{"id":2231,"date":"2023-07-23T03:41:14","date_gmt":"2023-07-23T03:41:14","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%ad%e1%84%8c%e1%85%ae%e1%86%ab%e1%84%91%e1%85%a7%e1%86%ab%e1%84%8e%e1%85%a1\/"},"modified":"2023-07-23T03:41:14","modified_gmt":"2023-07-23T03:41:14","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%ad%e1%84%8c%e1%85%ae%e1%86%ab%e1%84%91%e1%85%a7%e1%86%ab%e1%84%8e%e1%85%a1","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%ad%e1%84%8c%e1%85%ae%e1%86%ab%e1%84%91%e1%85%a7%e1%86%ab%e1%84%8e%e1%85%a1\/","title":{"rendered":"Pandas\uc5d0\uc11c \ud45c\uc900\ud3b8\ucc28\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95: \uc608 \ud3ec\ud568"},"content":{"rendered":"

<\/p>\n

\n

DataFrame.std()<\/a> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud32c\ub354 DataFrame \uac12\uc758 \ud45c\uc900 \ud3b8\ucc28\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uc2e4\uc81c\ub85c \ud45c\uc900 \ud3b8\ucc28\ub97c \uacc4\uc0b0\ud558\ub824\uba74 \ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: \uc5f4\uc758 \ud45c\uc900 \ud3b8\ucc28 \uacc4\uc0b0<\/strong><\/span><\/p>\n

 df [<\/span> ' column_name<\/span> '<\/span> ]<\/span> .<\/span> std<\/span> (<\/span> )<\/span><\/strong><\/pre>\n
 \ubc29\ubc95 2: \uc5ec\ub7ec \uc5f4\uc758 \ud45c\uc900 \ud3b8\ucc28 \uacc4\uc0b0<\/strong><\/span><\/p>\n
 df [[<\/span> ' column_name1<\/span> ', ' column_name2<\/span> ']<\/span> ]<\/span> .<\/span> std<\/span> (<\/span> )<\/span><\/strong><\/pre>\n
 \ubc29\ubc95 3: \ubaa8\ub4e0 \uc22b\uc790 \uc5f4\uc758 \ud45c\uc900 \ud3b8\ucc28 \uacc4\uc0b0<\/strong><\/span><\/p>\n
 df .<\/span> std<\/span> (<\/span> )<\/span><\/strong><\/pre>\n
 std()<\/strong> \ud568\uc218\ub294 \ud45c\uc900 \ud3b8\ucc28\ub97c \uacc4\uc0b0\ud560 \ub54c DataFrame\uc758 NaN \uac12\uc744 \uc790\ub3d9\uc73c\ub85c \ubb34\uc2dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \ub2e4\uc74c Pandas DataFrame\uc5d0\uc11c \uac01 \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'B', 'B', 'B', 'B', 'C', 'C'],\n                   ' points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n\tteam points assists rebounds\n0 to 25 5 11\n1 to 12 7 8\n2 B 15 7 10\n3 B 14 9 6\n4 B 19 12 6\n5 B 23 9 5\n6 C 25 9 9\n7 C 29 4 12\n<\/b><\/pre>\n
 \ubc29\ubc95 1: \uc5f4\uc758 \ud45c\uc900 \ud3b8\ucc28 \uacc4\uc0b0<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 DataFrame\uc5d0\uc11c \uc5f4\uc758 \ud45c\uc900 \ud3b8\ucc28\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #calculate standard deviation of 'points' column<\/span>\ndf [<\/span> ' points<\/span> '<\/span> ]<\/span> .<\/span> std<\/span> (<\/span> )<\/span>\n\n6.158617655657106\n<\/strong><\/pre>\n
 \ud45c\uc900\ud3b8\ucc28\ub294 6.1586<\/strong> \uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \ubc29\ubc95 2: \uc5ec\ub7ec \uc5f4\uc758 \ud45c\uc900 \ud3b8\ucc28 \uacc4\uc0b0<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 DataFrame\uc5d0 \uc788\ub294 \uc5ec\ub7ec \uc5f4\uc758 \ud45c\uc900 \ud3b8\ucc28\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #calculate standard deviation of 'points' and 'rebounds' columns<\/span>\ndf[[' points<\/span> ', ' rebounds<\/span> ']]. std<\/span> ()\n\npoints 6.158618\nrebounds 2.559994\ndtype:float64<\/span><\/span><\/strong><\/pre>\n
 “\ud3ec\uc778\ud2b8” \uc5f4\uc758 \ud45c\uc900\ud3b8\ucc28\ub294 6.1586<\/strong> \uc774\uace0 “\ub9ac\ubc14\uc6b4\ub4dc” \uc5f4\uc758 \ud45c\uc900\ud3b8\ucc28\ub294 2.5599<\/strong> \uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \ubc29\ubc95 3: \ubaa8\ub4e0 \uc22b\uc790 \uc5f4\uc758 \ud45c\uc900 \ud3b8\ucc28 \uacc4\uc0b0<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 DataFrame\uc5d0 \uc788\ub294 \uac01 \uc22b\uc790 \uc5f4\uc758 \ud45c\uc900 \ud3b8\ucc28\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #calculate standard deviation of all numeric columns\ndf. std<\/span> ()\n<\/span>\npoints 6.158618\nassists 2.549510\nrebounds 2.559994\ndtype:float64\n<\/span><\/span><\/strong><\/pre>\n
 Pandas\ub294 \uc22b\uc790 \uc5f4\uc774 \uc544\ub2c8\uae30 \ub54c\ubb38\uc5d0 “\ud300” \uc5f4\uc758 \ud45c\uc900 \ud3b8\ucc28\ub97c \uacc4\uc0b0\ud558\uc9c0 \uc54a\uc558\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 Pandas\uc5d0\uc11c \uc5f4\uc758 \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas\uc5d0\uc11c \uc5f4 \uc911\uc559\uac12\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas\uc5d0\uc11c \uc5f4\uc758 \ucd5c\ub300\uac12\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/p>\n","protected":false},"excerpt":{"rendered":"
DataFrame.std() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud32c\ub354 DataFrame \uac12\uc758 \ud45c\uc900 \ud3b8\ucc28\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc2e4\uc81c\ub85c \ud45c\uc900 \ud3b8\ucc28\ub97c \uacc4\uc0b0\ud558\ub824\uba74 \ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: \uc5f4\uc758 \ud45c\uc900 \ud3b8\ucc28 \uacc4\uc0b0 df [ ‘ column_name ‘ ] . std ( ) \ubc29\ubc95 2: \uc5ec\ub7ec \uc5f4\uc758 \ud45c\uc900 \ud3b8\ucc28 \uacc4\uc0b0 df [[ ‘ column_name1 ‘, ‘ column_name2 ‘] ] . std ( […]<\/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-2231","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas\uc5d0\uc11c \ud45c\uc900\ud3b8\ucc28\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568) - 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