{"id":3753,"date":"2023-07-15T18:16:37","date_gmt":"2023-07-15T18:16:37","guid":{"rendered":"https:\/\/statorials.org\/tr\/gunde-panda-grubu\/"},"modified":"2023-07-15T18:16:37","modified_gmt":"2023-07-15T18:16:37","slug":"gunde-panda-grubu","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/gunde-panda-grubu\/","title":{"rendered":"Pandas dataframe'de g\u00fcne g\u00f6re grupland\u0131rma (\u00f6rnekle)"},"content":{"rendered":"

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Bir pandas DataFrame’de sat\u0131rlar\u0131 g\u00fcne g\u00f6re gruplamak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n

 df. groupby<\/span> (df. your_date_column<\/span> . dt<\/span> . day<\/span> )[' values_column<\/span> ']. sum<\/span> ()\n<\/strong><\/pre>\n
 Bu \u00f6zel form\u00fcl, sat\u0131rlar\u0131 tarih_s\u00fctununuzdaki<\/strong> tarihe g\u00f6re grupland\u0131r\u0131r ve DataFrame’deki de\u011ferler_s\u00fctununa<\/strong> ili\u015fkin de\u011ferlerin toplam\u0131n\u0131 hesaplar.<\/span><\/p>\n
 dt.day()<\/strong> fonksiyonunun pandalardaki tarih s\u00fctunundan g\u00fcn\u00fc \u00e7\u0131kard\u0131\u011f\u0131n\u0131 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
 \u00d6rnek: Pandalarda g\u00fcne g\u00f6re nas\u0131l grupland\u0131r\u0131l\u0131r<\/strong><\/span><\/h2>\n
 Diyelim ki bir \u015firketin farkl\u0131 tarihlerde yapt\u0131\u011f\u0131 sat\u0131\u015flar\u0131 g\u00f6steren a\u015fa\u011f\u0131daki panda DataFrame’imiz var:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' date<\/span> ': pd. date_range<\/span> (start=' 1\/1\/2020<\/span> ', freq=' 8h<\/span> ', periods= 10<\/span> ),\n                   ' sales<\/span> ': [6, 8, 9, 11, 13, 8, 8, 15, 22, 9],\n                   ' returns<\/span> ': [0, 3, 2, 2, 1, 3, 2, 4, 1, 5]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n                 date sales returns\n0 2020-01-01 00:00:00 6 0\n1 2020-01-01 08:00:00 8 3\n2 2020-01-01 16:00:00 9 2\n3 2020-01-02 00:00:00 11 2\n4 2020-01-02 08:00:00 13 1\n5 2020-01-02 16:00:00 8 3\n6 2020-01-03 00:00:00 8 2\n7 2020-01-03 08:00:00 15 4\n8 2020-01-03 16:00:00 22 1\n9 2020-01-04 00:00:00 9 5<\/strong><\/span><\/pre>\n
 \u0130lgili:<\/strong> Pandalarda Tarih Aral\u0131\u011f\u0131 Nas\u0131l Olu\u015fturulur<\/a><\/span><\/p>\n
 G\u00fcne g\u00f6re grupland\u0131r\u0131lm\u0131\u015f sat\u0131\u015flar\u0131n toplam\u0131n\u0131 hesaplamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n
 #calculate sum of sales grouped by day\ndf. groupby<\/span> (df. date<\/span> . dt<\/span> . day<\/span> )[' sales<\/span> ']. sum<\/span> ()\n\ndate\n1 23\n2 32\n3 45\n4 9\nName: sales, dtype: int64<\/span><\/span><\/strong><\/pre>\n
 Sonucun nas\u0131l yorumlanaca\u011f\u0131 a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n