{"id":3916,"date":"2023-07-14T18:26:54","date_gmt":"2023-07-14T18:26:54","guid":{"rendered":"https:\/\/statorials.org\/ja\/3%e6%ac%a1%e5%9b%9e%e5%b8%b0python\/"},"modified":"2023-07-14T18:26:54","modified_gmt":"2023-07-14T18:26:54","slug":"3%e6%ac%a1%e5%9b%9e%e5%b8%b0python","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/3%e6%ac%a1%e5%9b%9e%e5%b8%b0python\/","title":{"rendered":"Python \u3067 3 \u6b21\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

3 \u6b21\u56de\u5e30\u306f<\/strong>\u3001\u5909\u6570\u9593\u306e\u95a2\u4fc2\u304c\u975e\u7dda\u5f62\u3067\u3042\u308b\u5834\u5408\u306b\u3001\u4e88\u6e2c\u5909\u6570\u3068\u5fdc\u7b54\u5909\u6570\u306e\u9593\u306e\u95a2\u4fc2\u3092\u5b9a\u91cf\u5316\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u56de\u5e30\u306e\u4e00\u7a2e\u3067\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067 3 \u6b21\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u4f8b: Python \u3067\u306e 3 \u6b21\u56de\u5e30<\/span><\/strong><\/h2>\n

2 \u3064\u306e\u5909\u6570 (x \u3068 y) \u3092\u542b\u3080\u6b21\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' x<\/span> ': [6, 9, 12, 16, 22, 28, 33, 40, 47, 51, 55, 60],\n                   ' y<\/span> ': [14, 28, 50, 64, 67, 57, 55, 57, 68, 74, 88, 110]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n     xy\n0 6 14\n1 9 28\n2 12 50\n3 16 64\n4 22 67\n5 28 57\n6 33 55\n7 40 57\n8 47 68\n9 51 74\n10 55 88\n11 60 110\n<\/strong><\/pre>\n
\u3053\u306e\u30c7\u30fc\u30bf\u306e\u5358\u7d14\u306a\u6563\u5e03\u56f3\u3092\u4f5c\u6210\u3059\u308b\u3068\u30012 \u3064\u306e\u5909\u6570\u9593\u306e\u95a2\u4fc2\u304c\u975e\u7dda\u5f62\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span> <\/p>\n
 import matplotlib. pyplot<\/span><\/span> as plt<\/span>\n\n#create scatterplot\n<\/span>plt. scatter<\/span> (df. x<\/span> , df. y<\/span> )<\/span><\/span><\/strong> <\/pre>\n
\"\"<\/p>\n
 x \u306e\u5024\u304c\u5897\u52a0\u3059\u308b\u3068\u3001y \u306f\u3042\u308b\u70b9\u307e\u3067\u5897\u52a0\u3057\u3001\u305d\u306e\u5f8c\u6e1b\u5c11\u3057\u3001\u305d\u306e\u5f8c\u518d\u3073\u5897\u52a0\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u30d7\u30ed\u30c3\u30c8\u5185\u306b 2 \u3064\u306e\u300c\u66f2\u7dda\u300d\u304c\u3042\u308b\u3053\u306e\u30d1\u30bf\u30fc\u30f3\u306f\u30012 \u3064\u306e\u5909\u6570\u9593\u306e\u4e09\u6b21\u95a2\u4fc2\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u306f\u30013 \u6b21\u56de\u5e30\u30e2\u30c7\u30eb\u304c 2 \u3064\u306e\u5909\u6570\u9593\u306e\u95a2\u4fc2\u3092\u5b9a\u91cf\u5316\u3059\u308b\u305f\u3081\u306e\u9069\u5207\u306a\u5019\u88dc\u3067\u3042\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n
 3 \u6b21\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u306b\u306f\u3001 numpy.polyfit()<\/a>\u95a2\u6570<\/span>\u3092\u4f7f\u7528\u3057\u3066\u6b21\u6570 3 \u306e\u591a\u9805\u5f0f\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\u307e\u3059\u3002<\/span> <\/p>\n
 import<\/span> numpy as<\/span> np\n\n#fit cubic regression model\n<\/span>model = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 3))<\/span>\n\n#add fitted cubic regression line to scatterplot\n<\/span>polyline = np. linspace<\/span> (1, 60, 50)\nplt. scatter<\/span> (df. x<\/span> , df. y<\/span> )\nplt. plot<\/span> (polyline, model(polyline))\n\n#add axis labels\n<\/span>plt. xlabel<\/span> (' x<\/span> ')\nplt. ylabel<\/span> (' y<\/span> ')\n\n#displayplot\n<\/span>plt. show<\/span> ()<\/strong> <\/pre>\n
\"Python<\/p>\n
\u30e2\u30c7\u30eb\u4fc2\u6570\u3092\u51fa\u529b\u3059\u308b\u3053\u3068\u3067\u3001\u8fd1\u4f3c\u3055\u308c\u305f 3 \u6b21\u56de\u5e30\u5f0f\u3092\u53d6\u5f97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 print<\/span> (model)\n\n          3 2\n0.003302x - 0.3214x + 9.832x - 32.01\n<\/strong><\/pre>\n
