{"id":3837,"date":"2023-07-15T06:06:23","date_gmt":"2023-07-15T06:06:23","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%83%e1%85%ae-%e1%84%8b%e1%85%a7%e1%86%af-%e1%84%8e%e1%85%a1%e1%84%90%e1%85%b3\/"},"modified":"2023-07-15T06:06:23","modified_gmt":"2023-07-15T06:06:23","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%83%e1%85%ae-%e1%84%8b%e1%85%a7%e1%86%af-%e1%84%8e%e1%85%a1%e1%84%90%e1%85%b3","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%83%e1%85%ae-%e1%84%8b%e1%85%a7%e1%86%af-%e1%84%8e%e1%85%a1%e1%84%90%e1%85%b3\/","title":{"rendered":"Pandas dataframe\uc5d0\uc11c \ub450 \uac1c\uc758 \uc5f4\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

Pandas DataFrame\uc5d0\uc11c \ub450 \uc5f4\uc758 \uac12\uc744 \ud45c\uc2dc\ud558\ub294 \ub450 \uac00\uc9c0 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: \ub450 \uac1c\uc758 \uc5f4\uc744 \uc0b0\uc810\ub3c4\uc758 \uc810\uc73c\ub85c \ud45c\uc2dc<\/strong><\/span><\/p>\n

 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\nplt. scatter<\/span> (df[' column1<\/span> '], df[' column2<\/span> '])<\/strong><\/span><\/pre>\n
 \ubc29\ubc95 2: \ub450 \uac1c\uc758 \uc5f4\uc744 \uc120 \uadf8\ub798\ud504\uc5d0 \uc120\uc73c\ub85c \ud45c\uc2dc<\/strong><\/span><\/p>\n
 df. plot<\/span> (x=' column1<\/span> ',y=[' column2<\/span> ',' column3<\/span> '])\n<\/strong><\/span><\/pre>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uac01 \ubc29\ubc95\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 1: \uc0b0\uc810\ub3c4\uc5d0 \ub450 \uac1c\uc758 \uc5f4 \ud45c\uc2dc<\/strong><\/span><\/h2>\n
 \ub2e4\uc591\ud55c \ub18d\uad6c \uc120\uc218\uc5d0 \ub300\ud55c \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c \ub2e4\uc74c\uacfc \uac19\uc740 pandas DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n                   ' points<\/span> ': [18, 22, 19, 14, 14, 11, 20, 28],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points assists\n0 to 18 5\n1 B 22 7\n2 C 19 7\n3 D 14 9\n4 E 14 12\n5 F 11 9\n6 G 20 9\n7:28 a.m. 4<\/strong><\/span><\/pre>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec x\ucd95\uc5d0 \ud3ec\uc778\ud2b8<\/strong> \uc5f4\uc744 \ud45c\uc2dc\ud558\uace0 y\ucd95\uc5d0 \uc9c0\uc6d0<\/strong> \uc5f4\uc744 \ud45c\uc2dc\ud558\ub294 \uc0b0\uc810\ub3c4\ub97c \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\n#create scatterplot\n<\/span>plt. scatter<\/span> (df[' points<\/span> '], df[' assists<\/span> '])\n\n#add axis labels\n<\/span>plt. xlabel<\/span> (' Points<\/span> ')\nplt. ylabel<\/span> (' Assists<\/span> ')<\/strong><\/span> <\/pre>\n
\"\ud32c\ub354\ub294<\/p>\n
 X\ucd95\uc5d0\ub294 \ud3ec\uc778\ud2b8<\/strong> \uc5f4\uc758 \uac12\uc774 \ud3ec\ud568\ub418\uace0 Y\ucd95\uc5d0\ub294 \uc5b4\uc2dc\uc2a4\ud2b8<\/strong> \uc5f4\uc758 \uac12\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 2: \uc120 \uadf8\ub798\ud504\uc5d0 \ub450 \uac1c\uc758 \uc5f4 \uadf8\ub9ac\uae30<\/strong><\/span><\/h2>\n
 6\uac1c\uc758 \ub2e4\ub978 \uac8c\uc784\uc5d0\uc11c \ub18d\uad6c\ud300\uc774 \ub4dd\uc810\ud55c \uc810\uc218\uc640 \ud5c8\uc6a9\ud55c \uc810\uc218\uc5d0 \ub300\ud55c \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c \ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' game<\/span> ': [1, 2, 3, 4, 5, 6],\n                   ' points_for<\/span> ': [99, 94, 92, 90, 87, 85],\n                   ' points_against<\/span> ': [89, 76, 78, 78, 85, 87]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n   game points_for points_against\n0 1 99 89\n1 2 94 76\n2 3 92 78\n3 4 90 78\n4 5 87 85\n5 6 85 87\n<\/strong><\/span><\/pre>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uba74 x\ucd95\uc5d0 game<\/strong> \uac12\uc744 \uc0ac\uc6a9\ud558\uba74\uc11c \ud55c \uc904\uc5d0\ub294 point_for<\/strong> \uac12\uc744 \ud45c\uc2dc\ud558\uace0 \ub2e4\ub978 \uc904\uc5d0\ub294 points_against<\/strong> \uac12\uc744 \ud45c\uc2dc\ud558\ub294 \uc120\ud615 \ucc28\ud2b8\ub97c \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 #plot points_for and points_against columns on same y-axis\n<\/span>df. plot<\/span> (x=' game<\/span> ', y=[' points_for<\/span> ', ' points_against<\/span> '])<\/strong><\/span> <\/pre>\n
