<\/p>\n
Ba\u015fka bir s\u00fctundan kayd\u0131r\u0131lan de\u011ferleri g\u00f6r\u00fcnt\u00fcleyen bir s\u00fctun olu\u015fturmak i\u00e7in pandalarda Shift()<\/strong> i\u015flevini kullanabilirsiniz.<\/span><\/p>\n Bu i\u015flev a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r:<\/span><\/p>\n Shift()<\/strong> i\u015flevinin de\u011ferinin, kayd\u0131rman\u0131n hesaplanaca\u011f\u0131 de\u011ferlerin say\u0131s\u0131n\u0131 g\u00f6sterdi\u011fini unutmay\u0131n.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n Diyelim ki bir ma\u011fazan\u0131n art arda 10 g\u00fcn boyunca yapt\u0131\u011f\u0131 sat\u0131\u015flar\u0131 g\u00f6steren a\u015fa\u011f\u0131daki pandalar DataFrame’e sahibiz:<\/span><\/span><\/p>\n Her sat\u0131rda \u00f6nceki g\u00fcn\u00fcn sat\u0131\u015flar\u0131n\u0131 g\u00f6r\u00fcnt\u00fcleyen bir kayd\u0131rma s\u00fctunu olu\u015fturmak i\u00e7in Shift()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n Sonucun nas\u0131l yorumlanaca\u011f\u0131 a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n Ve benzeri.<\/span><\/p>\n \u0130stersek DataFrame’e birden fazla ofset s\u00fctunu da ekleyebilece\u011fimizi unutmay\u0131n:<\/span><\/p>\n \u0130stedi\u011finiz kadar uzak s\u00fctun eklemek i\u00e7in ayn\u0131 genel yakla\u015f\u0131m\u0131 kullanabilirsiniz.<\/span><\/span><\/p>\n Not<\/strong> : Birincil s\u00fctun olu\u015fturmak i\u00e7in Shift()<\/strong> i\u015flevindeki negatif de\u011ferleri kullanman\u0131z yeterlidir.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde pandalarda di\u011fer ortak g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Pandalar’daki s\u00fctunlar nas\u0131l silinir<\/a> Ba\u015fka bir s\u00fctundan kayd\u0131r\u0131lan de\u011ferleri g\u00f6r\u00fcnt\u00fcleyen bir s\u00fctun olu\u015fturmak i\u00e7in pandalarda Shift() i\u015flevini kullanabilirsiniz. Bu i\u015flev a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r: df[‘ lagged_col1 ‘] = df[‘ col1 ‘]. shift ( 1 ) Shift() i\u015flevinin de\u011ferinin, kayd\u0131rman\u0131n hesaplanaca\u011f\u0131 de\u011ferlerin say\u0131s\u0131n\u0131 g\u00f6sterdi\u011fini unutmay\u0131n. A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir. \u00d6rnek: Pandalarda Ofset S\u00fctunu Olu\u015fturma Diyelim ki […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-3522","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df[' lagged_col1<\/span> '] = df[' col1<\/span> ']. shift<\/span> ( 1<\/span> )<\/strong><\/pre>\n \u00d6rnek: Pandalarda Ofset S\u00fctunu Olu\u015fturma<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' day<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n ' sales<\/span> ': [18, 10, 14, 13, 19, 24, 25, 29, 15, 18]})\n#view DataFrame\n<\/span>print<\/span> (df)\n\n day sales\n0 1 18\n1 2 10\n2 3 14\n3 4 13\n4 5 19\n5 6 24\n6 7 25\n7 8 29\n8 9 15\n9 10 18\n<\/strong><\/pre>\n #add column that represents lag of sales column\n<\/span>df[' sales_previous_day<\/span> '] = df[' sales<\/span> ']. shift<\/span> ( 1<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n day sales sales_previous_day\n0 1 18 NaN\n1 2 10 18.0\n2 3 14 10.0\n3 4 13 14.0\n4 5 19 13.0\n5 6 24 19.0\n6 7 25 24.0\n7 8 29 25.0\n8 9 15 29.0\n9 10 18 15.0\n<\/strong><\/pre>\n\n
#add two lag columns\n<\/span>df[' sales_previous_day<\/span> '] = df[' sales<\/span> ']. shift<\/span> ( 1<\/span> )\ndf[' sales_previous_day2<\/span> '] = df[' sales<\/span> ']. shift<\/span> ( 2<\/span> ) \n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n day sales sales_previous_day sales_previous_day2\n0 1 18 NaN NaN\n1 2 10 18.0 NaN\n2 3 14 10.0 18.0\n3 4 13 14.0 10.0\n4 5 19 13.0 14.0\n5 6 24 19.0 13.0\n6 7 25 24.0 19.0\n7 8 29 25.0 24.0\n8 9 15 29.0 25.0\n9 10 18 15.0 29.0\n<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalar’da s\u00fctunlar nas\u0131l hari\u00e7 tutulur?<\/a>
Pandalar’da se\u00e7ilen s\u00fctunlara bir i\u015flev nas\u0131l uygulan\u0131r?
Pandas DataFrame’de s\u00fctun s\u0131ras\u0131 nas\u0131l de\u011fi\u015ftirilir?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"