<\/p>\n
\u00c7o\u011funlukla belirli bir de\u011feri i\u00e7eren panda DataFrame’deki sat\u0131r numaralar\u0131n\u0131 almak isteyebilirsiniz.<\/span><\/p>\n Neyse ki .index<\/strong> i\u015flevini kullanarak bunu yapmak kolayd\u0131r.<\/span><\/p>\n Bu e\u011fitimde bu i\u015flevin pratik kullan\u0131m\u0131na ili\u015fkin birka\u00e7 \u00f6rnek g\u00f6sterilmektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame:<\/span><\/p>\n “Tak\u0131m”\u0131n Mavs’e e\u015fit oldu\u011fu sat\u0131r numaralar\u0131n\u0131 almak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n Dizin 0<\/strong> ve 1<\/strong> sat\u0131rlar\u0131nda tak\u0131m ad\u0131n\u0131n ‘Mavs’e e\u015fit oldu\u011funu g\u00f6rebiliriz.<\/span><\/p>\n Ayr\u0131ca belirli bir tak\u0131m ad\u0131 listesinde tak\u0131m ad\u0131n\u0131n bulundu\u011fu sat\u0131r numaralar\u0131n\u0131 da alabiliriz:<\/span><\/p>\n 0<\/strong> , 1<\/strong> ve 2<\/strong> sat\u0131r indekslerinde tak\u0131m ad\u0131n\u0131n ‘Mavs’ veya ‘Spurs’a e\u015fit oldu\u011funu g\u00f6rebiliriz.<\/span><\/p>\n A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame:<\/span><\/p>\n Tek bir sat\u0131r\u0131n belirli bir de\u011ferle e\u015fle\u015fti\u011fini biliyorsan\u0131z a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanarak bu benzersiz sat\u0131r numaras\u0131n\u0131 alabilirsiniz:<\/span><\/p>\n 3<\/strong> numaral\u0131 sat\u0131r indeksinde tak\u0131m\u0131n “Celtics” ile e\u015fit oldu\u011funu g\u00f6rebiliyoruz.<\/span><\/p>\n A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame:<\/span><\/p>\n Bir s\u00fctunun belirli bir de\u011fere e\u015fit oldu\u011fu toplam sat\u0131r say\u0131s\u0131n\u0131 bilmek istiyorsan\u0131z a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n Bu tak\u0131m\u0131n toplam 2<\/strong> hatta \u201cMavs\u201d ile e\u015fit oldu\u011funu g\u00f6rebiliyoruz.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde pandalarda di\u011fer ortak g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Pandas’ta birden \u00e7ok s\u00fctunda benzersiz de\u011ferler nas\u0131l bulunur?<\/a> \u00c7o\u011funlukla belirli bir de\u011feri i\u00e7eren panda DataFrame’deki sat\u0131r numaralar\u0131n\u0131 almak isteyebilirsiniz. Neyse ki .index i\u015flevini kullanarak bunu yapmak kolayd\u0131r. Bu e\u011fitimde bu i\u015flevin pratik kullan\u0131m\u0131na ili\u015fkin birka\u00e7 \u00f6rnek g\u00f6sterilmektedir. \u00d6rnek 1: Belirli bir de\u011fere kar\u015f\u0131l\u0131k gelen sat\u0131r numaralar\u0131n\u0131 alma A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame: import pandas as pd #createDataFrame df = pd.DataFrame({‘points’: [25, 12, […]<\/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-980","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n \u00d6rnek 1: Belirli bir de\u011fere kar\u015f\u0131l\u0131k gelen sat\u0131r numaralar\u0131n\u0131 alma<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd.DataFrame({'points': [25, 12, 15, 14, 19],\n 'assists': [5, 7, 7, 9, 12],\n 'team': ['Mavs', 'Mavs', 'Spurs', 'Celtics', 'Warriors']})\n\n#view DataFrame<\/span> \nprint<\/span> (df)\n\n team assists points\n0 25 5 Mavs\n1 12 7 Mavs\n2 15 7 Spurs\n3 14 9 Celtics\n4 19 12 Warriors\n<\/strong><\/pre>\n #get row numbers where 'team' is equal to Mavs<\/span>\ndf[df[' team<\/span> '] == ' Mavs<\/span> ']. index<\/span>\n\nInt64Index([0, 1], dtype='int64')\n<\/strong><\/pre>\n #get row numbers where 'team' is equal to Mavs or Spurs<\/span>\nfilter_list = [' Mavs<\/span> ', ' Spurs<\/span> ']\n\n#return only rows where team is in the list of team names\n<\/span>df[df. team<\/span> . isin<\/span> (filter_list)]. index\n<\/span><\/span><\/span>\nInt64Index([0, 1, 2], dtype='int64')\n<\/strong><\/pre>\n \u00d6rnek 2: Benzersiz bir sat\u0131r numaras\u0131 al\u0131n<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd.DataFrame({'points': [25, 12, 15, 14, 19],\n 'assists': [5, 7, 7, 9, 12],\n 'team': ['Mavs', 'Mavs', 'Spurs', 'Celtics', 'Warriors']})<\/strong><\/pre>\n #get the row number where team is equal to Celtics<\/span>\ndf[df[' team<\/span> '] == ' Celtics<\/span> ']. index<\/span> [ 0<\/span> ]\n\n3<\/strong><\/pre>\n \u00d6rnek 3: Sat\u0131r numaralar\u0131n\u0131n toplam\u0131n\u0131 alma<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd.DataFrame({'points': [25, 12, 15, 14, 19],\n 'assists': [5, 7, 7, 9, 12],\n 'team': ['Mavs', 'Mavs', 'Spurs', 'Celtics', 'Warriors']})<\/strong><\/pre>\n #find total number of rows where team is equal to Mavs<\/span>\nlen<\/span> (df[df[' team<\/span> '] == ' Celtics<\/span> ']. index )<\/span><\/span>\n\n2<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Pandas DataFrame’i birden \u00e7ok ko\u015fulda filtreleme<\/a>
Pandas DataFrame’de eksik de\u011ferler nas\u0131l say\u0131l\u0131r?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"