\u8fd1\u4f3c\u3055\u308c\u305f 3 \u6b21\u56de\u5e30\u5f0f\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n
 y = 0.003302(x) 3<\/sup> \u2013 0.3214(x) 2<\/sup> + 9.832x \u2013 30.01<\/strong><\/span><\/p>\n
\u3053\u306e\u65b9\u7a0b\u5f0f\u3092\u4f7f\u7528\u3057\u3066\u3001x \u306e\u5024\u306b\u57fa\u3065\u3044\u3066 y \u306e\u671f\u5f85\u5024\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u305f\u3068\u3048\u3070\u3001x \u304c 30 \u306e\u5834\u5408\u3001y \u306e\u671f\u5f85\u5024\u306f 64.844 \u3067\u3059\u3002<\/span><\/p>\n
 y = 0.003302(30) 3<\/sup> \u2013 0.3214(30) 2<\/sup> + 9.832(30) \u2013 30.01 = 64.844<\/span><\/p>\n
\u30e2\u30c7\u30eb\u306e R \u4e8c\u4e57 (\u4e88\u6e2c\u5b50\u5909\u6570\u306b\u3088\u3063\u3066\u8aac\u660e\u3067\u304d\u308b\u5fdc\u7b54\u5909\u6570\u306e\u5206\u6563\u306e\u5272\u5408) \u3092\u53d6\u5f97\u3059\u308b\u77ed\u3044\u95a2\u6570\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #define function to calculate r-squared<\/span>\ndef<\/span> polyfit(x, y, degree):\n    results = {}\n    coeffs = np. polyfit<\/span> (x, y, degree)\n    p = np. poly1d<\/span> (coeffs)\n    #calculate r-squared<\/span>\n    yhat = p(x)\n    ybar = np. sum<\/span> (y)\/len(y)\n    ssreg = np. sum<\/span> ((yhat-ybar) **<\/span> 2)\n    sstot = np. sum<\/span> ((y - ybar) **<\/span> 2)\n    results[' r_squared<\/span> '] = ssreg \/ sstot\n\n    return<\/span> results\n\n#find r-squared of polynomial model with degree = 3\n<\/span>polyfit(df. x<\/span> , df. y<\/span> , 3)\n\n{'r_squared': 0.9632469890057967}\n<\/strong><\/pre>\n
\u3053\u306e\u4f8b\u3067\u306f\u3001\u30e2\u30c7\u30eb\u306e R \u4e8c\u4e57\u306f0.9632<\/strong>\u3067\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u306f\u3001\u5fdc\u7b54\u5909\u6570\u306e\u5909\u52d5\u306e 96.32% \u304c\u4e88\u6e2c\u5909\u6570\u306b\u3088\u3063\u3066\u8aac\u660e\u3067\u304d\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u306e\u5024\u304c\u975e\u5e38\u306b\u9ad8\u3044\u305f\u3081\u30013 \u6b21\u56de\u5e30\u30e2\u30c7\u30eb\u304c 2 \u3064\u306e\u5909\u6570\u9593\u306e\u95a2\u4fc2\u3092\u9069\u5207\u306b\u5b9a\u91cf\u5316\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n
\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
 Python \u3067\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a>
Python \u3067\u4e8c\u6b21\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a>
Python \u3067\u591a\u9805\u5f0f\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
3 \u6b21\u56de\u5e30\u306f\u3001\u5909\u6570\u9593\u306e\u95a2\u4fc2\u304c\u975e\u7dda\u5f62\u3067\u3042\u308b\u5834\u5408\u306b\u3001\u4e88\u6e2c\u5909\u6570\u3068\u5fdc\u7b54\u5909\u6570\u306e\u9593\u306e\u95a2\u4fc2\u3092\u5b9a\u91cf\u5316\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u56de\u5e30\u306e\u4e00\u7a2e\u3067\u3059\u3002 \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067 3 \u6b21\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002 \u4f8b: Pytho […]<\/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-3916","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nPython \u3067 3 \u6b21\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 - \u7d71\u8a08<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067 3 \u6b21\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u4f8b\u3092\u6319\u3052\u3066\u8aac\u660e\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\/3\u6b21\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 3 \u6b21\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 - 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