{"id":967,"date":"2023-07-28T03:48:36","date_gmt":"2023-07-28T03:48:36","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e6%8c%87%e6%95%b0%e7%a7%bb%e5%8b%95%e5%b9%b3%e5%9d%87\/"},"modified":"2023-07-28T03:48:36","modified_gmt":"2023-07-28T03:48:36","slug":"%e3%83%8f%e3%82%9a%e3%83%b3%e3%82%bf%e3%82%99%e3%81%ae%e6%8c%87%e6%95%b0%e7%a7%bb%e5%8b%95%e5%b9%b3%e5%9d%87","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%e6%8c%87%e6%95%b0%e7%a7%bb%e5%8b%95%e5%b9%b3%e5%9d%87\/","title":{"rendered":"Pandas \u3067\u6307\u6570\u79fb\u52d5\u5e73\u5747\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u6642\u7cfb\u5217\u5206\u6790\u3067\u306f\u3001<strong>\u79fb\u52d5\u5e73\u5747<\/strong>\u306f\u5358\u306b\u904e\u53bb\u306e\u591a\u6570\u306e\u671f\u9593\u306e\u5e73\u5747\u5024\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6307\u6570\u79fb\u52d5\u5e73\u5747\u306f\u3001<\/strong>\u6700\u8fd1\u306e\u89b3\u6e2c\u7d50\u679c\u306b\u3088\u308a\u91cd\u70b9\u3092\u7f6e\u304f\u30bf\u30a4\u30d7\u306e\u79fb\u52d5\u5e73\u5747\u3067\u3042\u308a\u3001\u6700\u8fd1\u306e\u50be\u5411\u3092\u3088\u308a\u8fc5\u901f\u306b\u628a\u63e1\u3067\u304d\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas DataFrame \u306e\u5024\u306e\u5217\u306e\u6307\u6570\u79fb\u52d5\u5e73\u5747\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><strong>\u4f8b: \u30d1\u30f3\u30c0\u306e\u6307\u6570\u79fb\u52d5\u5e73\u5747<\/strong><\/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.DataFrame({'period': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                   'sales': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19]})\n\n<span style=\"color: #008080;\">#view DataFrame<\/span> \ndf\n\n        period sales\n0 1 25\n1 2 20\n2 3 14\n3 4 16\n4 5 27\n5 6 20\n6 7 12\n7 8 15\n8 9 14\n9 10 19\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/api\/pandas.DataFrame.ewm.html\" target=\"_blank\" rel=\"noopener noreferrer\">pandas.DataFrame.ewm()<\/a>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u4ee5\u524d\u306e\u3044\u304f\u3064\u304b\u306e\u671f\u9593\u306e\u6307\u6570\u52a0\u91cd\u79fb\u52d5\u5e73\u5747\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001\u524d\u306e 4 \u3064\u306e\u671f\u9593\u3092\u4f7f\u7528\u3057\u3066\u6307\u6570\u52a0\u91cd\u79fb\u52d5\u5e73\u5747\u3092\u8a08\u7b97\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 new column to hold 4-day exponentially weighted moving average<\/span>\ndf['4dayEWM'] = df['sales']. <span style=\"color: #3366ff;\">ewm<\/span> (span= <span style=\"color: #008000;\">4<\/span> , adjust= <span style=\"color: #008000;\">False<\/span> ). <span style=\"color: #3366ff;\">mean<\/span> ()\n\n<span style=\"color: #008080;\">#view DataFrame<\/span> \ndf\n\n        period sales 4dayEWM\n0 1 25 25.000000\n1 2 20 23.000000\n2 3 14 19.400000\n3 4 16 18.040000\n4 5 27 21.624000\n5 6 20 20.974400\n6 7 12 17.384640\n7 8 15 16.430784\n8 9 14 15.458470\n9 10 19 16.875082<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">matplotlib \u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u7528\u3057\u3066\u30014 \u65e5\u9593\u306e\u6307\u6570\u52a0\u91cd\u79fb\u52d5\u5e73\u5747\u306b\u5bfe\u3059\u308b\u58f2\u4e0a\u3092\u8996\u899a\u5316\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: #107d3f;\">import<\/span> matplotlib.pyplot <span style=\"color: #107d3f;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#plot sales and 4-day exponentially weighted moving average<\/span>\nplt. <span style=\"color: #3366ff;\">plot<\/span> (df['sales'], label='Sales')\nplt. <span style=\"color: #3366ff;\">plot<\/span> (df['4dayEWM'], label='4-day EWM')\n\n<span style=\"color: #008080;\">#add legend to plot<\/span>\nplt. <span style=\"color: #3366ff;\">legend<\/span> (loc=2)\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-10144 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/ewmpandas1.png\" alt=\"\u30d1\u30f3\u30c0\u306e\u6307\u6570\u52a0\u91cd\u79fb\u52d5\u5e73\u5747\" width=\"464\" height=\"318\" srcset=\"\" sizes=\"auto, \"><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n<p><a href=\"https:\/\/statorials.org\/ja\/\u79fb\u52d5\u5e73\u5747python\/\" target=\"_blank\" rel=\"noopener noreferrer\">Python \u3067\u79fb\u52d5\u5e73\u5747\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u5e73\u5747\u7684\u306a\u30cf\u309a\u30f3\u30bf\u3099\u306e\u5217\/\" target=\"_blank\" rel=\"noopener noreferrer\">Pandas \u3067\u5217\u306e\u5e73\u5747\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u81ea\u5df1\u76f8\u95a2python\/\" target=\"_blank\" rel=\"noopener noreferrer\">Python \u3067\u81ea\u5df1\u76f8\u95a2\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6642\u7cfb\u5217\u5206\u6790\u3067\u306f\u3001\u79fb\u52d5\u5e73\u5747\u306f\u5358\u306b\u904e\u53bb\u306e\u591a\u6570\u306e\u671f\u9593\u306e\u5e73\u5747\u5024\u3067\u3059\u3002 \u6307\u6570\u79fb\u52d5\u5e73\u5747\u306f\u3001\u6700\u8fd1\u306e\u89b3\u6e2c\u7d50\u679c\u306b\u3088\u308a\u91cd\u70b9\u3092\u7f6e\u304f\u30bf\u30a4\u30d7\u306e\u79fb\u52d5\u5e73\u5747\u3067\u3042\u308a\u3001\u6700\u8fd1\u306e\u50be\u5411\u3092\u3088\u308a\u8fc5\u901f\u306b\u628a\u63e1\u3067\u304d\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002 \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001pandas [&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-967","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>Pandas \u3067\u6307\u6570\u79fb\u52d5\u5e73\u5747\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/title>\n<meta name=\"description\" content=\"pandas \u3067\u6307\u6570\u79fb\u52d5\u5e73\u5747\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u4f8b\u3068\u3068\u3082\u306b\u7c21\u5358\u306b\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 rel=\"canonical\" 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