<\/p>\n
pandas application()<\/a> i\u015flevi, bir pandas DataFrame’in sat\u0131rlar\u0131na veya s\u00fctunlar\u0131na bir i\u015flev uygulamak i\u00e7in kullan\u0131labilir.<\/span><\/p>\n Bu i\u015flev, yerinde arg\u00fcman sa\u011flayan drop()<\/strong> ve replacement()<\/strong> gibi di\u011fer i\u015flevlerden farkl\u0131d\u0131r:<\/span><\/p>\n Apply()<\/strong> fonksiyonunun bir inplace arg\u00fcman\u0131 yoktur, dolay\u0131s\u0131yla bir inplace DataFrame’i d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanmam\u0131z gerekir:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler, bu s\u00f6zdiziminin pratikte a\u015fa\u011f\u0131daki pandalar DataFrame ile nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, bir veri \u00e7er\u00e7evesi s\u00fctununu yerinde d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in application()<\/strong> i\u015flevinin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, birden \u00e7ok veri \u00e7er\u00e7evesi s\u00fctununu yerinde d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in application() i\u015flevinin<\/strong> nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, veri \u00e7er\u00e7evesindeki t\u00fcm s\u00fctunlar\u0131 yerinde d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in application() i\u015flevinin<\/strong> nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde pandalarda di\u011fer ortak i\u015flevlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Pandalar’da s\u00fctunlar\u0131n toplam\u0131 nas\u0131l hesaplan\u0131r?<\/a> pandas application() i\u015flevi, bir pandas DataFrame’in sat\u0131rlar\u0131na veya s\u00fctunlar\u0131na bir i\u015flev uygulamak i\u00e7in kullan\u0131labilir. Bu i\u015flev, yerinde arg\u00fcman sa\u011flayan drop() ve replacement() gibi di\u011fer i\u015flevlerden farkl\u0131d\u0131r: df. drop ([‘ column1 ‘], inplace= True ) df. rename ({‘ old_column ‘: ‘ new_column ‘}, inplace= True ) Apply() fonksiyonunun bir inplace arg\u00fcman\u0131 yoktur, dolay\u0131s\u0131yla bir inplace DataFrame’i […]<\/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-2095","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df. drop<\/span> ([' column1<\/span> '], inplace= True<\/span> )\n\ndf. rename<\/span> ({' old_column<\/span> ': ' new_column<\/span> '}, inplace= True<\/span> )\n<\/strong><\/pre>\n df = df. apply<\/span> ( lambda<\/span> x: x* 2<\/span> )<\/strong><\/pre>\n import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#view DataFrame\n<\/span>df\n\n points assists rebounds\n0 25 5 11\n1 12 7 8\n2 15 7 10\n3 14 9 6\n4 19 12 6\n5 23 9 5\n6 25 9 9\n7 29 4 12\n<\/strong><\/pre>\n \u00d6rnek 1: Bir s\u00fctun i\u00e7in application() i\u015flevini kullan\u0131n<\/strong><\/span><\/h3>\n
#multiply all values in 'points' column by 2 in place\n<\/span>df. loc<\/span> [:, ' points<\/span> '] = df. points<\/span> . apply<\/span> ( lambda<\/span> x: x* 2<\/span> )\n\n#view updated DataFrame<\/span>\ndf\n\npoints assists rebounds\n0 50 5 11\n1 24 7 8\n2 30 7 10\n3 28 9 6\n4 38 12 6\n5 46 9 5\n6 50 9 9\n7 58 4 12\n<\/strong><\/pre>\n \u00d6rnek 2: Birden \u00e7ok s\u00fctun i\u00e7in application() i\u015flevini yerinde kullan\u0131n<\/strong><\/span><\/h3>\n
multiply all values in 'points' and 'rebounds' column by 2 in place<\/span>\ndf[[' points<\/span> ', ' rebounds<\/span> ']] = df[[' points<\/span> ', ' rebounds<\/span> ']]. apply<\/span> ( lambda<\/span> x: x* 2<\/span> )\n\n#view updated DataFrame\n<\/span>df\n\n\tpoints assists rebounds\n0 50 5 22\n1 24 7 16\n2 30 7 20\n3 28 9 12\n4 38 12 12\n5 46 9 10\n6 50 9 18\n7 58 4 24\n<\/strong><\/pre>\n \u00d6rnek 3: T\u00fcm s\u00fctunlar i\u00e7in application() i\u015flevini yerinde kullan\u0131n<\/strong><\/span><\/h3>\n
#multiply values in all columns by 2\n<\/span>df = df. apply<\/span> ( lambda<\/span> x: x* 2<\/span> )\n\n#view updated DataFrame<\/span>\ndf\n\n\tpoints assists rebounds\n0 50 10 22\n1 24 14 16\n2 30 14 20\n3 28 18 12\n4 38 24 12\n5 46 18 10\n6 50 18 18\n7 58 8 24\n<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
Pandalar’daki s\u00fctunlar\u0131n ortalamas\u0131 nas\u0131l hesaplan\u0131r?<\/a>
Pandalarda s\u00fctunlar\u0131n maksimum de\u011feri nas\u0131l bulunur?<\/p>\n","protected":false},"excerpt":{"rendered":"