(df- <span style=\"color: #3366ff;\">df.mean<\/span> ())\/df. <span style=\"color: #3366ff;\">std<\/span> ()\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u69cb\u6587\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 1: \u3059\u3079\u3066\u306e DataFrame \u5217\u3092\u6a19\u6e96\u5316\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001pandas DataFrame \u5185\u306e\u3059\u3079\u3066\u306e\u5217\u3092\u6a19\u6e96\u5316\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <b><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#create data frame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">y<\/span> ': [8, 12, 15, 14, 19, 23, 25, 29],\n                   ' <span style=\"color: #ff0000;\">x1<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' <span style=\"color: #ff0000;\">x2<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12],\n                   ' <span style=\"color: #ff0000;\">x3<\/span> ': [2, 2, 3, 2, 5, 5, 7, 9]})\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>df\n\n\ty x1 x2 x3\n0 8 5 11 2\n1 12 7 8 2\n2 15 7 10 3\n3 14 9 6 2\n4 19 12 6 5\n5 23 9 5 5\n6 25 9 9 7\n7 29 4 12 9\n\n<span style=\"color: #008080;\">#standardize the values in each column\n<\/span>df_new = (df- <span style=\"color: #3366ff;\">df.mean<\/span> ())\/df. <span style=\"color: #3366ff;\">std<\/span> ()\n\n<span style=\"color: #008080;\">#view new data frame\n<\/span>df_new\n\n\t        y x1 x2 x3\n0 -1.418032 -1.078639 1.025393 -0.908151\n1 -0.857822 -0.294174 -0.146485 -0.908151\n2 -0.437664 -0.294174 0.634767 -0.525772\n3 -0.577717 0.490290 -0.927736 -0.908151\n4 0.122546 1.666987 -0.927736 0.238987\n5 0.682756 0.490290 -1.318362 0.238987\n6 0.962861 0.490290 0.244141 1.003746\n7 1.523071 -1.470871 1.416019 1.768505<\/b><\/pre>\n<p><span style=\"color: #000000;\">\u5404\u5217\u306e\u5e73\u5747\u3068\u6a19\u6e96\u504f\u5dee\u304c\u305d\u308c\u305e\u308c 0 \u3068 1 \u306b\u7b49\u3057\u3044\u3053\u3068\u3092\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <b><span style=\"color: #008080;\">#view mean of each column\n<\/span>df_new. <span style=\"color: #3366ff;\">mean<\/span> ()\n\ny 0.000000e+00\nx1 2.775558e-17\nx2 -4.163336e-17\nx3 5.551115e-17\ndtype:float64\n\n<span style=\"color: #008080;\">#view standard deviation of each column\n<\/span>df_new. <span style=\"color: #3366ff;\">std<\/span> ()\n\ny 1.0\nx1 1.0\nx2 1.0\nx3 1.0\ndtype:float64\n<\/b><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 2: \u7279\u5b9a\u306e DataFrame \u5217\u3092\u6b63\u898f\u5316\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">DataFrame \u5185\u306e\u7279\u5b9a\u306e\u5217\u306e\u307f\u3092\u6b63\u898f\u5316\u3057\u305f\u3044\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001\u591a\u304f\u306e<a href=\"https:\/\/statorials.org\/ja\/-10\/\" target=\"_blank\" rel=\"noopener\">\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u306f\u3001<\/a>\u7279\u5b9a\u306e\u30e2\u30c7\u30eb\u3092\u30c7\u30fc\u30bf\u306b\u9069\u5408\u3055\u305b\u308b\u524d\u306b\u3001\u4e88\u6e2c\u5909\u6570\u306e\u307f\u3092\u6a19\u6e96\u5316\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001pandas DataFrame \u306e\u7279\u5b9a\u306e\u5217\u3092\u6a19\u6e96\u5316\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <b><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#create data frame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">y<\/span> ': [8, 12, 15, 14, 19, 23, 25, 29],\n                   ' <span style=\"color: #ff0000;\">x1<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' <span style=\"color: #ff0000;\">x2<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12],\n                   ' <span style=\"color: #ff0000;\">x3<\/span> ': [2, 2, 3, 2, 5, 5, 7, 9]})\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>df\n\n\ty x1 x2 x3\n0 8 5 11 2\n1 12 7 8 2\n2 15 7 10 3\n3 