{"id":3556,"date":"2023-07-16T20:16:40","date_gmt":"2023-07-16T20:16:40","guid":{"rendered":"https:\/\/statorials.org\/ja\/ols%e5%9b%9e%e5%b8%b0python\/"},"modified":"2023-07-16T20:16:40","modified_gmt":"2023-07-16T20:16:40","slug":"ols%e5%9b%9e%e5%b8%b0python","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/ols%e5%9b%9e%e5%b8%b0python\/","title":{"rendered":"Python \u3067 ols \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u4f8b\u3042\u308a)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u901a\u5e38\u6700\u5c0f\u4e8c\u4e57 (OLS) \u56de\u5e30\u306f\u30011 \u3064\u4ee5\u4e0a\u306e\u4e88\u6e2c\u5b50\u5909\u6570\u3068<a href=\"https:\/\/statorials.org\/ja\/\u5909\u6570\u306e\u8aac\u660e\u5fdc\u7b54\/\" target=\"_blank\" rel=\"noopener\">\u5fdc\u7b54\u5909\u6570<\/a>\u306e\u9593\u306e\u95a2\u4fc2\u3092\u6700\u3082\u3088\u304f\u8868\u3059\u76f4\u7dda\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u3092\u53ef\u80fd\u306b\u3059\u308b\u65b9\u6cd5\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u65b9\u6cd5\u306b\u3088\u308a\u3001\u6b21\u306e\u65b9\u7a0b\u5f0f\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u0177 = b <sub>0<\/sub> + b <sub>1<\/sub> x<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u0177<\/strong> : \u63a8\u5b9a\u5fdc\u7b54\u5024<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>b <sub>0<\/sub><\/strong> : \u56de\u5e30\u76f4\u7dda\u306e\u539f\u70b9<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>b <sub>1<\/sub><\/strong> : \u56de\u5e30\u76f4\u7dda\u306e\u50be\u304d<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u3053\u306e\u65b9\u7a0b\u5f0f\u306f\u3001\u4e88\u6e2c\u5b50\u3068\u5fdc\u7b54\u5909\u6570\u306e\u95a2\u4fc2\u3092\u7406\u89e3\u3059\u308b\u306e\u306b\u5f79\u7acb\u3061\u3001\u4e88\u6e2c\u5b50\u5909\u6570\u306e\u5024\u306b\u57fa\u3065\u3044\u3066\u5fdc\u7b54\u5909\u6570\u306e\u5024\u3092\u4e88\u6e2c\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u306f\u3001Python \u3067 OLS \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><b>\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u3092\u4f5c\u6210\u3059\u308b<\/b><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u300115 \u4eba\u306e\u751f\u5f92\u306b\u5bfe\u3057\u3066\u6b21\u306e 2 \u3064\u306e\u5909\u6570\u3092\u542b\u3080\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u7dcf\u5b66\u7fd2\u6642\u9593\u6570<\/span><\/li>\n<li><span style=\"color: #000000;\">\u8a66\u9a13\u306e\u7d50\u679c<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6642\u9593\u3092\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u3001\u8a66\u9a13\u30b9\u30b3\u30a2\u3092\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u3001OLS \u56de\u5e30\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u30d1\u30f3\u30c0\u3067\u3053\u306e\u507d\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008080;\">\n#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">hours<\/span> ': [1, 2, 4, 5, 5, 6, 6, 7, 8, 10, 11, 11, 12, 12, 14],\n                   ' <span style=\"color: #ff0000;\">score<\/span> ': [64, 66, 76, 73, 74, 81, 83, 82, 80, 88, 84, 82, 91, 93, 89]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n    hours score\n0 1 64\n1 2 66\n2 4 76\n3 5 73\n4 5 74\n5 6 81\n6 6 83\n7 7 82\n8 8 80\n9 10 88\n10 11 84\n11 11 82\n12 12 91\n13 12 93\n14 14 89<\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><b>\u30b9\u30c6\u30c3\u30d7 2: OLS \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b<\/b><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001 <a