{"id":2237,"date":"2023-07-23T03:04:58","date_gmt":"2023-07-23T03:04:58","guid":{"rendered":"https:\/\/statorials.org\/ja\/python%e3%81%a6%e3%82%99%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%82%92%e5%a4%89%e6%8f%9b%e3%81%99%e3%82%8b\/"},"modified":"2023-07-23T03:04:58","modified_gmt":"2023-07-23T03:04:58","slug":"python%e3%81%a6%e3%82%99%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%82%92%e5%a4%89%e6%8f%9b%e3%81%99%e3%82%8b","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/python%e3%81%a6%e3%82%99%e3%83%86%e3%82%99%e3%83%bc%e3%82%bf%e3%82%92%e5%a4%89%e6%8f%9b%e3%81%99%e3%82%8b\/","title":{"rendered":"Python \u3067\u30c7\u30fc\u30bf\u3092\u5909\u63db\u3059\u308b\u65b9\u6cd5 (\u5bfe\u6570\u3001\u5e73\u65b9\u6839\u3001\u7acb\u65b9\u6839)"},"content":{"rendered":"

<\/p>\n

\n

\u591a\u304f\u306e\u7d71\u8a08\u30c6\u30b9\u30c8\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u6b63\u898f\u5206\u5e03\u3057\u3066\u3044\u308b\u3053\u3068\u3092\u524d\u63d0\u3068\u3057\u3066\u3044\u307e\u3059\u3002\u305f\u3060\u3057\u3001\u5b9f\u969b\u306b\u306f\u305d\u3046\u3067\u306f\u306a\u3044\u3053\u3068\u304c\u3088\u304f\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u554f\u984c\u3092\u89e3\u6c7a\u3059\u308b 1 \u3064\u306e\u65b9\u6cd5\u306f\u3001\u6b21\u306e 3 \u3064\u306e\u5909\u63db\u306e\u3044\u305a\u308c\u304b\u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf \u30bb\u30c3\u30c8\u5185\u306e\u5024\u306e\u5206\u5e03\u3092\u5909\u63db\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n

1. \u5bfe\u6570\u5909\u63db:<\/strong>\u5fdc\u7b54\u5909\u6570\u3092 y \u304b\u3089log(y)<\/strong>\u306b\u5909\u63db\u3057\u307e\u3059\u3002<\/span><\/p>\n

2. \u5e73\u65b9\u6839\u5909\u63db:<\/strong>\u5fdc\u7b54\u5909\u6570\u3092 y \u304b\u3089\u221ay<\/span><\/strong>\u306b\u5909\u63db\u3057\u307e\u3059\u3002<\/span><\/p>\n

3. \u7acb\u65b9\u6839\u5909\u63db:<\/strong>\u5fdc\u7b54\u5909\u6570\u3092 y \u304b\u3089y 1\/3<\/sup><\/strong>\u306b\u5909\u63db\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u308c\u3089\u306e\u5909\u63db\u3092\u5b9f\u884c\u3059\u308b\u3068\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u901a\u5e38\u3001\u3088\u308a\u6b63\u898f\u5206\u5e03\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n

\u6b21\u306e\u4f8b\u306f\u3001Python \u3067\u3053\u308c\u3089\u306e\u5909\u63db\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n

Python \u3067\u306e\u5bfe\u6570\u5909\u63db<\/strong><\/span><\/h3>\n

\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u5909\u6570\u306b\u5bfe\u3057\u3066\u5bfe\u6570\u5909\u63db\u3092<\/strong>\u5b9f\u884c\u3057\u3001\u30c7\u30fc\u30bf\u306e\u5143\u306e\u5206\u5e03\u3068\u5bfe\u6570\u5909\u63db\u3055\u308c\u305f\u5206\u5e03\u3092\u8868\u793a\u3059\u308b\u305f\u3081\u306e\u4e26\u5217\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span> <\/p>\n

