{"id":2237,"date":"2023-07-23T03:04:58","date_gmt":"2023-07-23T03:04:58","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b3%e1%84%85%e1%85%a9-%e1%84%83%e1%85%a6%e1%84%8b%e1%85%b5%e1%84%90%e1%85%a5-%e1%84%87%e1%85%a7%e1%86%ab%e1%84%92%e1%85%aa\/"},"modified":"2023-07-23T03:04:58","modified_gmt":"2023-07-23T03:04:58","slug":"%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b3%e1%84%85%e1%85%a9-%e1%84%83%e1%85%a6%e1%84%8b%e1%85%b5%e1%84%90%e1%85%a5-%e1%84%87%e1%85%a7%e1%86%ab%e1%84%92%e1%85%aa","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b3%e1%84%85%e1%85%a9-%e1%84%83%e1%85%a6%e1%84%8b%e1%85%b5%e1%84%90%e1%85%a5-%e1%84%87%e1%85%a7%e1%86%ab%e1%84%92%e1%85%aa\/","title":{"rendered":"Python\uc5d0\uc11c \ub370\uc774\ud130\ub97c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95(\ub85c\uadf8, \uc81c\uacf1\uadfc, \uc138\uc81c\uacf1\uadfc)"},"content":{"rendered":"

<\/p>\n

\n

\ub9ce\uc740 \ud1b5\uacc4 \ud14c\uc2a4\ud2b8\uc5d0\uc11c\ub294 \ub370\uc774\ud130 \uc138\ud2b8\uac00 \uc815\uaddc \ubd84\ud3ec\ub97c \ub530\ub978\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc2e4\uc81c\ub85c\ub294 \uadf8\ub807\uc9c0 \uc54a\uc740 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740 \ub2e4\uc74c \uc138 \uac00\uc9c0 \ubcc0\ud658 \uc911 \ud558\ub098\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \uc138\ud2b8\uc758 \uac12 \ubd84\ud3ec\ub97c \ubcc0\ud658\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n

1. \ub85c\uadf8 \ubcc0\ud658:<\/strong> \uc751\ub2f5 \ubcc0\uc218\ub97c y\uc5d0\uc11c log(y)<\/strong> \ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.<\/span><\/p>\n

2. \uc81c\uacf1\uadfc \ubcc0\ud658:<\/strong> \uc751\ub2f5 \ubcc0\uc218\ub97c y\uc5d0\uc11c \u221ay<\/span><\/strong> \ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.<\/span><\/p>\n

3. \uc138\uc81c\uacf1\uadfc \ubcc0\ud658:<\/strong> \uc751\ub2f5 \ubcc0\uc218\ub97c y\uc5d0\uc11c y 1\/3<\/sup><\/strong> \uc73c\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc774\ub7ec\ud55c \ubcc0\ud658\uc744 \uc218\ud589\ud568\uc73c\ub85c\uc368 \ub370\uc774\ud130\uc138\ud2b8\ub294 \uc77c\ubc18\uc801\uc73c\ub85c \ubcf4\ub2e4 \uc815\uaddc \ubd84\ud3ec\ub97c \ub744\uac8c \ub429\ub2c8\ub2e4.<\/span><\/p>\n

\ub2e4\uc74c \uc608\uc81c\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \uc774\ub7ec\ud55c \ubcc0\ud658\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

