{"id":4259,"date":"2023-07-12T10:38:33","date_gmt":"2023-07-12T10:38:33","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e5%90%84%e8%a1%8c%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e6%9c%80%e5%a4%a7%e5%80%a4\/"},"modified":"2023-07-12T10:38:33","modified_gmt":"2023-07-12T10:38:33","slug":"%e5%90%84%e8%a1%8c%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e6%9c%80%e5%a4%a7%e5%80%a4","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e5%90%84%e8%a1%8c%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e6%9c%80%e5%a4%a7%e5%80%a4\/","title":{"rendered":"\u30d1\u30f3\u30c0: \u5404\u884c\u306e\u6700\u5927\u5024\u3092\u898b\u3064\u3051\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u306e\u5404\u884c\u306e\u6700\u5927\u5024\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">max<\/span> '] = df. <span style=\"color: #3366ff;\">max<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u7279\u5b9a\u306e\u69cb\u6587\u306f\u3001DataFrame \u306e\u5404\u884c\u306e\u6700\u5927\u5024\u3092\u542b\u3080<strong>max<\/strong>\u3068\u3044\u3046\u65b0\u3057\u3044\u5217\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u69cb\u6587\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u4f8b: Pandas \u306e\u5404\u884c\u306e\u6700\u5927\u5024\u3092\u898b\u3064\u3051\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">points<\/span> ': [4, np.nan, 10, 2, 15, np.nan, 7, 22],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [np.nan, 3, 9, 7, 6, 8, 14, 10],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [10, 9, 4, 4, 3, 7, 10, 11]})\n    \n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   points rebound assists\n0 4.0 NaN 10\n1 NaN 3.0 9\n2 10.0 9.0 4\n3 2.0 7.0 4\n4 15.0 6.0 3\n5 NaN 8.0 7\n6 7.0 14.0 10\n7 22.0 10.0 11\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001\u5404\u884c\u306e\u6700\u5927\u5024\u3092\u542b\u3080<strong>max<\/strong>\u3068\u3044\u3046\u540d\u524d\u306e\u65b0\u3057\u3044\u5217\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#create new column that contains max value of each row\n<\/span>df[' <span style=\"color: #ff0000;\">max<\/span> '] = df. <span style=\"color: #3366ff;\">max<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   points rebounds assists max\n0 4.0 NaN 10 10.0\n1 NaN 3.0 9 9.0\n2 10.0 9.0 4 10.0\n3 2.0 7.0 4 7.0\n4 15.0 6.0 3 15.0\n5 NaN 8.0 7 8.0\n6 7.0 14.0 10 14.0\n7 22.0 10.0 11 22.0<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>max<\/strong>\u3068\u3044\u3046\u65b0\u3057\u3044\u5217\u306b\u306f\u3001\u5404\u884c\u306e\u6700\u5927\u5024\u304c\u542b\u307e\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001\u6b21\u306e\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u6700\u521d\u306e\u884c\u306e\u6700\u5927\u5024\u306f<strong>10<\/strong>\u3067\u3059\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\">2 \u884c\u76ee\u306e\u6700\u5927\u5024\u306f<strong>9<\/strong>\u3067\u3059\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\">3 \u884c\u76ee\u306e\u6700\u5927\u5024\u306f<strong>10<\/strong>\u3067\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u3005\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305f\u3001 <strong>max()<\/strong>\u95a2\u6570\u306f\u3001\u5404\u884c\u306e\u6700\u5927\u5024\u3092\u6c7a\u5b9a\u3059\u308b\u3068\u304d\u306b NaN \u5024\u3092\u81ea\u52d5\u7684\u306b\u7121\u8996\u3059\u308b\u3053\u3068\u306b\u3082\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7279\u5b9a\u306e\u5217\u306e\u307f\u306e\u5404\u884c\u306e\u6700\u5927\u5024\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u5404\u884c\u306e\u6700\u5927\u5024\u3092\u691c\u7d22\u3057\u3001<strong>\u30dd\u30a4\u30f3\u30c8\u5217<\/strong>\u3068<strong>\u30ea\u30d0\u30a6\u30f3\u30c9<\/strong>\u5217\u306e\u307f\u3092\u8003\u616e\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#add