{"id":2028,"date":"2023-07-24T00:47:19","date_gmt":"2023-07-24T00:47:19","guid":{"rendered":"https:\/\/statorials.org\/tr\/pandalar-fillna\/"},"modified":"2023-07-24T00:47:19","modified_gmt":"2023-07-24T00:47:19","slug":"pandalar-fillna","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/pandalar-fillna\/","title":{"rendered":"Nan de\u011ferlerini de\u011fi\u015ftirmek i\u00e7in pandas fillna() nas\u0131l kullan\u0131l\u0131r?"},"content":{"rendered":"

<\/p>\n

\n

Bir pandan\u0131n DataFrame’indeki NaN de\u011ferlerini de\u011fi\u015ftirmek i\u00e7in fillna()<\/strong> i\u015flevini kullanabilirsiniz.<\/span><\/p>\n

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

 #replace NaN values in one column<\/span>\ndf[' col1<\/span> '] = df[' col1<\/span> ']. fillna<\/span> (0)\n\n#replace NaN values in multiple columns\n<\/span>df[[' col1<\/span> ', ' col2<\/span> ']] = df[[' col1<\/span> ', ' col2<\/span> ']]. fillna<\/span> (0) \n\n#replace NaN values in all columns<\/span>\ndf = df. fillna<\/span> (0)<\/strong><\/pre>\n
 Bu e\u011fitimde bu i\u015flevin a\u015fa\u011f\u0131daki pandalar DataFrame ile nas\u0131l kullan\u0131laca\u011f\u0131 a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\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': [25, np.nan, 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<\/span>\ndf\n\n        rating points assists rebounds\n0 NaN 25.0 5.0 11\n1 85.0 NaN 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: Bir s\u00fctundaki NaN de\u011ferlerini de\u011fi\u015ftirin<\/strong><\/span><\/h3>\n
 A\u015fa\u011f\u0131daki kod, “not” s\u00fctununda NaN de\u011ferlerinin s\u0131f\u0131rlarla nas\u0131l de\u011fi\u015ftirilece\u011fini g\u00f6sterir:<\/span><\/p>\n
 #replace NaNs with zeros in 'rating' column<\/span>\ndf[' rating<\/span> '] = df[' rating<\/span> ']. fillna<\/span> (0)\n\n#view DataFrame<\/span>\ndf\n\n\trating points assists rebounds\n0 0.0 25.0 5.0 11\n1 85.0 NaN 7.0 8\n2 0.0 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 2: Birden fazla s\u00fctundaki NaN de\u011ferlerini de\u011fi\u015ftirin<\/strong><\/h3>\n
 A\u015fa\u011f\u0131daki kod, “derece” ve “puan” s\u00fctunlar\u0131nda NaN de\u011ferlerinin s\u0131f\u0131rlarla nas\u0131l de\u011fi\u015ftirilece\u011fini g\u00f6sterir:<\/span><\/p>\n
 #replace NaNs with zeros in 'rating' and 'points' columns<\/span>\ndf[[' rating<\/span> ', ' points<\/span> ']] = df[[' rating<\/span> ', ' points<\/span> ']]. fillna<\/span> (0)\n\n#view DataFrame\n<\/span>df\n\n\trating points assists rebounds\n0 0.0 25.0 5.0 11\n1 85.0 0.0 7.0 8\n2 0.0 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 3: T\u00fcm s\u00fctunlardaki NaN de\u011ferlerini de\u011fi\u015ftirin<\/strong><\/h3>\n
 A\u015fa\u011f\u0131daki kod, her s\u00fctundaki NaN de\u011ferlerinin s\u0131f\u0131rlarla nas\u0131l de\u011fi\u015ftirilece\u011fini g\u00f6sterir:<\/span><\/p>\n
 #replace NaNs with zeros in all columns<\/span> \ndf = df. fillna<\/span> (0)\n\n#view DataFrame\n<\/span>df\n\n        rating points assists rebounds\n0 0.0 25.0 5.0 11\n1 85.0 0.0 7.0 8\n2 0.0 14.0 7.0 10\n3 88.0 16.0 0.0 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
 fillna()<\/strong> i\u015flevine ili\u015fkin \u00e7evrimi\u00e7i belgelerin tamam\u0131n\u0131 burada<\/a> bulabilirsiniz.<\/span><\/p>\n
 Ek kaynaklar<\/strong><\/span><\/h3>\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
 Pandalarda eksik de\u011ferler nas\u0131l say\u0131l\u0131r?<\/a>
 Pandalar’da NaN de\u011ferlerine sahip sat\u0131rlar nas\u0131l silinir<\/a>
 Pandalarda belirli bir de\u011fer i\u00e7eren sat\u0131rlar nas\u0131l silinir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
Bir pandan\u0131n DataFrame’indeki NaN de\u011ferlerini de\u011fi\u015ftirmek i\u00e7in fillna() i\u015flevini kullanabilirsiniz. Bu i\u015flev a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r: #replace NaN values in one column df[‘ col1 ‘] = df[‘ col1 ‘]. fillna (0) #replace NaN values in multiple columns df[[‘ col1 ‘, ‘ col2 ‘]] = df[[‘ col1 ‘, ‘ col2 ‘]]. fillna (0) #replace NaN values […]<\/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-2028","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\nNaN de\u011ferlerini de\u011fi\u015ftirmek i\u00e7in Pandas fillna() nas\u0131l kullan\u0131l\u0131r?<\/title>\n<meta name=\"description\" content=\"Bu e\u011fitimde, bir DataFrame'deki NaN de\u011ferlerini de\u011fi\u015ftirmek i\u00e7in pandas fillna() i\u015flevinin nas\u0131l kullan\u0131laca\u011f\u0131 \u00f6rneklerle a\u00e7\u0131klanmaktad\u0131r.\" \/>\n<meta name=\"robots\" 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