 import<\/span> numpy as<\/span> np\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\n#make this example reproducible\n<\/span>n.p. random<\/span> . seeds<\/span> (0)\n\n#create beta distributed random variable with 200 values\n<\/span>data = np. random<\/span> . beta<\/span> (a= 4<\/span> , b= 15<\/span> , size= 300<\/span> )\n\n#create log-transformed data\n<\/span>data_log = np. log<\/span> (data)\n\n#define grid of plots\n<\/span>fig, axs = plt. subplots<\/span> (nrows= 1<\/span> , ncols= 2<\/span> )\n\n#create histograms\n<\/span>axs[0]. hist<\/span> (data, edgecolor=' black<\/span> ')\naxs[1]. hist<\/span> (data_log, edgecolor=' black<\/span> ')\n\n#add title to each histogram\n<\/span>axs[0]. set_title<\/span> (' Original Data<\/span> ')\naxs[1].set_title(' Log-Transformed Data<\/span> ')\n<\/strong><\/pre>\n
\"\"<\/p>\n
\u5bfe\u6570\u5909\u63db\u3055\u308c\u305f\u5206\u5e03\u304c\u5143\u306e\u5206\u5e03\u3088\u308a\u3082\u3069\u306e\u3088\u3046\u306b\u6b63\u898f\u5206\u5e03\u306b\u306a\u3063\u3066\u3044\u308b\u304b\u306b\u6ce8\u76ee\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
\u307e\u3060\u5b8c\u5168\u306a\u300c\u91e3\u9418\u578b\u300d\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u304c\u3001\u5143\u306e\u5206\u5e03\u3088\u308a\u3082\u6b63\u898f\u5206\u5e03\u306b\u8fd1\u3065\u3044\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 Python \u3067\u306e\u5e73\u65b9\u6839\u5909\u63db<\/strong><\/span><\/h3>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u5909\u6570\u306b\u5bfe\u3057\u3066\u5e73\u65b9\u6839\u5909\u63db\u3092<\/strong>\u5b9f\u884c\u3057\u3001\u4e26\u3079\u3066\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3057\u3066\u3001\u5143\u306e\u5206\u5e03\u3068\u5e73\u65b9\u6839\u5909\u63db\u3055\u308c\u305f\u30c7\u30fc\u30bf\u306e\u5206\u5e03\u3092\u8868\u793a\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span> <\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\n#make this example reproducible\n<\/span>n.p. random<\/span> . seeds<\/span> (0)\n\n#create beta distributed random variable with 200 values\n<\/span>data = np. random<\/span> . beta<\/span> (a= 1<\/span> , b= 5<\/span> , size= 300<\/span> )\n\n#create log-transformed data\n<\/span>data_log = np. sqrt<\/span> (data)\n\n#define grid of plots\n<\/span>fig, axs = plt. subplots<\/span> (nrows= 1<\/span> , ncols= 2<\/span> )\n\n#create histograms\n<\/span>axs[0]. hist<\/span> (data, edgecolor=' black<\/span> ')\naxs[1]. hist<\/span> (data_log, edgecolor=' black<\/span> ')\n\n#add title to each histogram\n<\/span>axs[0]. set_title<\/span> (' Original Data<\/span> ')\naxs[1].set_title(' Square Root Transformed Data<\/span> ')<\/strong> <\/pre>\n
\"\"<\/p>\n
\u5e73\u65b9\u6839\u5909\u63db\u3055\u308c\u305f\u30c7\u30fc\u30bf\u304c\u5143\u306e\u30c7\u30fc\u30bf\u3088\u308a\u3082\u306f\u308b\u304b\u306b\u6b63\u898f\u5206\u5e03\u3057\u3066\u3044\u308b\u3053\u3068\u306b\u6ce8\u76ee\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
 Python \u3067\u306e\u7acb\u65b9\u6839\u5909\u63db<\/strong><\/span><\/h3>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u5909\u6570\u306b\u5bfe\u3057\u3066\u7acb\u65b9\u6839\u5909\u63db\u3092<\/strong>\u5b9f\u884c\u3057\u3001\u30c7\u30fc\u30bf\u306e\u5143\u306e\u5206\u5e03\u3068\u7acb\u65b9\u6839\u5909\u63db\u5f8c\u306e\u5206\u5e03\u3092\u8868\u793a\u3059\u308b\u305f\u3081\u306e\u4e26\u5217\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span> <\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\n#make this example reproducible\n<\/span>n.p. random<\/span> . seeds<\/span> (0)\n\n#create beta distributed random variable with 200 values\n<\/span>data = np. random<\/span> . beta<\/span> (a= 1<\/span> , b= 5<\/span> , size= 300<\/span> )\n\n#create log-transformed data\n<\/span>data_log = np. cbrt<\/span> (data)\n\n#define grid of plots\n<\/span>fig, axs = plt. subplots<\/span> (nrows= 1<\/span> , ncols= 2<\/span> )\n\n#create histograms\n<\/span>axs[0]. hist<\/span> (data, edgecolor=' black<\/span> ')\naxs[1]. hist<\/span> (data_log, edgecolor=' black<\/span> ')\n\n#add title to each histogram\n<\/span>axs[0]. set_title<\/span> (' Original Data<\/span> ')\naxs[1].set_title(' Cube Root Transformed Data<\/span> ')<\/strong> <\/pre>\n
\"\"<\/p>\n
\u7acb\u65b9\u6839\u306b\u5909\u63db\u3055\u308c\u305f\u30c7\u30fc\u30bf\u304c\u5143\u306e\u30c7\u30fc\u30bf\u3088\u308a\u3082\u306f\u308b\u304b\u306b\u6b63\u898f\u5206\u5e03\u3057\u3066\u3044\u308b\u3053\u3068\u306b\u6ce8\u76ee\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
Python \u3067 Z \u30b9\u30b3\u30a2\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a>
Python \u3067\u30c7\u30fc\u30bf\u3092\u6b63\u898f\u5316\u3059\u308b\u65b9\u6cd5<\/a>
\u7d71\u8a08\u306b\u304a\u3051\u308b\u6b63\u898f\u6027\u306e\u4eee\u5b9a\u3068\u306f\u4f55\u3067\u3059\u304b?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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