href=\"https:\/\/www.statsmodels.org\/stable\/index.html\" target=\"_blank\" rel=\"noopener\">statsmodels<\/a>\u30e2\u30b8\u30e5\u30fc\u30eb\u306e\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066<strong>\u6642\u9593<\/strong>\u3092\u4f7f\u7528\u3057\u3001<strong>\u5fdc\u7b54<\/strong>\u5909\u6570\u3068\u3057\u3066\u30b9\u30b3\u30a2\u3092\u4f7f\u7528\u3057\u3066 OLS \u56de\u5e30\u3092\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> statsmodels.api <span style=\"color: #008000;\">as<\/span> sm\n<\/span>\n#define predictor and response variables\n<span style=\"color: #000000;\">y = df[' <span style=\"color: #ff0000;\">score<\/span> ']\nx = df[' <span style=\"color: #ff0000;\">hours<\/span> ']<\/span>\n\n#add constant to predictor variables\n<span style=\"color: #000000;\">x = sm. <span style=\"color: #3366ff;\">add_constant<\/span> (x)\n<\/span>\n#fit linear regression model\n<span style=\"color: #000000;\">model = sm. <span style=\"color: #3366ff;\">OLS<\/span> (y,x). <span style=\"color: #3366ff;\">fit<\/span> ()\n<\/span>\n#view model summary\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.831\nModel: OLS Adj. R-squared: 0.818\nMethod: Least Squares F-statistic: 63.91\nDate: Fri, 26 Aug 2022 Prob (F-statistic): 2.25e-06\nTime: 10:42:24 Log-Likelihood: -39,594\nNo. Observations: 15 AIC: 83.19\nDf Residuals: 13 BIC: 84.60\nModel: 1                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P&gt;|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 65.3340 2.106 31.023 0.000 60.784 69.884\nhours 1.9824 0.248 7.995 0.000 1.447 2.518\n==================================================== ============================\nOmnibus: 4,351 Durbin-Watson: 1,677\nProb(Omnibus): 0.114 Jarque-Bera (JB): 1.329\nSkew: 0.092 Prob(JB): 0.515\nKurtosis: 1.554 Cond. No. 19.2\n==================================================== ============================<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>coef<\/strong>\u5217\u304b\u3089\u56de\u5e30\u4fc2\u6570\u3092\u78ba\u8a8d\u3057\u3001\u6b21\u306e\u8fd1\u4f3c\u56de\u5e30\u5f0f\u3092\u66f8\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u30b9\u30b3\u30a2 = 65.334 + 1.9824*(\u6642\u9593)<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u306f\u3001\u5b66\u7fd2\u6642\u9593\u304c\u8ffd\u52a0\u3055\u308c\u308b\u3054\u3068\u306b\u3001\u8a66\u9a13\u306e\u5e73\u5747\u30b9\u30b3\u30a2\u304c<strong>1.9824<\/strong>\u30dd\u30a4\u30f3\u30c8\u5897\u52a0\u3059\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5143\u306e\u5024<strong>65,334 \u306f\u3001<\/strong> 0 \u6642\u9593\u52c9\u5f37\u3057\u305f\u751f\u5f92\u306e\u4e88\u60f3\u3055\u308c\u308b\u8a66\u9a13\u306e\u5e73\u5747\u30b9\u30b3\u30a2\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u65b9\u7a0b\u5f0f\u3092\u4f7f\u7528\u3057\u3066\u3001\u5b66\u751f\u306e\u52c9\u5f37\u6642\u9593\u306b\u57fa\u3065\u3044\u3066\u4e88\u60f3\u3055\u308c\u308b\u8a66\u9a13\u306e\u5f97\u70b9\u3092\u6c42\u3081\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u300110 \u6642\u9593\u52c9\u5f37\u3057\u305f\u5b66\u751f\u306f\u3001\u8a66\u9a13\u30b9\u30b3\u30a2<strong>85.158<\/strong>\u3092\u9054\u6210\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u30b9\u30b3\u30a2 = 65.334 + 1.9824*(10) = 85.158<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u306e\u6982\u8981\u306e\u6b8b\u308a\u306e\u90e8\u5206\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>P(&gt;|t|):<\/strong>\u3053\u308c\u306f\u30e2\u30c7\u30eb\u4fc2\u6570\u306b\u95a2\u9023\u4ed8\u3051\u3089\u308c\u305f