{"id":895,"date":"2023-07-28T09:46:04","date_gmt":"2023-07-28T09:46:04","guid":{"rendered":"https:\/\/statorials.org\/tr\/pandalari-birak\/"},"modified":"2023-07-28T09:46:04","modified_gmt":"2023-07-28T09:46:04","slug":"pandalari-birak","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/pandalari-birak\/","title":{"rendered":"Pandalar'da nan de\u011ferlerine sahip sat\u0131rlar nas\u0131l silinir"},"content":{"rendered":"

<\/p>\n

\n

\u00c7o\u011fu zaman bir pandas DataFrame’de NaN de\u011ferleri i\u00e7eren sat\u0131rlar\u0131 kald\u0131rmak isteyebilirsiniz. Neyse ki pandas dropna()<\/a> i\u015flevini kullanarak bunu yapmak kolayd\u0131r.<\/span><\/p>\n

Bu e\u011fitimde, bu fonksiyonun a\u015fa\u011f\u0131daki pandalar DataFrame’de kullan\u0131lmas\u0131na ili\u015fkin birka\u00e7 \u00f6rnek g\u00f6sterilmektedir:<\/span><\/p>\n

 import<\/span> numpy as<\/span> np\nimport<\/span> scipy.stats as<\/span> stats\n\n#create DataFrame with some NaN values<\/span>\ndf = pd.DataFrame({'rating': [np.nan, 85, np.nan, 88, 94, 90, 76, 75, 87, 86],\n                   'points': [np.nan, 25, 14, 16, 27, 20, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, np.nan, 5, 7, 6, 9, 9, 5],\n                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n#view DataFrame\n<\/span>df\n\n\n        rating points assists rebounds\n0 NaN NaN 5.0 11\n1 85.0 25.0 7.0 8\n2 NaN 14.0 7.0 10\n3 88.0 16.0 NaN 6\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7\n<\/strong><\/pre>\n
 \u00d6rnek 1: NaN de\u011ferlerine sahip sat\u0131rlar\u0131 silin<\/strong><\/span><\/h3>\n
 NaN de\u011ferleri<\/em> i\u00e7eren t\u00fcm sat\u0131rlar\u0131 kald\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n
 df. dropna<\/span> ()\n\n\trating points assists rebounds\n1 85.0 25.0 7.0 8\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7\n<\/strong><\/pre>\n
 \u00d6rnek 2: T\u00fcm NaN de\u011ferlerine sahip sat\u0131rlar\u0131 kald\u0131r\u0131n<\/strong><\/span><\/h3>\n
 Her s\u00fctundaki t\u00fcm<\/em> NaN de\u011ferlerini i\u00e7eren t\u00fcm sat\u0131rlar\u0131 kald\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n
 df. dropna<\/span> (how=' all<\/span> ') \n\n        rating points assists rebounds\n0 NaN NaN 5.0 11\n1 85.0 25.0 7.0 8\n2 NaN 14.0 7.0 10\n3 88.0 16.0 NaN 6\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7\n<\/strong><\/pre>\n
 Bu \u00f6zel DataFrame’de t\u00fcm NaN de\u011ferlerine sahip hi\u00e7bir sat\u0131r yoktu, dolay\u0131s\u0131yla sat\u0131rlar\u0131n hi\u00e7biri silinmedi.<\/span><\/p>\n
 \u00d6rnek 3: Belirli bir e\u015fi\u011fin alt\u0131ndaki sat\u0131rlar\u0131 silin<\/strong><\/span><\/h3>\n
 En az\u0131ndan<\/em> belirli say\u0131da NaN olmayan de\u011fere sahip olmayan t\u00fcm sat\u0131rlar\u0131 kald\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n
 df. dropna<\/span> (thresh= 3<\/span> ) \n\n\trating points assists rebounds\n1 85.0 25.0 7.0 8\n2 NaN 14.0 7.0 10\n3 88.0 16.0 NaN 6\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7\n<\/strong><\/pre>\n
 Orijinal DataFrame’in ilk sat\u0131r\u0131 en az 3 NaN olmayan de\u011fer i\u00e7ermiyordu, dolay\u0131s\u0131yla kald\u0131r\u0131lan tek sat\u0131r bu oldu.<\/span><\/p>\n
 \u00d6rnek 4: Belirli bir s\u00fctunda Nan de\u011ferlerine sahip bir sat\u0131r\u0131 silin<\/strong><\/span><\/h3>\n
 Belirli bir s\u00fctunda NaN de\u011feri olan t\u00fcm sat\u0131rlar\u0131 kald\u0131rmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n
 df. dropna<\/span> (subset=[' assists<\/span> '])\n\n\trating points assists rebounds\n0 NaN NaN 5.0 11\n1 85.0 25.0 7.0 8\n2 NaN 14.0 7.0 10\n4 94.0 27.0 5.0 6\n5 90.0 20.0 7.0 9\n6 76.0 12.0 6.0 6\n7 75.0 15.0 9.0 10\n8 87.0 14.0 9.0 10\n9 86.0 19.0 5.0 7<\/strong><\/pre>\n
 \u00d6rnek 5: NaN i\u00e7eren sat\u0131rlar\u0131 sildikten sonra dizini s\u0131f\u0131rlay\u0131n<\/strong><\/span><\/h3>\n
 NaN de\u011ferlerine sahip sat\u0131rlar\u0131 sildikten sonra DataFrame’in dizinini s\u0131f\u0131rlamak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span><\/p>\n