Python\uc758 \ub85c\uadf8 \ubcc0\ud658<\/strong><\/span><\/h3>\n

\ub2e4\uc74c \ucf54\ub4dc\ub294 \ubcc0\uc218\uc5d0 \ub300\ud574 \ub85c\uadf8 \ubcc0\ud658\uc744<\/strong> \uc218\ud589\ud558\uace0 \ubcd1\ub82c \ud50c\ub86f\uc744 \uc0dd\uc131\ud558\uc5ec \ub370\uc774\ud130\uc758 \uc6d0\ub798 \ubd84\ud3ec\uc640 \ub85c\uadf8 \ubcc0\ud658\ub41c \ubd84\ud3ec\ub97c \ud45c\uc2dc\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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
 \ub85c\uadf8 \ubcc0\ud658\ub41c \ubd84\ud3ec\uac00 \uc6d0\ub798 \ubd84\ud3ec\ubcf4\ub2e4 \ub354 \uc815\uaddc \ubd84\ud3ec\ub97c \ub530\ub974\ub294\uc9c0 \ud655\uc778\ud558\uc138\uc694.<\/span><\/p>\n
 \uc5ec\uc804\ud788 \uc644\ubcbd\ud55c “\uc885 \ubaa8\uc591”\uc740 \uc544\ub2c8\uc9c0\ub9cc \uc6d0\ub798 \ubd84\ud3ec\ubcf4\ub2e4 \uc815\uaddc \ubd84\ud3ec\uc5d0 \ub354 \uac00\uae5d\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 Python\uc758 \uc81c\uacf1\uadfc \ubcc0\ud658<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \ubcc0\uc218\uc5d0 \ub300\ud574 \uc81c\uacf1\uadfc \ubcc0\ud658\uc744<\/strong> \uc218\ud589\ud558\uace0 \ubcd1\ub82c \ud50c\ub86f\uc744 \uc0dd\uc131\ud558\uc5ec \uc6d0\ub798 \ubd84\ud3ec\uc640 \ub370\uc774\ud130\uc758 \uc81c\uacf1\uadfc \ubcc0\ud658 \ubd84\ud3ec\ub97c \ud45c\uc2dc\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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
 \uc81c\uacf1\uadfc\uc73c\ub85c \ubcc0\ud658\ub41c \ub370\uc774\ud130\uac00 \uc6d0\ub798 \ub370\uc774\ud130\ubcf4\ub2e4 \ud6e8\uc52c \ub354 \uc815\uaddc \ubd84\ud3ec\ub97c \ub530\ub974\ub294\uc9c0 \ud655\uc778\ud558\uc138\uc694.<\/span><\/p>\n
 Python\uc758 \ud050\ube0c \ub8e8\ud2b8 \ubcc0\ud658<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \ubcc0\uc218\uc5d0 \ub300\ud574 \ud050\ube0c \ub8e8\ud2b8 \ubcc0\ud658\uc744<\/strong> \uc218\ud589\ud558\uace0 \ubcd1\ub82c \ud50c\ub86f\uc744 \uc0dd\uc131\ud558\uc5ec \uc6d0\ub798 \ubd84\ud3ec\uc640 \ub370\uc774\ud130\uc758 \ud050\ube0c \ub8e8\ud2b8 \ubcc0\ud658 \ubd84\ud3ec\ub97c \ud45c\uc2dc\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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
 \ud050\ube0c \ub8e8\ud2b8\ub85c \ubcc0\ud658\ub41c \ub370\uc774\ud130\uac00 \uc6d0\ubcf8 \ub370\uc774\ud130\ubcf4\ub2e4 \ud6e8\uc52c \ub354 \uc815\uaddc \ubd84\ud3ec\ub97c \uc774\ub8e8\uace0 \uc788\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n
 Python\uc5d0\uc11c Z \uc810\uc218\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a>
 Python\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uc815\uaddc\ud654\ud558\ub294 \ubc29\ubc95<\/a>
 \ud1b5\uacc4\uc5d0\uc11c \uc815\uaddc\uc131 \uac00\uc815\uc740 \ubb34\uc5c7\uc785\ub2c8\uae4c?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub9ce\uc740 \ud1b5\uacc4 \ud14c\uc2a4\ud2b8\uc5d0\uc11c\ub294 \ub370\uc774\ud130 \uc138\ud2b8\uac00 \uc815\uaddc \ubd84\ud3ec\ub97c \ub530\ub978\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc2e4\uc81c\ub85c\ub294 \uadf8\ub807\uc9c0 \uc54a\uc740 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4. \uc774 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740 \ub2e4\uc74c \uc138 \uac00\uc9c0 \ubcc0\ud658 \uc911 \ud558\ub098\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \uc138\ud2b8\uc758 \uac12 \ubd84\ud3ec\ub97c \ubcc0\ud658\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. 1. \ub85c\uadf8 \ubcc0\ud658: \uc751\ub2f5 \ubcc0\uc218\ub97c y\uc5d0\uc11c log(y) \ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4. 2. \uc81c\uacf1\uadfc \ubcc0\ud658: \uc751\ub2f5 \ubcc0\uc218\ub97c y\uc5d0\uc11c \u221ay \ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4. 3. \uc138\uc81c\uacf1\uadfc \ubcc0\ud658: […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-2237","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPython\uc5d0\uc11c \ub370\uc774\ud130\ub97c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95(\ub85c\uadf8, \uc81c\uacf1\uadfc, \uc138\uc81c\uacf1\uadfc) - 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