\"\"<\/p>\n
 \ud30c\ub780\uc0c9 \uc120\uc740 \uac01 \uac8c\uc784\uc758 points_for<\/strong> \uc5f4 \uac12\uc744 \ub098\ud0c0\ub0b4\uace0 \uc8fc\ud669\uc0c9 \uc120\uc740 \uac01 \uac8c\uc784\uc758 points_against<\/strong> \uc5f4 \uac12\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\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 Groupby \ubc0f Plot\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas\uc5d0\uc11c \uc5f4 \uac12\uc758 \ubd84\ud3ec\ub97c \uadf8\ub9ac\ub294 \ubc29\ubc95<\/a>
 \ud32c\ub354 \ud50c\ub86f\uc758 \uadf8\ub9bc \ud06c\uae30\ub97c \uc870\uc815\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
Pandas DataFrame\uc5d0\uc11c \ub450 \uc5f4\uc758 \uac12\uc744 \ud45c\uc2dc\ud558\ub294 \ub450 \uac00\uc9c0 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95\uc774 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: \ub450 \uac1c\uc758 \uc5f4\uc744 \uc0b0\uc810\ub3c4\uc758 \uc810\uc73c\ub85c \ud45c\uc2dc import matplotlib. pyplot as plt plt. scatter (df[‘ column1 ‘], df[‘ column2 ‘]) \ubc29\ubc95 2: \ub450 \uac1c\uc758 \uc5f4\uc744 \uc120 \uadf8\ub798\ud504\uc5d0 \uc120\uc73c\ub85c \ud45c\uc2dc df. plot (x=’ column1 ‘,y=[‘ column2 ‘,’ column3 ‘]) \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uac01 \ubc29\ubc95\uc744 \uc2e4\uc81c\ub85c […]<\/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-3837","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas DataFrame\uc5d0\uc11c \ub450 \uac1c\uc758 \uc5f4\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc5ec\ub7ec \uc608\uc81c\uc640 \ud568\uaed8 Pandas DataFrame\uc5d0\uc11c \ub450 \uac1c\uc758 \uc5f4\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\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\/ko\/\u1111\u1162\u11ab\u1103\u1165-\u1103\u116e-\u110b\u1167\u11af-\u110e\u1161\u1110\u1173\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas DataFrame\uc5d0\uc11c \ub450 \uac1c\uc758 \uc5f4\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95 - Statorials\" \/>\n<meta property=\"og:description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc5ec\ub7ec \uc608\uc81c\uc640 \ud568\uaed8 Pandas DataFrame\uc5d0\uc11c \ub450 \uac1c\uc758 \uc5f4\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ko\/\u1111\u1162\u11ab\u1103\u1165-\u1103\u116e-\u110b\u1167\u11af-\u110e\u1161\u1110\u1173\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-15T06:06:23+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/deux1.jpg\" \/>\n<meta name=\"author\" content=\"\ubca4\uc790\ubbfc \uc564\ub354\uc2a8\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"\ubca4\uc790\ubbfc \uc564\ub354\uc2a8\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%83%e1%85%ae-%e1%84%8b%e1%85%a7%e1%86%af-%e1%84%8e%e1%85%a1%e1%84%90%e1%85%b3\/\",\"url\":\"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%83%e1%85%ae-%e1%84%8b%e1%85%a7%e1%86%af-%e1%84%8e%e1%85%a1%e1%84%90%e1%85%b3\/\",\"name\":\"Pandas DataFrame\uc5d0\uc11c \ub450 \uac1c\uc758 \uc5f4\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95 - 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