{"id":850,"date":"2023-07-28T13:14:00","date_gmt":"2023-07-28T13:14:00","guid":{"rendered":"https:\/\/statorials.org\/ja\/python%e3%81%ae%e9%a0%bb%e5%ba%a6%e8%a1%a8\/"},"modified":"2023-07-28T13:14:00","modified_gmt":"2023-07-28T13:14:00","slug":"python%e3%81%ae%e9%a0%bb%e5%ba%a6%e8%a1%a8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/python%e3%81%ae%e9%a0%bb%e5%ba%a6%e8%a1%a8\/","title":{"rendered":"Python \u3067\u5ea6\u6570\u8868\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u5ea6\u6570\u8868\u306f\u3001<\/strong>\u3055\u307e\u3056\u307e\u306a\u30ab\u30c6\u30b4\u30ea\u306e\u5ea6\u6570\u3092\u8868\u793a\u3059\u308b\u8868\u3067\u3059\u3002\u3053\u306e\u30bf\u30a4\u30d7\u306e\u30c6\u30fc\u30d6\u30eb\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u5024\u306e\u5206\u5e03\u3092\u7406\u89e3\u3059\u308b\u306e\u306b\u7279\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u983b\u5ea6\u8868\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7cfb\u5217\u306e\u4e00\u65b9\u5411\u5ea6\u6570\u8868<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">pandas \u30b7\u30ea\u30fc\u30ba\u5185\u306e\u500b\u3005\u306e\u5024\u306e\u983b\u5ea6\u3092\u898b\u3064\u3051\u308b\u306b\u306f\u3001 <strong>value_counts()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#defineSeries<\/span>\ndata = pd.Series([1, 1, 1, 2, 3, 3, 3, 3, 4, 4, 5])\n\n<span style=\"color: #008080;\">#find frequencies of each value\n<\/span>data.value_counts()\n\n3 4\n1 3\n4 2\n5 1\n2 1\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u30c7\u30fc\u30bf\u5024\u3092\u983b\u5ea6\u3067\u4e26\u3079\u66ff\u3048\u305f\u304f\u306a\u3044\u5834\u5408\u306f\u3001 <strong>sort=False<\/strong>\u5f15\u6570\u3092\u8ffd\u52a0\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>data.value_counts(sort= <span style=\"color: #008000;\">False<\/span> )\n\n1 3\n2 1\n3 4\n4 2\n5 1<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5024\u300c1\u300d\u306f\u30b7\u30ea\u30fc\u30ba\u5185\u306b<strong>3<\/strong>\u56de\u73fe\u308c\u307e\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5024\u300c2\u300d\u306f\u4e00\u9023\u306e\u4e2d\u3067<strong>1<\/strong>\u56de\u51fa\u73fe\u3057\u307e\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5024\u300c3\u300d\u306f\u30b7\u30ea\u30fc\u30ba\u5185\u3067<strong>4<\/strong>\u56de\u51fa\u73fe\u3057\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u3005\u3002<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>DataFrame<\/strong><\/span><span style=\"color: #000000;\"><strong>\u306e\u4e00\u65b9\u5411\u983b\u5ea6\u30c6\u30fc\u30d6\u30eb<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">pandas DataFrame \u306e\u983b\u5ea6\u3092\u898b\u3064\u3051\u308b\u306b\u306f\u3001\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3059\u308b<strong>crosstab<\/strong> <strong>()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u30af\u30ed\u30b9\u96c6\u8a08 (\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3001\u5217)<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>Index:<\/strong>\u30b0\u30eb\u30fc\u30d7\u5316\u3059\u308b\u5217\u306e\u540d\u524d<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u5217:<\/strong>\u983b\u5ea6\u5217\u306b\u4ed8\u3051\u308b\u540d\u524d<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001\u30af\u30e9\u30b9\u5185\u306e 10 \u4eba\u306e\u7570\u306a\u308b\u751f\u5f92\u306e\u5b66\u5e74\u3001\u5e74\u9f62\u3001\u6027\u5225\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u542b\u3080 DataFrame \u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002\u5404\u6587\u5b57\u30b0\u30ec\u30fc\u30c9\u306e\u983b\u5ea6\u3092\u78ba\u8a8d\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create data<\/span>\ndf = pd.DataFrame({'Grade': ['A','A','A','B','B', 'B', 'B', 'C', 'D', 'D '],\n                   'Age': [18, 18, 18, 19, 19, 20, 18, 18, 19, 19],\n                   'Gender': ['M','M', 'F', 'F', 'F', 'M', 'M', 'F', 'M', 'F']})\n<span style=\"color: #008080;\">\n#view data<\/span>\ndf\n\n\tGrade Age Gender\n0 to 18 m\n1 to 18 m\n2 A 18 F\n3 B 19 F\n4 B 19 F\n5 B 20 M\n6 B 18 M\n7 C 18 F\n8 D 19 M\n9 D 19 F \t  \n\n<span style=\"color: #008080;\">#find frequency of each letter grade\n<\/span>pd.crosstab(index=df[' <span style=\"color: #008000;\">Grade<\/span> '], columns=' <span style=\"color: #008000;\">count<\/span> ')\n\ncol_0 count\nGrade\t\nAt 3\nB4\nC 1\nD 2<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u30af\u30e9\u30b9\u3067<strong>3 \u4eba\u306e<\/strong>\u751f\u5f92\u304c\u300cA\u300d\u3092\u7372\u5f97\u3057\u307e\u3057\u305f\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30af\u30e9\u30b9\u5185\u3067\u300cB\u300d\u8a55\u4fa1\u3092\u53d7\u3051\u305f\u751f\u5f92\u306f<strong>4 \u540d<\/strong>\u3067\u3057\u305f\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30af\u30e9\u30b9\u5185\u3067<strong>1 \u4eba\u306e<\/strong>\u751f\u5f92\u304c\u300cC\u300d\u8a55\u4fa1\u3092\u7372\u5f97\u3057\u307e\u3057\u305f\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30af\u30e9\u30b9\u3067<strong>2 \u4eba\u306e<\/strong>\u751f\u5f92\u304c\u300cD\u300d\u8a55\u4fa1\u3092\u7372\u5f97\u3057\u307e\u3057\u305f\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u540c\u69d8\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u4ed6\u306e\u5217\u306e\u983b\u5ea6\u30ab\u30a6\u30f3\u30c8\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u3001\u5e74\u9f62\u3054\u3068\u306e\u983b\u5ea6\u3092\u898b\u3064\u3051\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>pd.crosstab(index=df[' <span style=\"color: #008000;\">Age<\/span> '], columns=' <span style=\"color: #008000;\">count<\/span> ') \n\ncol_0 count\nAge\t\n18 5\n19 4\n20 1\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">18\u6b73\u306e\u751f\u5f92\u306f<strong>5<\/strong>\u4eba\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u751f\u5f92<strong>4<\/strong>\u540d\u306f19\u6b73\u3067\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u751f\u5f92<strong>1<\/strong>\u540d\u306f20\u6b73\u3067\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5408\u8a08\u3067\u5272\u308b\u3053\u3068\u306b\u3088\u308a\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5272\u5408\u3068\u3057\u3066\u983b\u5ea6\u3092\u7c21\u5358\u306b\u8868\u793a\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define crosstab<\/span>\ntab = pd.crosstab(index=df['Age'], columns='count')\n\n<span style=\"color: #008080;\">#find proportions<\/span> \ntab\/tab.sum()\n\ncol_0 count\nAge\t\n18 0.5\n19 0.4\n20 0.1\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5b66\u751f\u306e<strong>50%<\/strong>\u306f 18 \u6b73\u3067\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5b66\u751f\u306e<strong>40%<\/strong>\u304c 19 \u6b73\u3067\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5b66\u751f\u306e<strong>10\uff05<\/strong>\u304c20\u6b73\u3067\u3059\u3002<\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>DataFrame \u306e\u53cc\u65b9\u5411\u5ea6\u6570\u30c6\u30fc\u30d6\u30eb<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u53cc\u65b9\u5411\u5ea6\u6570\u8868\u3092\u4f5c\u6210\u3057\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e 2 \u3064\u306e\u7570\u306a\u308b\u5909\u6570\u306e\u5ea6\u6570\u3092\u8868\u793a\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u3001\u5e74\u9f62\u5909\u6570\u3068\u5b66\u5e74\u5909\u6570\u306e\u53cc\u65b9\u5411\u5ea6\u6570\u8868\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>pd.crosstab(index=df[' <span style=\"color: #008000;\">Age<\/span> '], columns=df[' <span style=\"color: #008000;\">Grade<\/span> '])\n\n\nGrade A B C D\nAge\t\t\t\t\n18 3 1 1 0\n19 0 2 0 2\n20 0 1 0 0\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u3053\u306e\u30af\u30e9\u30b9\u306b\u306f\u300cA\u300d\u3092\u7372\u5f97\u3057\u305f 18 \u6b73\u306e\u751f\u5f92\u304c<strong>3<\/strong>\u4eba\u3044\u307e\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30af\u30e9\u30b9\u306b\u306f 18 \u6b73\u3067\u300cB\u300d\u8a55\u4fa1\u3092\u53d7\u3051\u305f\u751f\u5f92\u304c<strong>1<\/strong>\u4eba\u3044\u307e\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30af\u30e9\u30b9\u306b\u306f 18 \u6b73\u3067\u300cC\u300d\u8a55\u4fa1\u3092\u53d7\u3051\u305f\u751f\u5f92\u304c<strong>1<\/strong>\u4eba\u3044\u307e\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u3053\u306e\u30af\u30e9\u30b9\u3067\u300cD\u300d\u8a55\u4fa1\u3092\u53d7\u3051\u305f 18 \u6b73\u306e\u751f\u5f92\u306f<strong>0 \u4eba<\/strong>\u3067\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u3005\u3002<\/span><\/p>\n<p> <em><span style=\"color: #000000;\"><strong>crosstab()<\/strong>\u95a2\u6570\u306e\u5b8c\u5168\u306a\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306f<a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/api\/pandas.crosstab.html\" target=\"_blank\" rel=\"noopener\">\u3053\u3053\u3067<\/a>\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5ea6\u6570\u8868\u306f\u3001\u3055\u307e\u3056\u307e\u306a\u30ab\u30c6\u30b4\u30ea\u306e\u5ea6\u6570\u3092\u8868\u793a\u3059\u308b\u8868\u3067\u3059\u3002\u3053\u306e\u30bf\u30a4\u30d7\u306e\u30c6\u30fc\u30d6\u30eb\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u5024\u306e\u5206\u5e03\u3092\u7406\u89e3\u3059\u308b\u306e\u306b\u7279\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002 \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u983b\u5ea6\u8868\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002 \u7cfb\u5217\u306e\u4e00\u65b9\u5411 [&hellip;]<\/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-850","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python \u3067\u5ea6\u6570\u8868\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5 - \u7d71\u8a08<\/title>\n<meta name=\"description\" content=\"Python\u3067\u5ea6\u6570\u8868\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u308f\u304b\u308a\u3084\u3059\u304f\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\u983b\u5ea6\u8868\/\" \/>\n<meta property=\"og:locale\" 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