new column that contains max value of each row for points and rebounds columns\n<\/span>df[' <span style=\"color: #ff0000;\">max<\/span> '] = df[[' <span style=\"color: #ff0000;\">points<\/span> ', ' <span style=\"color: #ff0000;\">rebounds<\/span> ']]. <span style=\"color: #3366ff;\">max<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   points rebounds assists max\n0 4.0 NaN 10 4.0\n1 NaN 3.0 9 3.0\n2 10.0 9.0 4 10.0\n3 2.0 7.0 4 7.0\n4 15.0 6.0 3 15.0\n5 NaN 8.0 7 8.0\n6 7.0 14.0 10 14.0\n7 22.0 10.0 11 22.0<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>max<\/strong>\u3068\u3044\u3046\u5217\u306b\u306f\u3001<strong>\u30dd\u30a4\u30f3\u30c8<\/strong>\u5217\u3068<strong>\u30ea\u30d0\u30a6\u30f3\u30c9<\/strong>\u5217\u306e\u307f\u306e\u5404\u884c\u306e\u6700\u5927\u5024\u304c\u542b\u307e\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8<\/strong>: pandas <strong>max()<\/strong>\u95a2\u6570\u306e\u5b8c\u5168\u306a\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306f<a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.DataFrame.max.html\" target=\"_blank\" rel=\"noopener\">\u3053\u3053\u3067<\/a>\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u30d1\u30f3\u30c0\u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u30bf\u30b9\u30af\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ja\/\u5217\u4e0a\u306e\u30cf\u309a\u30f3\u30bf\u3099\u6700\u5927\u5024\/\" target=\"_blank\" rel=\"noopener\">\u30d1\u30f3\u30c0: \u8907\u6570\u306e\u5217\u306e\u6700\u5927\u5024\u3092\u898b\u3064\u3051\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306f\u6700\u5927\u884c\u3092\u8fd4\u3057\u307e\u3059\/\" target=\"_blank\" rel=\"noopener\">Pandas: \u7279\u5b9a\u306e\u5217\u306e\u6700\u5927\u5024\u3092\u6301\u3064\u884c\u3092\u8fd4\u3057\u307e\u3059<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306f-inf-\u3092-max-\u306b\u7f6e\u304d\u63db\u3048\u307e\u3059\/\" target=\"_blank\" rel=\"noopener\">\u30d1\u30f3\u30c0: inf \u3092 Max Value \u306b\u7f6e\u304d\u63db\u3048\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001pandas DataFrame \u306e\u5404\u884c\u306e\u6700\u5927\u5024\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002 df[&#8216; max &#8216;] = df. max (axis= 1 ) \u3053\u306e\u7279\u5b9a\u306e\u69cb\u6587\u306f\u3001DataFrame \u306e\u5404\u884c\u306e\u6700\u5927\u5024\u3092 [&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-4259","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>\u30d1\u30f3\u30c0: \u5404\u884c\u306e\u6700\u5927\u5024\u3092\u898b\u3064\u3051\u308b\u65b9\u6cd5 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame \u306e\u5404\u884c\u306e\u6700\u5927\u5024\u3092\u898b\u3064\u3051\u308b\u65b9\u6cd5\u3092\u4f8b\u3092\u6319\u3052\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/ja\/%e5%90%84%e8%a1%8c%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e6%9c%80%e5%a4%a7%e5%80%a4\/\",\"url\":\"https:\/\/statorials.org\/ja\/%e5%90%84%e8%a1%8c%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e6%9c%80%e5%a4%a7%e5%80%a4\/\",\"name\":\"\u30d1\u30f3\u30c0: \u5404\u884c\u306e\u6700\u5927\u5024\u3092\u898b\u3064\u3051\u308b\u65b9\u6cd5 \u2013 Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/ja\/#website\"},\"datePublished\":\"2023-07-12T10:38:33+00:00\",\"dateModified\":\"2023-07-12T10:38:33+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/ja\/#\/schema\/person\/86b92d2dd87368b26360d19d9c6a5d83\"},\"description\":\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame 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