<\/p>\n
Pandalarda groupby()<\/strong> i\u015flevindeki s\u00fctunlar\u0131 yeniden adland\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu \u00f6zel \u00f6rnek, \u00fc\u00e7 toplam s\u00fctunu hesaplar ve bunlar\u0131 sum_col1<\/strong> , Mean_col2<\/strong> ve max_col3<\/strong> olarak adland\u0131r\u0131r.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame:<\/span><\/p>\n Sat\u0131rlar\u0131 ekip<\/strong> s\u00fctununa g\u00f6re gruplamak ve ard\u0131ndan toplu s\u00fctunlar i\u00e7in belirli adlar sa\u011flarken \u00fc\u00e7 toplam s\u00fctunu hesaplamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Birle\u015ftirilmi\u015f \u00fc\u00e7 s\u00fctunun, agg()<\/strong> i\u015flevinde sa\u011flad\u0131\u011f\u0131m\u0131z \u00f6zel adlara sahip oldu\u011funu unutmay\u0131n.<\/span><\/p>\n Ayr\u0131ca istersek agg()<\/strong> fonksiyonundaki toplam, ortalama ve maksimum de\u011ferleri hesaplamak i\u00e7in NumPy fonksiyonlar\u0131n\u0131 kullanabilece\u011fimizi de unutmay\u0131n.<\/span><\/p>\n Bu sonu\u00e7lar \u00f6nceki \u00f6rne\u011fin sonu\u00e7lar\u0131na kar\u015f\u0131l\u0131k gelir.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde pandalarda di\u011fer yayg\u0131n i\u015flemlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Pandalarda t\u00fcm s\u00fctun adlar\u0131 nas\u0131l listelenir?<\/a> Pandalarda groupby() i\u015flevindeki s\u00fctunlar\u0131 yeniden adland\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df. groupby (‘ group_col ‘). agg (sum_col1=(‘ col1 ‘, ‘ sum ‘), mean_col2=(‘ col2 ‘, ‘ mean ‘), max_col3=(‘ col3 ‘, ‘ max ‘)) Bu \u00f6zel \u00f6rnek, \u00fc\u00e7 toplam s\u00fctunu hesaplar ve bunlar\u0131 sum_col1 , Mean_col2 ve max_col3 olarak adland\u0131r\u0131r. A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin […]<\/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-3567","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df. groupby<\/span> (' group_col<\/span> '). agg<\/span> (sum_col1=(' col1<\/span> ', ' sum<\/span> '),\n mean_col2=(' col2<\/span> ', ' mean<\/span> '),\n max_col3=(' col3<\/span> ', ' max<\/span> '))\n<\/strong><\/pre>\n \u00d6rnek: Pandalar’daki Groupby i\u015flevindeki s\u00fctunlar\u0131 yeniden adland\u0131r\u0131n<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n ' points<\/span> ': [30, 22, 19, 14, 14, 11, 20, 28],\n ' assists<\/span> ': [5, 6, 6, 5, 8, 7, 7, 9],\n ' rebounds<\/span> ': [4, 13, 15, 10, 7, 7, 5, 11]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n team points assists rebounds\n0 to 30 5 4\n1 to 22 6 13\n2 A 19 6 15\n3 A 14 5 10\n4 B 14 8 7\n5 B 11 7 7\n6 B 20 7 5\n7 B 28 9 11\n<\/span><\/span><\/strong><\/pre>\n #calculate several aggregated columns by group and rename aggregated columns\n<\/span>df. groupby<\/span> (' team<\/span> '). agg<\/span> (sum_points=(' points<\/span> ', ' sum<\/span> '),\n mean_assists=(' assists<\/span> ', ' mean<\/span> '),\n max_rebounds=(' rebounds<\/span> ', ' max<\/span> '))\n\n\tsum_points mean_assists max_rebounds\nteam\t\t\t\nA 85 5.50 15\nB 73 7.75 11\n<\/span><\/span><\/strong><\/pre>\n import<\/span> numpy as<\/span> np<\/span>\n\n#calculate several aggregated columns by group and rename aggregated columns\n<\/span>df. groupby<\/span> (' team<\/span> '). agg<\/span> (sum_points=(' points<\/span> ', np. sum<\/span> ),\n mean_assists=(' assists<\/span> ', np. mean<\/span> ),\n max_rebounds=(' rebounds<\/span> ', np. max<\/span> ))\n\n\tsum_points mean_assists max_rebounds\nteam\t\t\t\nA 85 5.50 15\nB 73 7.75 11<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandalar’da s\u00fctunlar ada g\u00f6re nas\u0131l s\u0131ralan\u0131r<\/a>
Pandalar’da yinelenen s\u00fctunlar nas\u0131l kald\u0131r\u0131l\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"