<\/p>\n
Pandas DataFrame’de kategorik bir de\u011fi\u015fkeni say\u0131sal bir de\u011fi\u015fkene d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bir DataFrame’deki her kategorik de\u011fi\u015fkeni say\u0131sal bir de\u011fi\u015fkene d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini de kullanabilirsiniz:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame:<\/span><\/p>\n \u201cTak\u0131m\u201d s\u00fctununu say\u0131sala d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/span><\/p>\n D\u00f6n\u00fc\u015f\u00fcm \u015fu \u015fekilde ger\u00e7ekle\u015fti:<\/span><\/p>\n Tekrar a\u015fa\u011f\u0131daki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n DataFrame’deki her kategorik de\u011fi\u015fkeni say\u0131sal bir de\u011fi\u015fkene d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n \u0130ki kategorik s\u00fctunun (tak\u0131m ve pozisyon) her ikisinin de say\u0131lara d\u00f6n\u00fc\u015ft\u00fcr\u00fcld\u00fc\u011f\u00fcn\u00fc ancak puan ve ribaund s\u00fctunlar\u0131n\u0131n ayn\u0131 kald\u0131\u011f\u0131n\u0131 unutmay\u0131n.<\/span><\/p>\n Not<\/strong> : Pandas Factorize()<\/strong> i\u015flevinin tam belgelerini burada<\/a> bulabilirsiniz.<\/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 Pandas DataFrame s\u00fctunlar\u0131n\u0131 dizelere d\u00f6n\u00fc\u015ft\u00fcrme<\/a> Pandas DataFrame’de kategorik bir de\u011fi\u015fkeni say\u0131sal bir de\u011fi\u015fkene d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: df[‘ column_name ‘] = pd. factorize (df[‘ column_name ‘])[0] Bir DataFrame’deki her kategorik de\u011fi\u015fkeni say\u0131sal bir de\u011fi\u015fkene d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini de kullanabilirsiniz: #identify all categorical variables cat_columns = df. select_dtypes ([‘ object ‘]). columns #convert all categorical variables to numeric […]<\/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-2375","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n df[' column_name<\/span> '] = pd. factorize<\/span> (df[' column_name<\/span> '])[0]\n<\/strong><\/pre>\n #identify all categorical variables<\/span>\ncat_columns = df. select_dtypes<\/span> ([' object<\/span> ']). columns<\/span>\n\n#convert all categorical variables to numeric<\/span>\ndf[cat_columns] = df[cat_columns]. apply<\/span> ( lambda<\/span> x: pd.factorize<\/span> (x)[ 0<\/span> ])\n<\/b><\/pre>\n \u00d6rnek 1: Kategorik Bir De\u011fi\u015fkeni Say\u0131sala D\u00f6n\u00fc\u015ft\u00fcrme<\/strong><\/span><\/h3>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],\n ' position<\/span> ': ['G', 'G', 'F', 'G', 'F', 'C', 'G', 'F', 'C'],\n ' points<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4, 13],\n ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12, 10]})\n\n#view DataFrame\ndf\n\n<\/span>team position<\/span> points rebounds\n0 A G<\/span> 5 11\n1 A G<\/span> 7 8\n2 A F<\/span> 7 10\n3 B G<\/span> 9 6\n4 B F<\/span> 12 6\n5 B C<\/span> 9 5\n6 C G<\/span> 9 9\n7 C F<\/span> 4 12\n8 C C<\/span> 13 10\n<\/span><\/span><\/span><\/span><\/strong><\/pre>\n #convert 'team' column to numeric\n<\/span>df[' team<\/span> '] = pd. factorize<\/span> (df[' team<\/span> '])[ 0<\/span> ]<\/span>\n\n#view updated DataFrame\ndf<\/span>\n\nteam position<\/span> points rebounds\n0 0 G<\/span> 5 11\n1 0 G<\/span> 7 8\n2 0 F<\/span> 7 10\n3 1 G<\/span> 9 6\n4 1 F<\/span> 12 6\n5 1 C<\/span> 9 5\n6 2 G<\/span> 9 9\n7 2 F<\/span> 4 12\n8 2 C<\/span> 13 10\n<\/span><\/span><\/strong><\/pre>\n\n
\u00d6rnek 2: Birden \u00c7ok Kategorik De\u011fi\u015fkeni Say\u0131sal De\u011ferlere D\u00f6n\u00fc\u015ft\u00fcrme<\/strong><\/span><\/h3>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],\n ' position<\/span> ': ['G', 'G', 'F', 'G', 'F', 'C', 'G', 'F', 'C'],\n ' points<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4, 13],\n ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12, 10]})\n\n#view DataFrame\n<\/span>df\n\n team position points rebounds\n0 A G 5 11\n1 A G 7 8\n2 A F 7 10\n3 B G 9 6\n4 B F 12 6\n5 B C 9 5\n6 C G 9 9\n7 C F 4 12\n8 C C 13 10\n<\/span><\/span><\/strong><\/pre>\n #get all categorical columns\n<\/span>cat_columns = df. select_dtypes<\/span> ([' object<\/span> ']). columns<\/span>\n\n#convert all categorical columns to numeric\n<\/span>df[cat_columns] = df[cat_columns]. apply<\/span> ( lambda<\/span> x: pd.factorize<\/span> (x)[ 0<\/span> ])\n\n#view updated DataFrame\n<\/span>df\n\n\tteam position points rebounds\n0 0 0 5 11\n1 0 0 7 8\n2 0 1 7 10\n3 1 0 9 6\n4 1 1 12 6\n5 1 2 9 5\n6 2 0 9 9\n7 2 1 4 12\n8 2 2 13 10\n<\/strong><\/span><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
Pandas DataFrame s\u00fctunlar\u0131n\u0131 tam say\u0131ya d\u00f6n\u00fc\u015ft\u00fcrme<\/a>
Pandas DataFrame’de dizeleri kayan hale nas\u0131l d\u00f6n\u00fc\u015ft\u00fcrebilirim?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"