{"id":3651,"date":"2023-07-16T08:13:17","date_gmt":"2023-07-16T08:13:17","guid":{"rendered":"https:\/\/statorials.org\/tr\/eslesen-pandalar\/"},"modified":"2023-07-16T08:13:17","modified_gmt":"2023-07-16T08:13:17","slug":"eslesen-pandalar","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/eslesen-pandalar\/","title":{"rendered":"Pandalarda corrwith() nas\u0131l kullan\u0131l\u0131r (\u00f6rneklerle)"},"content":{"rendered":"

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\u0130ki farkl\u0131 panda DataFrames’te ayn\u0131 ada sahip<\/em> say\u0131sal s\u00fctunlar aras\u0131ndaki ikili korelasyonu hesaplamak i\u00e7in pandalarda corrwith()<\/strong> i\u015flevini kullanabilirsiniz.<\/span><\/p>\n

Bu i\u015flev a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r:<\/span><\/p>\n

 df1. corrwith<\/span> (df2)<\/span><\/span>\n<\/strong><\/pre>\n
 Not<\/strong> : Bu i\u015flev, ayn\u0131 DataFrame i\u00e7indeki iki say\u0131sal s\u00fctun aras\u0131ndaki korelasyonu hesaplayan corr()<\/strong> i\u015flevinden farkl\u0131d\u0131r.<\/span><\/p>\n
 A\u015fa\u011f\u0131daki \u00f6rnek corrwith()<\/strong> fonksiyonunun pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir.<\/span><\/p>\n
 \u00d6rnek: Pandalarda corrwith() nas\u0131l kullan\u0131l\u0131r?<\/strong><\/span><\/h2>\n
 A\u015fa\u011f\u0131daki iki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#create first DataFrame\n<\/span>df1 = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F'],\n                    ' points<\/span> ': [18, 22, 29, 25, 14, 11],\n                    ' assists<\/span> ': [4, 5, 5, 4, 8, 12],\n                    ' rebounds<\/span> ': [10, 6, 4, 6, 3, 5]})\n\nprint<\/span> (df1)\n\n  team points assists rebounds\n0 to 18 4 10\n1 B 22 5 6\n2 C 29 5 4\n3 D 25 4 6\n4 E 14 8 3\n5 F 11 12 5\n\n#create second DataFrame<\/span>\ndf2 = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F'],\n                    ' points<\/span> ': [22, 25, 27, 35, 25, 20],\n                    ' assists<\/span> ': [15, 13, 8, 8, 5, 8],\n                    ' rebs<\/span> ': [4, 11, 12, 8, 7, 10]})\n\nprint<\/span> (df2)\n\n  team points assists rebs\n0 A 22 15 4\n1 B 25 13 11\n2 C 27 8 12\n3 D 35 8 8\n4 E 25 5 7\n5 F 20 8 10<\/b><\/pre>\n
 \u0130ki DataFrame’de ayn\u0131 adlara sahip say\u0131sal s\u00fctunlar aras\u0131ndaki korelasyonu hesaplamak i\u00e7in corrwith()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n
 #calculate correlation between numeric columns with same names in each DataFrame\n<\/span>df1. corrwith<\/span> (df2)\n\npoints 0.677051\nassists -0.478184\nNaN rebounds\nrebs NaN\ndtype:float64<\/span><\/span><\/strong><\/pre>\n
 Sonu\u00e7tan \u015funlar\u0131 g\u00f6rebiliriz:<\/span><\/p>\n