p \u5024\u3067\u3059\u3002<em>\u6642\u9593<\/em>\u306e p \u5024 (0.000) \u306f 0.05 \u672a\u6e80\u3067\u3042\u308b\u305f\u3081\u3001<em>\u6642\u9593<\/em>\u3068<em>\u30b9\u30b3\u30a2<\/em>\u306e\u9593\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u95a2\u9023\u304c\u3042\u308b\u3068\u8a00\u3048\u307e\u3059\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>R \u4e8c\u4e57:<\/strong>\u3053\u308c\u306f\u3001\u8a66\u9a13\u306e\u5f97\u70b9\u306e\u5909\u52d5\u306e\u30d1\u30fc\u30bb\u30f3\u30c6\u30fc\u30b8\u304c\u52c9\u5f37\u6642\u9593\u6570\u306b\u3088\u3063\u3066\u8aac\u660e\u3067\u304d\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u306e\u5834\u5408\u3001\u30b9\u30b3\u30a2\u306e\u5909\u52d5\u306e<strong>83.1%<\/strong>\u306f\u52c9\u5f37\u6642\u9593\u306b\u3088\u3063\u3066\u8aac\u660e\u3067\u304d\u307e\u3059\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>F \u7d71\u8a08\u91cf\u3068 p \u5024:<\/strong> F \u7d71\u8a08\u91cf ( <strong>63.91<\/strong> ) \u3068\u5bfe\u5fdc\u3059\u308b p \u5024 ( <strong>2.25e-06<\/strong> ) \u306f\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u5168\u4f53\u7684\u306a\u6709\u610f\u6027\u3001\u3064\u307e\u308a\u30e2\u30c7\u30eb\u5185\u306e\u4e88\u6e2c\u5b50\u5909\u6570\u304c\u5909\u52d5\u306e\u8aac\u660e\u306b\u5f79\u7acb\u3064\u304b\u3069\u3046\u304b\u3092\u793a\u3057\u307e\u3059\u3002\u5fdc\u7b54\u5909\u6570\u306b\u3002\u3053\u306e\u4f8b\u306e p \u5024\u306f 0.05 \u672a\u6e80\u3067\u3042\u308b\u305f\u3081\u3001\u30e2\u30c7\u30eb\u306f\u7d71\u8a08\u7684\u306b\u6709\u610f\u3067\u3042\u308a\u3001<em>\u6642\u9593\u306f<\/em><em>\u30b9\u30b3\u30a2\u306e<\/em>\u5909\u52d5\u3092\u8aac\u660e\u3059\u308b\u306e\u306b\u5f79\u7acb\u3064\u3068\u8003\u3048\u3089\u308c\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<h2><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: \u6700\u9069\u306a\u30e9\u30a4\u30f3\u3092\u8996\u899a\u5316\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6700\u5f8c\u306b\u3001 <strong>matplotlib<\/strong>\u30c7\u30fc\u30bf\u8996\u899a\u5316\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3057\u3066\u3001\u5b9f\u969b\u306e\u30c7\u30fc\u30bf \u30dd\u30a4\u30f3\u30c8\u306b\u9069\u5408\u3057\u305f\u56de\u5e30\u76f4\u7dda\u3092\u8996\u899a\u5316\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<\/span>\n#find line of best fit\n<span style=\"color: #000000;\">a, b = np. <span style=\"color: #3366ff;\">polyfit<\/span> (df[' <span style=\"color: #ff0000;\">hours<\/span> '], df[' <span style=\"color: #ff0000;\">score<\/span> '], <span style=\"color: #008000;\">1<\/span> )\n<\/span>\n#add points to plot\n<span style=\"color: #000000;\">plt. <span style=\"color: #3366ff;\">scatter<\/span> (df[' <span style=\"color: #ff0000;\">hours<\/span> '], df[' <span style=\"color: #ff0000;\">score<\/span> '], color=' <span style=\"color: #ff0000;\">purple<\/span> ')\n<\/span>\n#add line of best fit to plot\n<span style=\"color: #000000;\">plt. <span style=\"color: #3366ff;\">plot<\/span> (df[' <span style=\"color: #ff0000;\">hours<\/span> '], a*df[' <span style=\"color: #ff0000;\">hours<\/span> ']+b)\n<\/span>\n#add fitted regression equation to plot\n<span style=\"color: #000000;\">plt. <span style=\"color: #3366ff;\">text<\/span> ( <span style=\"color: #008000;\">1<\/span> , <span style=\"color: #008000;\">90<\/span> , 'y = ' + '{:.3f}'.format(b) + ' + {:.3f}'.format(a) + 'x', size= <span style=\"color: #008000;\">12<\/span> )\n\n<span style=\"color: #008080;\">#add axis