<\/p>\n
Bir pandas DataFrame’de belirli bir h\u00fccrenin de\u011ferini ayarlamak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler, bu s\u00f6zdiziminin pratikte a\u015fa\u011f\u0131daki pandalar DataFrame ile nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, “puan” s\u00fctununun 3. dizin konumunun de\u011ferinin 99’a nas\u0131l ayarlanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n “Puan” s\u00fctununun 3. indeks konumundaki de\u011ferin de\u011fi\u015ftirildi\u011fini ve DataFrame’deki di\u011fer t\u00fcm de\u011ferlerin ayn\u0131 kald\u0131\u011f\u0131n\u0131 unutmay\u0131n.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, bir aral\u0131ktaki birden \u00e7ok h\u00fccrenin de\u011ferinin ayn\u0131 anda nas\u0131l ayarlanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, yaln\u0131zca<\/em> puan s\u00fctunundaki de\u011fer 20’den b\u00fcy\u00fckse “ribaund” s\u00fctunundaki de\u011ferlerin 99’a nas\u0131l ayarlanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Puan s\u00fctunundaki de\u011fer 20’den b\u00fcy\u00fckse ribaund s\u00fctunundaki her de\u011ferin 99 olarak de\u011fi\u015ftirildi\u011fini unutmay\u0131n.<\/span><\/p>\n Di\u011fer t\u00fcm de\u011ferler ayn\u0131 kald\u0131.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde pandalarda di\u011fer ortak i\u015flevlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Pandas DataFrame’den h\u00fccre de\u011feri nas\u0131l al\u0131n\u0131r<\/a> Bir pandas DataFrame’de belirli bir h\u00fccrenin de\u011ferini ayarlamak i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz: #set value at row index 0 and column ‘col_name’ to be 99 df. at [0, ‘ col_name ‘] = 99 A\u015fa\u011f\u0131daki \u00f6rnekler, bu s\u00f6zdiziminin pratikte a\u015fa\u011f\u0131daki pandalar DataFrame ile nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir: import pandas as pd #createDataFrame df = pd. DataFrame ({‘ […]<\/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-2097","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n #set value at row index 0 and column 'col_name' to be 99<\/span>\ndf. at<\/span> [0, ' col_name<\/span> '] = 99\n<\/strong><\/pre>\n import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#view DataFrame\n<\/span>df\n\n\tpoints assists rebounds\n0 25 5 11\n1 12 7 8\n2 15 7 10\n3 14 9 6\n4 19 12 6\n5 23 9 5\n6 25 9 9\n7 29 4 12<\/strong><\/pre>\n \u00d6rnek 1: Pandalar’da bir h\u00fccrenin de\u011ferini ayarlama<\/strong><\/span><\/h3>\n
#set value in 3rd index position and 'points' column to be 99<\/span>\ndf. at<\/span> [3, ' points<\/span> '] = 99\n\n#view updated DataFrame<\/span>\ndf\n\n points assists rebounds\n0 25 5 11\n1 12 7 8\n2 15 7 10\n3 99 9 6\n4 19 12 6\n5 23 9 5\n6 25 9 9\n7 29 4 12\n<\/strong><\/pre>\n \u00d6rnek 2: Pandalar’da birden \u00e7ok h\u00fccrenin de\u011ferini ayarlama<\/strong><\/span><\/h3>\n
#set values in index positions 0 to 3 in 'points' column to be 99<\/span> \ndf. at<\/span> [0:3, ' points<\/span> '] = 99\n\n#view updated DataFrame<\/span>\ndf\n\n<\/span>points assists rebounds\n0 99 5 11\n1 99 7 8\n2 99 7 10\n3 99 9 6\n4 19 12 6\n5 23 9 5\n6 25 9 9\n7 29 4 12\n<\/strong><\/pre>\n \u00d6rnek 3: Pandalarda De\u011ferleri Ko\u015fullu Olarak Ayarlay\u0131n<\/strong><\/h3>\n
#set values in 'rebounds' column to be 99 if value in points column is greater than 20\ndf. loc<\/span> [df[' points<\/span> ']>20, [' rebounds<\/span> ']] = 99<\/span>\n\n#view updated DataFrame\ndf<\/span>\n\npoints assists rebounds\n0 25 5 99\n1 12 7 8\n2 15 7 10\n3 14 9 6\n4 19 12 6\n5 23 9 99\n6 25 9 99\n7 29 4 99\n<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
Pandas DataFrame’in ilk sat\u0131r\u0131 nas\u0131l al\u0131n\u0131r<\/a>
Pandas DataFrame’den ilk s\u00fctun nas\u0131l al\u0131n\u0131r<\/a>
Pandalarda s\u00fctunu de\u011ferle e\u015fle\u015fen sat\u0131rlar\u0131n dizini nas\u0131l al\u0131n\u0131r?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"