{"id":4201,"date":"2023-07-12T21:32:52","date_gmt":"2023-07-12T21:32:52","guid":{"rendered":"https:\/\/statorials.org\/ko\/matplotlib-%e1%84%87%e1%85%a2%e1%84%91%e1%85%a9-%e1%84%83%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8b%e1%85%a5%e1%84%80%e1%85%b3%e1%84%85%e1%85%a2%e1%86%b7\/"},"modified":"2023-07-12T21:32:52","modified_gmt":"2023-07-12T21:32:52","slug":"matplotlib-%e1%84%87%e1%85%a2%e1%84%91%e1%85%a9-%e1%84%83%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8b%e1%85%a5%e1%84%80%e1%85%b3%e1%84%85%e1%85%a2%e1%86%b7","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/matplotlib-%e1%84%87%e1%85%a2%e1%84%91%e1%85%a9-%e1%84%83%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8b%e1%85%a5%e1%84%80%e1%85%b3%e1%84%85%e1%85%a2%e1%86%b7\/","title":{"rendered":"Matplotlib\uc5d0\uc11c \ubd84\ud3ec\ub3c4\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

Python\uc5d0\uc11c \ubd84\ud3ec \ub2e4\uc774\uc5b4\uadf8\ub7a8\uc744 \ub9cc\ub4dc\ub294 \ub450 \uac00\uc9c0 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: Matplotlib\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud788\uc2a4\ud1a0\uadf8\ub7a8 \ub9cc\ub4e4\uae30<\/strong><\/span><\/p>\n

 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\nplt. hist<\/span> (data, color=' lightgreen<\/span> ', ec=' black<\/span> ', bins= 15<\/span> )\n<\/span><\/span><\/strong><\/pre>\n
 \uc0c9\uc0c1<\/strong> \uc740 \ub9c9\ub300\uc758 \ucc44\uc6b0\uae30 \uc0c9\uc0c1\uc744 \uc81c\uc5b4\ud558\uace0, ec\ub294<\/strong> \ub9c9\ub300 \uac00\uc7a5\uc790\ub9ac\uc758 \uc0c9\uc0c1\uc744 \uc81c\uc5b4\ud558\uba70, \ube48\uc740<\/strong> \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc758 \ube48 \uc218\ub97c \uc81c\uc5b4\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ubc29\ubc95 2: Seaborn\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubc00\ub3c4 \uace1\uc120\uc774 \ud3ec\ud568\ub41c \ud788\uc2a4\ud1a0\uadf8\ub7a8 \ub9cc\ub4e4\uae30<\/strong><\/span><\/p>\n
 import<\/span> seaborn as<\/span> sns\nsns. displot<\/span> (data, kde= True<\/span> , bins= 15<\/span> )\n<\/span><\/span><\/strong><\/pre>\n
 kde=True<\/strong> \ub294 \ubc00\ub3c4 \uace1\uc120\uc774 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc5d0 \uacb9\uccd0\uc838\uc57c \ud568\uc744 \uc9c0\uc815\ud569\ub2c8\ub2e4.<\/span><\/span><\/p>\n
 \ub2e4\uc74c \uc608\ub294 \uc2e4\uc81c\ub85c \uac01 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc74c NumPy \ubc30\uc5f4\uc758 \uac12 \ubd84\ud3ec\ub97c \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\n\n#make this example reproducible.\n<\/span>n.p. random<\/span> . seed<\/span> ( 1<\/span> )\n\n#create numpy array with 1000 values that follows normal dist with mean=10 and sd=2\n<\/span>data = np. random<\/span> . normal<\/span> (size= 1000<\/span> , loc= 10<\/span> , scale= 2<\/span> )\n\n#view first five values\n<\/span>data[: 5<\/span> ]\n\narray([13.24869073, 8.77648717, 8.9436565, 7.85406276, 11.73081526])\n<\/span><\/span><\/strong><\/pre>\n
 \uc608\uc81c 1: Matplotlib\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud788\uc2a4\ud1a0\uadf8\ub7a8 \ub9cc\ub4e4\uae30<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec Matplotlib\uc5d0\uc11c \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \uc0dd\uc131\ud558\uc5ec NumPy \ubc30\uc5f4\uc758 \uac12 \ubd84\ud3ec\ub97c \uc2dc\uac01\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\n#create histogram\n<\/span>plt. hist<\/span> (data, color=' lightgreen<\/span> ', ec=' black<\/span> ', bins= 15<\/span> )\n<\/span><\/span><\/strong><\/pre>\n