labels\n<\/span>plt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #ff0000;\">Hours Studied<\/span> ')\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #ff0000;\">Exam Score<\/span> ')\n<\/span><\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-29456 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/ligne11.jpg\" alt=\"\" width=\"502\" height=\"385\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u7d2b\u8272\u306e\u70b9\u306f\u5b9f\u969b\u306e\u30c7\u30fc\u30bf \u30dd\u30a4\u30f3\u30c8\u3092\u8868\u3057\u3001\u9752\u8272\u306e\u7dda\u306f\u8fd1\u4f3c\u3055\u308c\u305f\u56de\u5e30\u76f4\u7dda\u3092\u8868\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305f\u3001 <strong>plt.text()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u8fd1\u4f3c\u56de\u5e30\u5f0f\u3092\u30d7\u30ed\u30c3\u30c8\u306e\u5de6\u4e0a\u9685\u306b\u8ffd\u52a0\u3057\u307e\u3057\u305f\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u30b0\u30e9\u30d5\u3092\u898b\u308b\u3068\u3001\u8fd1\u4f3c\u56de\u5e30\u76f4\u7dda\u304c<strong>\u6642\u9593<\/strong>\u5909\u6570\u3068<strong>\u30b9\u30b3\u30a2<\/strong>\u5909\u6570\u306e\u9593\u306e\u95a2\u4fc2\u3092\u975e\u5e38\u306b\u3088\u304f\u6349\u3048\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u30bf\u30b9\u30af\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ja\/\u30ed\u30b7\u3099\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30python\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u6307\u6570\u56de\u5e30python\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u6307\u6570\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/python\u306eaic\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u56de\u5e30\u30e2\u30c7\u30eb\u306e AIC \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u901a\u5e38\u6700\u5c0f\u4e8c\u4e57 (OLS) \u56de\u5e30\u306f\u30011 \u3064\u4ee5\u4e0a\u306e\u4e88\u6e2c\u5b50\u5909\u6570\u3068\u5fdc\u7b54\u5909\u6570\u306e\u9593\u306e\u95a2\u4fc2\u3092\u6700\u3082\u3088\u304f\u8868\u3059\u76f4\u7dda\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u3092\u53ef\u80fd\u306b\u3059\u308b\u65b9\u6cd5\u3067\u3059\u3002 \u3053\u306e\u65b9\u6cd5\u306b\u3088\u308a\u3001\u6b21\u306e\u65b9\u7a0b\u5f0f\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002 \u0177 = b 0 + b 1 x \u91d1 [&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-3556","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 OLS \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u901a\u5e38\u6700\u5c0f\u4e8c\u4e57 (OLS) \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/ja\/ols\u56de\u5e30python\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python \u3067 OLS \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - Statorials\" \/>\n<meta property=\"og:description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u901a\u5e38\u6700\u5c0f\u4e8c\u4e57 (OLS) \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ja\/ols\u56de\u5e30python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-16T20:16:40+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/ligne11.jpg\" \/>\n<meta name=\"author\" content=\"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u57f7\u7b46\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u63a8\u5b9a\u8aad\u307f\u53d6\u308a\u6642\u9593\" \/>\n\t<meta name=\"twitter:data2\" content=\"1\u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/ja\/ols%e5%9b%9e%e5%b8%b0python\/\",\"url\":\"https:\/\/statorials.org\/ja\/ols%e5%9b%9e%e5%b8%b0python\/\",\"name\":\"Python \u3067 OLS \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/ja\/#website\"},\"datePublished\":\"2023-07-16T20:16:40+00:00\",\"dateModified\":\"2023-07-16T20:16:40+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/ja\/#\/schema\/person\/86b92d2dd87368b26360d19d9c6a5d83\"},\"description\":\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u901a\u5e38\u6700\u5c0f\u4e8c\u4e57 (OLS) \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/ja\/ols%e5%9b%9e%e5%b8%b0python\/#breadcrumb\"},\"inLanguage\":\"ja\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/ja\/ols%e5%9b%9e%e5%b8%b0python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/ja\/ols%e5%9b%9e%e5%b8%b0python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"\u5bb6\",\"item\":\"https:\/\/statorials.org\/ja\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Python \u3067 ols \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u4f8b\u3042\u308a)\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/ja\/#website\",\"url\":\"https:\/\/statorials.org\/ja\/\",\"name\":\"Statorials\",\"description\":\"\u7d71\u8a08\u80fd\u529b\u3078\u306e\u30ac\u30a4\u30c9\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/ja\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"ja\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/ja\/#\/schema\/person\/86b92d2dd87368b26360d19d9c6a5d83\",\"name\":\"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"ja\",\"@id\":\"https:\/\/statorials.org\/ja\/#\/schema\/person\/image\/\",\"url\":\"http:\/\/statorials.org\/ja\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"http:\/\/statorials.org\/ja\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb\"},\"description\":\"\u79c1\u306f\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u3067\u3059\u3002\u9000\u8077\u3057\u305f\u7d71\u8a08\u6559\u6388\u304b\u3089\u3001\u5c02\u4efb\u306e Statorials \u6559\u80b2\u8005\u306b\u306a\u308a\u307e\u3057\u305f\u3002 \u7d71\u8a08\u5206\u91ce\u306b\u304a\u3051\u308b\u8c4a\u5bcc\u306a\u7d4c\u9a13\u3068\u5c02\u9580\u77e5\u8b58\u3092\u6d3b\u304b\u3057\u3066\u3001\u79c1\u306f Statorials \u3092\u901a\u3058\u3066\u5b66\u751f\u306b\u529b\u3092\u4e0e\u3048\u308b\u305f\u3081\u306b\u81ea\u5206\u306e\u77e5\u8b58\u3092\u5171\u6709\u3059\u308b\u3053\u3068\u306b\u5c3d\u529b\u3057\u3066\u3044\u307e\u3059\u3002\u3082\u3063\u3068\u77e5\u308b\",\"sameAs\":[\"http:\/\/statorials.org\/ja\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Python \u3067 OLS \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - Statorials","description":"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u901a\u5e38\u6700\u5c0f\u4e8c\u4e57 (OLS) \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/statorials.org\/ja\/ols\u56de\u5e30python\/","og_locale":"ja_JP","og_type":"article","og_title":"Python \u3067 OLS \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - Statorials","og_description":"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u901a\u5e38\u6700\u5c0f\u4e8c\u4e57 (OLS) \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002","og_url":"https:\/\/statorials.org\/ja\/ols\u56de\u5e30python\/","og_site_name":"Statorials","article_published_time":"2023-07-16T20:16:40+00:00","og_image":[{"url":"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/ligne11.jpg"}],"author":"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb","twitter_card":"summary_large_image","twitter_misc":{"\u57f7\u7b46\u8005":"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb","\u63a8\u5b9a\u8aad\u307f\u53d6\u308a\u6642\u9593":"1\u5206"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/statorials.org\/ja\/ols%e5%9b%9e%e5%b8%b0python\/","url":"https:\/\/statorials.org\/ja\/ols%e5%9b%9e%e5%b8%b0python\/","name":"Python \u3067 OLS \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - Statorials","isPartOf":{"@id":"https:\/\/statorials.org\/ja\/#website"},"datePublished":"2023-07-16T20:16:40+00:00","dateModified":"2023-07-16T20:16:40+00:00","author":{"@id":"https:\/\/statorials.org\/ja\/#\/schema\/person\/86b92d2dd87368b26360d19d9c6a5d83"},"description":"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u901a\u5e38\u6700\u5c0f\u4e8c\u4e57 (OLS) \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002","breadcrumb":{"@id":"https:\/\/statorials.org\/ja\/ols%e5%9b%9e%e5%b8%b0python\/#breadcrumb"},"inLanguage":"ja","potentialAction":[{"@type":"ReadAction","target":["https:\/\/statorials.org\/ja\/ols%e5%9b%9e%e5%b8%b0python\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/statorials.org\/ja\/ols%e5%9b%9e%e5%b8%b0python\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"\u5bb6","item":"https:\/\/statorials.org\/ja\/"},{"@type":"ListItem","position":2,"name":"Python \u3067 ols \u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u4f8b\u3042\u308a)"}]},{"@type":"WebSite","@id":"https:\/\/statorials.org\/ja\/#website","url":"https:\/\/statorials.org\/ja\/","name":"Statorials","description":"\u7d71\u8a08\u80fd\u529b\u3078\u306e\u30ac\u30a4\u30c9","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/statorials.org\/ja\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"ja"},{"@type":"Person","@id":"https:\/\/statorials.org\/ja\/#\/schema\/person\/86b92d2dd87368b26360d19d9c6a5d83","name":"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb","image":{"@type":"ImageObject","inLanguage":"ja","@id":"https:\/\/statorials.org\/ja\/#\/schema\/person\/image\/","url":"http:\/\/statorials.org\/ja\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg","contentUrl":"http:\/\/statorials.org\/ja\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg","caption":"\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u30fb\u30a2\u30f3\u30c0\u30fc\u30bd\u30f3\u535a\u58eb"},"description":"\u79c1\u306f\u30d9\u30f3\u30b8\u30e3\u30df\u30f3\u3067\u3059\u3002\u9000\u8077\u3057\u305f\u7d71\u8a08\u6559\u6388\u304b\u3089\u3001\u5c02\u4efb\u306e Statorials \u6559\u80b2\u8005\u306b\u306a\u308a\u307e\u3057\u305f\u3002 \u7d71\u8a08\u5206\u91ce\u306b\u304a\u3051\u308b\u8c4a\u5bcc\u306a\u7d4c\u9a13\u3068\u5c02\u9580\u77e5\u8b58\u3092\u6d3b\u304b\u3057\u3066\u3001\u79c1\u306f Statorials \u3092\u901a\u3058\u3066\u5b66\u751f\u306b\u529b\u3092\u4e0e\u3048\u308b\u305f\u3081\u306b\u81ea\u5206\u306e\u77e5\u8b58\u3092\u5171\u6709\u3059\u308b\u3053\u3068\u306b\u5c3d\u529b\u3057\u3066\u3044\u307e\u3059\u3002\u3082\u3063\u3068\u77e5\u308b","sameAs":["http:\/\/statorials.org\/ja"]}]}},"yoast_meta":{"yoast_wpseo_title":"","yoast_wpseo_metadesc":"","yoast_wpseo_canonical":""},"_links":{"self":[{"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/posts\/3556","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/comments?post=3556"}],"version-history":[{"count":0,"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/posts\/3556\/revisions"}],"wp:attachment":[{"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/media?parent=3556"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/categories?post=3556"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/statorials.org\/ja\/wp-json\/wp\/v2\/tags?post=3556"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}