 #drop all rows that have any NaN values<\/span>\ndf = df. dropna<\/span> ()\n\n#reset index of DataFrame<\/span>\ndf = df. reset_index<\/span> (drop=True)\n\n#view DataFrame<\/span>\ndf\n\n        rating points assists rebounds\n0 85.0 25.0 7.0 8\n1 94.0 27.0 5.0 6\n2 90.0 20.0 7.0 9\n3 76.0 12.0 6.0 6\n4 75.0 15.0 9.0 10\n5 87.0 14.0 9.0 10\n6 86.0 19.0 5.0 77<\/strong><\/pre>\n
 dropna() i\u015flevine ili\u015fkin t\u00fcm belgeleri burada<\/a> bulabilirsiniz.<\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
\u00c7o\u011fu zaman bir pandas DataFrame’de NaN de\u011ferleri i\u00e7eren sat\u0131rlar\u0131 kald\u0131rmak isteyebilirsiniz. Neyse ki pandas dropna() i\u015flevini kullanarak bunu yapmak kolayd\u0131r. Bu e\u011fitimde, bu fonksiyonun a\u015fa\u011f\u0131daki pandalar DataFrame’de kullan\u0131lmas\u0131na ili\u015fkin birka\u00e7 \u00f6rnek g\u00f6sterilmektedir: import numpy as np import scipy.stats as stats #create DataFrame with some NaN values df = pd.DataFrame({‘rating’: [np.nan, 85, np.nan, 88, 94, 90, […]<\/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-895","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\nPandalar'da NaN de\u011ferlerine sahip sat\u0131rlar nas\u0131l silinir<\/title>\n<meta name=\"description\" content=\"Bir pandas DataFrame'de NaN de\u011ferlerine sahip sat\u0131rlar\u0131n nas\u0131l silinece\u011fine dair basit bir a\u00e7\u0131klama.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/tr\/pandalari-birak\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandalar'da NaN de\u011ferlerine sahip sat\u0131rlar nas\u0131l silinir\" \/>\n<meta property=\"og:description\" content=\"Bir pandas DataFrame'de NaN de\u011ferlerine sahip sat\u0131rlar\u0131n nas\u0131l silinece\u011fine dair basit bir a\u00e7\u0131klama.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/tr\/pandalari-birak\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-28T09:46:04+00:00\" \/>\n<meta name=\"author\" content=\"Dr.benjamin anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Dr.benjamin anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/tr\/pandalari-birak\/\",\"url\":\"https:\/\/statorials.org\/tr\/pandalari-birak\/\",\"name\":\"Pandalar'da NaN de\u011ferlerine sahip sat\u0131rlar nas\u0131l silinir\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/tr\/#website\"},\"datePublished\":\"2023-07-28T09:46:04+00:00\",\"dateModified\":\"2023-07-28T09:46:04+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/tr\/#\/schema\/person\/365dc158a39a7c8ae256355451e3de48\"},\"description\":\"Bir pandas DataFrame'de NaN de\u011ferlerine sahip sat\u0131rlar\u0131n nas\u0131l silinece\u011fine dair basit bir a\u00e7\u0131klama.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/tr\/pandalari-birak\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/tr\/pandalari-birak\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/tr\/pandalari-birak\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Ev\",\"item\":\"https:\/\/statorials.org\/tr\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Pandalar'da nan de\u011ferlerine sahip sat\u0131rlar nas\u0131l silinir\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/tr\/#website\",\"url\":\"https:\/\/statorials.org\/tr\/\",\"name\":\"Statorials\",\"description\":\"\u0130statistik okuryazarl\u0131\u011f\u0131 rehberiniz!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/tr\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"tr\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/tr\/#\/schema\/person\/365dc158a39a7c8ae256355451e3de48\",\"name\":\"Dr.benjamin anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"tr\",\"@id\":\"https:\/\/statorials.org\/tr\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/statorials.org\/tr\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"https:\/\/statorials.org\/tr\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Dr.benjamin anderson\"},\"description\":\"Merhaba, ben Benjamin, emekli bir istatistik profes\u00f6r\u00fc ve Statorials \u00f6\u011fretmenine d\u00f6n\u00fc\u015ft\u00fcm. \u0130statistik alan\u0131ndaki kapsaml\u0131 deneyimim ve uzmanl\u0131\u011f\u0131mla, \u00f6\u011frencilerimi Statorials arac\u0131l\u0131\u011f\u0131yla g\u00fc\u00e7lendirmek i\u00e7in bilgilerimi payla\u015fmaya can at\u0131yorum. 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