<\/p>\n
Bir pandas DataFrame’de grup kayd\u0131rmal\u0131 de\u011ferleri hesaplamak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz:<\/span><\/p>\n Y\u00f6ntem 1: Uzakl\u0131\u011f\u0131 bir gruba g\u00f6re hesaplama<\/strong><\/span><\/p>\n Y\u00f6ntem 2: Birden \u00e7ok gruba g\u00f6re uzakl\u0131\u011f\u0131 hesaplama<\/strong><\/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 \u00f6rnekler her y\u00f6ntemin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir.<\/span><\/p>\n Diyelim ki iki ma\u011fazan\u0131n birbirini takip eden g\u00fcnlerde yapt\u0131\u011f\u0131 sat\u0131\u015flar\u0131 g\u00f6steren a\u015fa\u011f\u0131daki panda DataFrame’imiz var:<\/span><\/p>\n Her ma\u011faza i\u00e7in \u00f6nceki g\u00fcn\u00fcn sat\u0131\u015flar\u0131n\u0131 g\u00f6r\u00fcnt\u00fcleyen bir mahsup s\u00fctunu olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini 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 \u0130ki ma\u011fazan\u0131n \u00e7al\u0131\u015fanlar\u0131n\u0131n birbirini takip eden g\u00fcnlerde yapt\u0131\u011f\u0131 sat\u0131\u015flar\u0131 g\u00f6steren a\u015fa\u011f\u0131daki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Her ma\u011fazadaki her \u00e7al\u0131\u015fan i\u00e7in \u00f6nceki g\u00fcn\u00fcn sat\u0131\u015flar\u0131n\u0131 g\u00f6r\u00fcnt\u00fcleyen bir mahsup s\u00fctunu olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Yeni lagged_sales<\/strong> s\u00fctunu, her ma\u011fazadaki her \u00e7al\u0131\u015fan i\u00e7in \u00f6nceki g\u00fcn\u00fcn sat\u0131\u015flar\u0131n\u0131 g\u00f6r\u00fcnt\u00fcler.<\/span><\/p>\n Not<\/strong> : Bu \u00f6rnekte iki s\u00fctuna g\u00f6re grupland\u0131rma yapt\u0131k ancak groupby()<\/strong> i\u015flevine istedi\u011finiz kadar de\u011fi\u015fken ad\u0131 ekleyerek istedi\u011finiz kadar s\u00fctuna g\u00f6re grupland\u0131rabilirsiniz.<\/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> Bir pandas DataFrame’de grup kayd\u0131rmal\u0131 de\u011ferleri hesaplamak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz: Y\u00f6ntem 1: Uzakl\u0131\u011f\u0131 bir gruba g\u00f6re hesaplama df[‘ lagged_values ‘] = df. groupby ([‘ group ‘])[‘ values ‘]. shift ( 1 ) Y\u00f6ntem 2: Birden \u00e7ok gruba g\u00f6re uzakl\u0131\u011f\u0131 hesaplama df[‘ lagged_values ‘] = df. groupby ([‘ group1 ‘, ‘ group2 ‘])[‘ values ‘]. […]<\/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-3523","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df[' lagged_values<\/span> '] = df. groupby<\/span> ([' group<\/span> '])[' values<\/span> ']. shift<\/span> ( 1<\/span> )<\/strong><\/span><\/pre>\n df[' lagged_values<\/span> '] = df. groupby<\/span> ([' group1<\/span> ', ' group2<\/span> '])[' values<\/span> ']. shift<\/span> ( 1<\/span> )<\/strong><\/span><\/pre>\n \u00d6rnek 1: Bir gruba g\u00f6re ofseti hesaplama<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' store<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n ' sales<\/span> ': [18, 10, 14, 13, 19, 24, 25, 29]})\n#view DataFrame\n<\/span>print<\/span> (df)\n\n blind sales\n0 to 18\n1 to 10\n2 to 14\n3 to 13\n4 B 19\n5 B 24\n6 B 25\n7 B 29<\/strong><\/pre>\n #add column that displays lag of sales column by store\n<\/span>df[' lagged_sales<\/span> '] = df. groupby<\/span> ([' store<\/span> '])[' sales<\/span> ']. shift<\/span> ( 1<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n store sales lagged_sales\n0 to 18 NaN\n1 to 10 18.0\n2 A 14 10.0\n3 A 13 14.0\n4 B 19 NaN\n5 B 24 19.0\n6 B 25 24.0\n7 B 29 25.0\n<\/strong><\/pre>\n\n
\u00d6rnek 2: \u00c7oklu gruplara g\u00f6re uzakl\u0131\u011f\u0131 hesaplama<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' store<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n ' employee<\/span> ':['O', 'O', 'R', 'R', 'O', 'O', 'R', 'R'],\n ' sales<\/span> ': [18, 10, 14, 13, 19, 24, 25, 29]})\n#view DataFrame\n<\/span>print<\/span> (df)\n\n store employee sales\n0 AO 18\n1 AO 10\n2 AR 14\n3 AR 13\n4 BO 19\n5 BO 24\n6 BR 25\n7 BR 29\n<\/strong><\/pre>\n #add column that displays lag of sales column by store and employee\n<\/span>df[' lagged_sales<\/span> '] = df. groupby<\/span> ([' store<\/span> ',' employee<\/span> '])[' sales<\/span> ']. shift<\/span> ( 1<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n store employee sales lagged_sales\n0 AO 18 NaN\n1 AO 10 18.0\n2 AR 14 NaN\n3 AR 13 14.0\n4 BO 19 NaN\n5 BO 24 19.0\n6 BR 25 NaN\n7 BR 29 25.0\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":"