{"id":1600,"date":"2023-07-25T16:56:36","date_gmt":"2023-07-25T16:56:36","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e8%a1%8c%e9%96%93%e3%81%ae%e9%81%95%e3%81%84\/"},"modified":"2023-07-25T16:56:36","modified_gmt":"2023-07-25T16:56:36","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e8%a1%8c%e9%96%93%e3%81%ae%e9%81%95%e3%81%84","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e8%a1%8c%e9%96%93%e3%81%ae%e9%81%95%e3%81%84\/","title":{"rendered":"\u30d1\u30f3\u30c0: 2 \u3064\u306e\u7dda\u306e\u9055\u3044\u3092\u898b\u3064\u3051\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>DataFrame.diff()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001pandas DataFrame \u5185\u306e 2 \u3064\u306e\u884c\u306e\u9055\u3044\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <strong><span style=\"color: #000000;\">DataFrame.diff(\u671f\u9593=1\u3001\u8ef8=0)<\/span><\/strong><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>period:<\/strong>\u5dee\u5206\u3092\u8a08\u7b97\u3059\u308b\u524d\u306e\u884c\u306e\u6570\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u8ef8:<\/strong>\u884c (0) \u307e\u305f\u306f\u5217 (1) \u306e\u9055\u3044\u3092\u898b\u3064\u3051\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u95a2\u6570\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 1: \u524d\u306e\u5404\u884c\u9593\u306e\u9055\u3044\u3092\u898b\u3064\u3051\u308b<\/strong><\/span><\/h3>\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;\"> <strong><span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">period<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8],\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [12, 14, 15, 15, 18, 20, 19, 24],\n                   ' <span style=\"color: #ff0000;\">returns<\/span> ': [2, 2, 3, 3, 5, 4, 4, 6]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span>df\n\n\tperiod sales returns\n0 1 12 2\n1 2 14 2\n2 3 15 3\n3 4 15 3\n4 5 18 5\n5 6 20 4\n6 7 19 4\n7 8 24 6<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001DataFrame \u306e\u73fe\u5728\u306e\u5404\u884c\u3068\u524d\u306e\u884c\u306e\u9593\u306e\u9055\u3044\u3092\u898b\u3064\u3051\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#add new column to represent sales differences between each row<\/span>\ndf[' <span style=\"color: #ff0000;\">sales_diff<\/span> '] = df[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">diff<\/span> ()\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span>df\n\n        period sales returns sales_diff\n0 1 12 2 NaN\n1 2 14 2 2.0\n2 3 15 3 1.0\n3 4 15 3 0.0\n4 5 18 5 3.0\n5 6 20 4 2.0\n6 7 19 4 -1.0\n7 8 24 6 5.0\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u524d\u306e\u3044\u304f\u3064\u304b\u306e\u884c\u9593\u306e\u9055\u3044\u3082\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u305f\u3068\u3048\u3070\u3001\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u73fe\u5728\u306e\u5404\u884c\u3068 3 \u884c\u524d\u306b\u8868\u793a\u3055\u308c\u305f\u884c\u3068\u306e\u9055\u3044\u3092\u898b\u3064\u3051\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#add new column to represent sales differences between current row and 3 rows earlier<\/span>\ndf[' <span style=\"color: #ff0000;\">sales_diff<\/span> '] = df[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">diff<\/span> (periods= <span style=\"color: #008000;\">3<\/span> )\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span>df\n\n        period sales returns sales_diff\n0 1 12 2 NaN\n1 2 14 2 NaN\n2 3 15 3 NaN\n3 4 15 3 3.0\n4 5 18 5 4.0\n5 6 20 4 5.0\n6 7 19 4 4.0\n7 8 24 6 6.0<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 2: \u6761\u4ef6\u306b\u57fa\u3065\u3044\u3066\u9055\u3044\u3092\u898b\u3064\u3051\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">DataFrame \u3092\u30d5\u30a3\u30eb\u30bf\u30fc\u3057\u3066\u3001\u73fe\u5728\u306e\u884c\u3068\u524d\u306e\u884c\u306e\u5dee\u304c\u7279\u5b9a\u306e\u5024\u3088\u308a\u5c0f\u3055\u3044\u307e\u305f\u306f\u5927\u304d\u3044\u884c\u3092\u8868\u793a\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u73fe\u5728\u306e\u884c\u306e\u5024\u304c\u524d\u306e\u884c\u306e\u5024<strong>\u3088\u308a\u5c0f\u3055\u3044<\/strong>\u884c\u306e\u307f\u3092\u8fd4\u3057\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;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">period<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8],\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [12, 14, 15, 13, 18, 20, 19, 24],\n                   ' <span style=\"color: #ff0000;\">returns<\/span> ': [2, 2, 3, 3, 5, 4, 4, 6]})\n\n<span style=\"color: #008080;\">#find difference between each current row and the previous row\n<\/span>df[' <span style=\"color: #ff0000;\">sales_diff<\/span> '] = df[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">diff<\/span> ()\n\n<span style=\"color: #008080;\">#filter for rows where difference is less than zero\n<\/span>df = df[df[' <span style=\"color: #ff0000;\">sales_diff<\/span> ']&lt; <span style=\"color: #008000;\">0<\/span> ]\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span>df\n\n        period sales returns sales_diff\n3 4 13 3 -2.0\n6 7 19 4 -1.0<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n<p><a href=\"https:\/\/statorials.org\/ja\/\u5358\u4e00\u306e\u30cf\u309a\u30f3\u30bf\u3099\u3001\u8907\u6570\u306e\u5217\/\" target=\"_blank\" rel=\"noopener\">Pandas \u306e\u8907\u6570\u306e\u5217\u306e\u4e00\u610f\u306e\u5024\u3092\u898b\u3064\u3051\u308b\u65b9\u6cd5<\/a><br \/>Pandas DataFrame \u3092\u5217\u5024\u3067\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u3059\u308b\u65b9\u6cd5<br \/><a href=\"https:\/\/statorials.org\/ja\/\u30cf\u309a\u30f3\u30bf\u3099\u306f\u30a4\u30f3\u30c6\u3099\u30c3\u30af\u30b9\u306b\u3088\u3063\u3066\u884c\u3092\u9078\u629e\u3057\u307e\u3059\/\" target=\"_blank\" rel=\"noopener\">Pandas DataFrame \u3067\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u306b\u3088\u3063\u3066\u884c\u3092\u9078\u629e\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>DataFrame.diff()\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001pandas DataFrame \u5185\u306e 2 \u3064\u306e\u884c\u306e\u9055\u3044\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002 \u3053\u306e\u95a2\u6570\u306f\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002 DataFrame.diff(\u671f\u9593=1\u3001\u8ef8=0) \u91d1 [&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-1600","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: 2 \u3064\u306e\u7dda\u306e\u9055\u3044\u3092\u898b\u3064\u3051\u308b\u65b9\u6cd5<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame \u5185\u306e 2 \u3064\u306e\u884c\u306e\u9055\u3044\u3092\u898b\u3064\u3051\u308b\u65b9\u6cd5\u3092\u3044\u304f\u3064\u304b\u306e\u4f8b\u3068\u3068\u3082\u306b\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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