\"\"<\/p>\n
 x\ucd95\uc740 NumPy \ubc30\uc5f4\uc758 \uac12\uc744 \ud45c\uc2dc\ud558\uace0 y\ucd95\uc740 \uc774\ub7ec\ud55c \uac12\uc758 \ube48\ub3c4\ub97c \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 bins<\/strong> \uc778\uc218\uc5d0 \uc0ac\uc6a9\ud558\ub294 \uac12\uc774 \ud074\uc218\ub85d \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc5d0 \ub354 \ub9ce\uc740 \ub9c9\ub300\uac00 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 2: Seaborn\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubc00\ub3c4 \uace1\uc120\uc774 \ud3ec\ud568\ub41c \ud788\uc2a4\ud1a0\uadf8\ub7a8 \ub9cc\ub4e4\uae30<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec seaborn \ub370\uc774\ud130 \uc2dc\uac01\ud654 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubc00\ub3c4 \uace1\uc120\uc774<\/a> \uacb9\uccd0\uc9c4 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 import<\/span> seaborn as<\/span> sns\n<\/span>\n#create histogram with density curve overlaid\n<\/span>sns. displot<\/span> (data, kde= True<\/span> , bins= 15<\/span> )<\/span><\/span><\/strong> <\/pre>\n
\"\"<\/p>\n
 \uacb0\uacfc\ub294 \ubc00\ub3c4 \uace1\uc120\uc774 \uacb9\uccd0\uc9c4 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \ubc00\ub3c4 \uace1\uc120\uc744 \uc0ac\uc6a9\ud558\uba74 \ub2e8\uc77c \uc5f0\uc18d \uace1\uc120\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubd84\ud3ec\uc758 \ubaa8\uc591\uc744 \uc694\uc57d\ud560 \uc218 \uc788\ub2e4\ub294 \uc7a5\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \ucc28\ud2b8\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 Matplotlib\uc5d0\uc11c \ub204\uc801 \ub9c9\ub300 \ucc28\ud2b8\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a>
 Matplotlib\uc5d0\uc11c \uc0c1\ub300 \ube48\ub3c4 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a>
 Seaborn\uc5d0\uc11c \uc218\ud3c9 \ub9c9\ub300 \uadf8\ub798\ud504\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
Python\uc5d0\uc11c \ubd84\ud3ec \ub2e4\uc774\uc5b4\uadf8\ub7a8\uc744 \ub9cc\ub4dc\ub294 \ub450 \uac00\uc9c0 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95\uc774 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: Matplotlib\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud788\uc2a4\ud1a0\uadf8\ub7a8 \ub9cc\ub4e4\uae30 import matplotlib. pyplot as plt plt. hist (data, color=’ lightgreen ‘, ec=’ black ‘, bins= 15 ) \uc0c9\uc0c1 \uc740 \ub9c9\ub300\uc758 \ucc44\uc6b0\uae30 \uc0c9\uc0c1\uc744 \uc81c\uc5b4\ud558\uace0, ec\ub294 \ub9c9\ub300 \uac00\uc7a5\uc790\ub9ac\uc758 \uc0c9\uc0c1\uc744 \uc81c\uc5b4\ud558\uba70, \ube48\uc740 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc758 \ube48 \uc218\ub97c \uc81c\uc5b4\ud569\ub2c8\ub2e4. \ubc29\ubc95 2: Seaborn\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubc00\ub3c4 \uace1\uc120\uc774 \ud3ec\ud568\ub41c […]<\/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-4201","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nMatplotlib\uc5d0\uc11c \ubd84\ud3ec\ub3c4\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95 - 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 \ucc38\uace0<\/strong> : \uc5ec\uae30\uc5d0\uc11c<\/a> seaborn displot()<\/strong> \ud568\uc218\uc5d0 \ub300\ud55c \uc804\uccb4 \ubb38\uc11c\ub97c \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/span><\/p>\n