{"id":958,"date":"2023-07-28T04:33:25","date_gmt":"2023-07-28T04:33:25","guid":{"rendered":"https:\/\/statorials.org\/ja\/python%e3%81%ae%e5%9b%9b%e5%88%86%e4%bd%8d%e7%af%84%e5%9b%b2\/"},"modified":"2023-07-28T04:33:25","modified_gmt":"2023-07-28T04:33:25","slug":"python%e3%81%ae%e5%9b%9b%e5%88%86%e4%bd%8d%e7%af%84%e5%9b%b2","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/python%e3%81%ae%e5%9b%9b%e5%88%86%e4%bd%8d%e7%af%84%e5%9b%b2\/","title":{"rendered":"Python \u3067\u56db\u5206\u4f4d\u7bc4\u56f2\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u56db\u5206\u4f4d<\/strong>\u7bc4\u56f2 \u306f<\/strong>\u300cIQR\u300d\u3068\u547c\u3070\u308c\u308b\u3053\u3068\u304c\u3042\u308a\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4e2d\u9593 50% \u306e\u5206\u5e03\u3092\u6e2c\u5b9a\u3059\u308b\u65b9\u6cd5\u3067\u3059\u3002\u3053\u308c\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u7b2c 1 \u56db\u5206\u4f4d* (25 \u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb) \u3068\u7b2c 3 \u56db\u5206\u4f4d (75 \u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb) \u306e\u5dee\u3068\u3057\u3066\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n

\u5e78\u3044\u306a\u3053\u3068\u306b\u3001Python \u3067\u306fnumpy.percentile()<\/a>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u56db\u5206\u4f4d\u7bc4\u56f2\u3092\u8a08\u7b97\u3059\u308b\u306e\u306f\u7c21\u5358\u3067\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u3053\u306e\u6a5f\u80fd\u306e\u5b9f\u969b\u306e\u4f7f\u7528\u4f8b\u3092\u3044\u304f\u3064\u304b\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u4f8b 1: \u30c6\u30fc\u30d6\u30eb\u306e\u56db\u5206\u4f4d\u7bc4\u56f2<\/strong><\/span><\/h3>\n

\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u5358\u4e00\u306e\u30c6\u30fc\u30d6\u30eb\u5185\u306e\u5024\u306e\u56db\u5206\u4f4d\u7bc4\u56f2\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n

 import<\/span> numpy as<\/span> np\n\n#define array of data<\/span>\ndata = np.array([14, 19, 20, 22, 24, 26, 27, 30, 30, 31, 36, 38, 44, 47])\n\n#calculate interquartile range<\/span> \nq3, q1 = np. percentile<\/span> (data, [75,25])\niqr = q3 - q1\n\n#display interquartile range<\/span> \niqr\n\n12.25<\/strong><\/pre>\n
\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u56db\u5206\u4f4d\u7bc4\u56f2\u306f12.25<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u5024\u306e\u4e2d\u9593 50% \u306e\u5206\u5e03\u3067\u3059\u3002<\/span><\/p>\n
\u4f8b 2: \u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u5217\u306e\u56db\u5206\u4f4d\u7bc4\u56f2<\/strong><\/span><\/h3>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u5185\u306e\u5358\u4e00\u5217\u306e\u56db\u5206\u4f4d\u7bc4\u56f2\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\n\n#create data frame<\/span>\ndf = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],\n                   'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5],\n                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n#calculate interquartile range of values in the 'points' column<\/span>\nq75, q25 = np. percentile<\/span> (df['points'], [75,25])\niqr = q75 - q25\n\n#display interquartile range<\/span> \niqr\n\n5.75<\/strong><\/pre>\n
\u30dd\u30a4\u30f3\u30c8\u5217\u306e\u5024\u306e\u56db\u5206\u4f4d\u7bc4\u56f2\u306f5.75<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u4f8b 3: \u8907\u6570\u306e\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u5217\u306e\u56db\u5206\u4f4d\u7bc4\u56f2<\/strong><\/span><\/h3>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u5185\u306e\u8907\u6570\u306e\u5217\u306e\u56db\u5206\u4f4d\u7bc4\u56f2\u3092\u540c\u6642\u306b\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\n\n#create data frame<\/span>\ndf = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],\n                   'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5],\n                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n#define function to calculate interquartile range\n<\/span>def<\/span> find_iqr(x):\n  return<\/span> np. subtract<\/span> (*np. percentile<\/span> (x, [75, 25]))\n\n#calculate IQR for 'rating' and 'points' columns\n<\/span>df[[' rating<\/span> ', ' points<\/span> ']]. apply<\/span> (find_iqr)\n\nrating 6.75\npoints 5.75\ndtype:float64\n\n#calculate IQR for all columns\n<\/span>df. apply<\/span> (find_iqr)\n\nrating 6.75\npoints 5.75\nassists 2.50\nrebounds 3.75\ndtype:float64\n<\/strong><\/pre>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
\u56db\u5206\u4f4d\u7bc4\u56f2 (IQR) \u306f\u5916\u308c\u5024\u306e\u5f71\u97ff\u3092\u53d7\u3051\u307e\u3059\u304b?<\/a>
 Excel \u3067\u56db\u5206\u4f4d\u7bc4\u56f2 (IQR) \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a>
\u56db\u5206\u4f4d\u7bc4\u56f2\u8a08\u7b97\u30c4\u30fc\u30eb<\/p>\n","protected":false},"excerpt":{"rendered":"
\u56db\u5206\u4f4d\u7bc4\u56f2 \u306f\u300cIQR\u300d\u3068\u547c\u3070\u308c\u308b\u3053\u3068\u304c\u3042\u308a\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4e2d\u9593 50% \u306e\u5206\u5e03\u3092\u6e2c\u5b9a\u3059\u308b\u65b9\u6cd5\u3067\u3059\u3002\u3053\u308c\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u7b2c 1 \u56db\u5206\u4f4d* (25 \u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb) \u3068\u7b2c 3 \u56db\u5206\u4f4d (75 \u30d1\u30fc\u30bb\u30f3\u30bf\u30a4\u30eb) \u306e\u5dee\u3068\u3057\u3066 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-958","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nPython \u3067\u56db\u5206\u4f4d\u7bc4\u56f2\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5 - Statorials<\/title>\n<meta name=\"description\" content=\"Python\u3067\u56db\u5206\u4f4d\u7bc4\u56f2\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u7c21\u5358\u306b\u89e3\u8aac\u3057\u307e\u3059\u3002\" \/>\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\/ja\/python\u306e\u56db\u5206\u4f4d\u7bc4\u56f2\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python \u3067\u56db\u5206\u4f4d\u7bc4\u56f2\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5 - 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