14 9 6 2\n4 19 12 6 5\n5 23 9 5 5\n6 25 9 9 7\n7 29 4 12 9\n\n<span style=\"color: #008080;\">#define predictor variable columns<\/span>\ndf_x = df[[' <span style=\"color: #ff0000;\">x1<\/span> ', ' <span style=\"color: #ff0000;\">x2<\/span> ', ' <span style=\"color: #ff0000;\">x3<\/span> ']]\n\n<span style=\"color: #008080;\">#standardize the values for each predictor variable\n<\/span>df[[' <span style=\"color: #ff0000;\">x1<\/span> ',' <span style=\"color: #ff0000;\">x2<\/span> ',' <span style=\"color: #ff0000;\">x3<\/span> ']] = (df_x- <span style=\"color: #3366ff;\">df_x.mean<\/span> ())\/df_x. <span style=\"color: #3366ff;\">std<\/span> ()\n\n<span style=\"color: #008080;\">#view new data frame\n<\/span>df\n\n         y x1 x2 x3\n0 8 -1.078639 1.025393 -0.908151\n1 12 -0.294174 -0.146485 -0.908151\n2 15 -0.294174 0.634767 -0.525772\n3 14 0.490290 -0.927736 -0.908151\n4 19 1.666987 -0.927736 0.238987\n5 23 0.490290 -1.318362 0.238987\n6 25 0.490290 0.244141 1.003746\n7 29 -1.470871 1.416019 1.768505<\/b><\/pre>\n<p><span style=\"color: #000000;\">\u5217\u300cy\u300d\u306f\u5909\u66f4\u3055\u308c\u307e\u305b\u3093\u304c\u3001\u5217\u300cx1\u300d\u3001\u300cx2\u300d\u3001\u304a\u3088\u3073\u300cx3\u300d\u306f\u3059\u3079\u3066\u6a19\u6e96\u5316\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e88\u6e2c\u5b50\u5909\u6570\u306e\u5404\u5217\u306e\u5e73\u5747\u3068\u6a19\u6e96\u504f\u5dee\u304c\u305d\u308c\u305e\u308c 0 \u3068 1 \u306b\u7b49\u3057\u3044\u3053\u3068\u3092\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <b><span style=\"color: #008080;\">#view mean of each predictor variable column\n<\/span>df[[' <span style=\"color: #ff0000;\">x1<\/span> ', ' <span style=\"color: #ff0000;\">x2<\/span> ', ' <span style=\"color: #ff0000;\">x3<\/span> ']]. <span style=\"color: #3366ff;\">mean<\/span> ()\n\nx1 2.775558e-17\nx2 -4.163336e-17\nx3 5.551115e-17\ndtype:float64\n\n<span style=\"color: #008080;\">#view standard deviation of each predictor variable column\n<\/span>df[[' <span style=\"color: #ff0000;\">x1<\/span> ', ' <span style=\"color: #ff0000;\">x2<\/span> ', ' <span style=\"color: #ff0000;\">x3<\/span> ']]. <span style=\"color: #3366ff;\">std<\/span> ()\n\nx1 1.0\nx2 1.0\nx3 1.0\ndtype:float64<\/b><\/pre>\n<h3><strong><span style=\"color: #000000;\">\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/span><\/strong><\/h3>\n<p><a href=\"https:\/\/statorials.org\/ja\/pandas-\u30c6\u3099\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u5217\u3092\u6b63\u898f\u5316\u3059\u308b\/\" target=\"_blank\" rel=\"noopener\">Pandas DataFrame \u306e\u5217\u3092\u6b63\u898f\u5316\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u7570\u5e38\u5024\u3092\u524a\u9664\u3059\u308bpython\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u5916\u308c\u5024\u3092\u524a\u9664\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u6a19\u6e96\u5316\u3068\u6b63\u898f\u5316\/\" target=\"_blank\" rel=\"noopener\">\u6a19\u6e96\u5316\u3068\u6b63\u898f\u5316: \u9055\u3044\u306f\u4f55\u3067\u3059\u304b?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6a19\u6e96\u5316\u3068\u306f\u3001\u5e73\u5747\u5024\u304c 0\u3001\u6a19\u6e96\u504f\u5dee\u304c 1 \u306b\u306a\u308b\u3088\u3046\u306b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u3059\u3079\u3066\u306e\u5024\u3092\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3059\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002 \u6b21\u306e\u5f0f\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u5024\u3092\u6b63\u898f\u5316\u3057\u307e\u3059\u3002 x\u65b0\u3057\u3044= (x i \u2013 x )  [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-1821","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python \u3067\u30c7\u30fc\u30bf\u3092\u6a19\u6e96\u5316\u3059\u308b\u65b9\u6cd5: 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