{"id":3005,"date":"2023-07-19T16:28:39","date_gmt":"2023-07-19T16:28:39","guid":{"rendered":"https:\/\/statorials.org\/tr\/pandalar-veri-cercevesine-gruplandiriliyor\/"},"modified":"2023-07-19T16:28:39","modified_gmt":"2023-07-19T16:28:39","slug":"pandalar-veri-cercevesine-gruplandiriliyor","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/pandalar-veri-cercevesine-gruplandiriliyor\/","title":{"rendered":"Pandas groupby \u00e7\u0131kt\u0131s\u0131n\u0131 dataframe'e d\u00f6n\u00fc\u015ft\u00fcrme"},"content":{"rendered":"

<\/p>\n

\n

Bu e\u011fitimde, bir panda GroupBy \u00e7\u0131kt\u0131s\u0131n\u0131n bir pandas DataFrame’e nas\u0131l d\u00f6n\u00fc\u015ft\u00fcr\u00fclece\u011fi a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n

\u00d6rnek: Pandas GroupBy \u00e7\u0131kt\u0131s\u0131n\u0131 DataFrame’e d\u00f6n\u00fc\u015ft\u00fcrme<\/strong><\/span><\/h3>\n

Farkl\u0131 tak\u0131mlardaki basketbol oyuncular\u0131n\u0131n att\u0131\u011f\u0131 say\u0131lar\u0131 g\u00f6steren a\u015fa\u011f\u0131daki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\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                   ' position<\/span> ': ['G', 'G', 'F', 'C', 'G', 'F', 'F', 'F'],\n                   ' points<\/span> ': [5, 7, 7, 10, 12, 22, 15, 10]})\n\n#view DataFrame<\/span>\nprint<\/span> (df)\n\n  team position points\n0 AG 5\n1 AG 7\n2AF 7\n3 AC 10\n4 BG 12\n5 BF 22\n6 BF 15\n7 BF 10\n<\/strong><\/span><\/pre>\n
 Tak\u0131ma<\/strong> ve pozisyona<\/strong> g\u00f6re grupland\u0131r\u0131lm\u0131\u015f oyuncu say\u0131s\u0131n\u0131 saymak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n
 #count number of players, grouped by team and position\ngroup = df. groupby<\/span> ([' team<\/span> ', ' position<\/span> ']). size<\/span> ()\n<\/span>\n#viewoutput\nprint<\/span> (group)\n\nteam position\nAC 1\n      F 1\n      G2\nBF 3\n      G 1\ndtype: int64\n<\/span><\/span><\/strong><\/pre>\n
 \u00c7\u0131kt\u0131da tak\u0131ma<\/strong> ve pozisyona<\/strong> g\u00f6re grupland\u0131r\u0131lm\u0131\u015f toplam oyuncu say\u0131s\u0131n\u0131 g\u00f6rebiliriz.<\/span><\/p>\n
 Ancak \u00e7\u0131kt\u0131m\u0131z\u0131n her sat\u0131rda tak\u0131m ad\u0131n\u0131 \u015fu \u015fekilde g\u00f6r\u00fcnt\u00fclemesini istedi\u011fimizi varsayal\u0131m:<\/span><\/p>\n
 team position count\n0 AC 1\n1 AF 1\n2 AG 2\n3 BF 3\n4 BG 1\n<\/strong><\/span><\/pre>\n
 Bu sonuca ula\u015fmak i\u00e7in GroupBy’yi \u00e7al\u0131\u015ft\u0131r\u0131rken basit\u00e7e reset_index()<\/strong> y\u00f6ntemini kullanabiliriz:<\/span><\/p>\n
 #count number of players, grouped by team and position\n<\/span>df_out = df. groupby<\/span> ([' team<\/span> ', ' position<\/span> ']). size<\/span> (). reset_index<\/span> (name=' count<\/span> ')\n\n#viewoutput\n<\/span>print<\/span> (df_out)\n\n  team position count\n0 AC 1\n1 AF 1\n2 AG 2\n3 BF 3\n4 BG 1\n<\/strong><\/span><\/pre>\n
 \u00c7\u0131kt\u0131 art\u0131k istenen formatta g\u00f6r\u00fcn\u00fcr.<\/span><\/span><\/p>\n
 reset_index()<\/strong> i\u015flevindeki ad<\/strong> ba\u011f\u0131ms\u0131z de\u011fi\u015fkeninin GroupBy taraf\u0131ndan \u00fcretilen yeni s\u00fctunun ad\u0131n\u0131 belirtti\u011fini unutmay\u0131n.<\/span><\/p>\n
 Sonucun ger\u00e7ekten bir pandas DataFrame oldu\u011funu da do\u011frulayabiliriz:<\/span><\/span><\/p>\n
 #display object type of df_out\n<\/span>type<\/span> (df_out)\n\npandas.core.frame.DataFrame\n<\/strong><\/span><\/pre>\n
 Not<\/strong> : Pandalarda GroupBy i\u015fleminin tam belgelerini burada<\/a> bulabilirsiniz.<\/span><\/p>\n
 Ek kaynaklar<\/strong><\/span><\/h3>\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
 Pandalar: Grup ba\u015f\u0131na k\u00fcm\u00fclatif toplam nas\u0131l hesaplan\u0131r?<\/a>
 Pandalar: benzersiz de\u011ferlerin gruba g\u00f6re nas\u0131l say\u0131laca\u011f\u0131<\/a>
 Pandalar: gruba g\u00f6re korelasyon nas\u0131l hesaplan\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
Bu e\u011fitimde, bir panda GroupBy \u00e7\u0131kt\u0131s\u0131n\u0131n bir pandas DataFrame’e nas\u0131l d\u00f6n\u00fc\u015ft\u00fcr\u00fclece\u011fi a\u00e7\u0131klanmaktad\u0131r. \u00d6rnek: Pandas GroupBy \u00e7\u0131kt\u0131s\u0131n\u0131 DataFrame’e d\u00f6n\u00fc\u015ft\u00fcrme Farkl\u0131 tak\u0131mlardaki basketbol oyuncular\u0131n\u0131n att\u0131\u011f\u0131 say\u0131lar\u0131 g\u00f6steren a\u015fa\u011f\u0131daki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m: import pandas as pd #createDataFrame df = pd. DataFrame ({‘ team ‘: [‘A’, ‘A’, ‘A’, ‘A’, ‘B’, ‘B’, ‘B’, ‘B’], ‘ position ‘: [‘G’, […]<\/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-3005","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\nPandas GroupBy \u00e7\u0131kt\u0131s\u0131n\u0131 DataFrame'e d\u00f6n\u00fc\u015ft\u00fcrme - Statorials<\/title>\n<meta name=\"description\" content=\"Bu e\u011fitimde bir pandas GroupBy nesnesinin bir pandas DataFrame'e nas\u0131l d\u00f6n\u00fc\u015ft\u00fcr\u00fclece\u011fi bir \u00f6rnekle a\u00e7\u0131klanmaktad\u0131r.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/tr\/pandalar-veri-cercevesine-gruplandiriliyor\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas GroupBy \u00e7\u0131kt\u0131s\u0131n\u0131 DataFrame'e d\u00f6n\u00fc\u015ft\u00fcrme - 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