{"id":1359,"date":"2023-07-26T17:12:24","date_gmt":"2023-07-26T17:12:24","guid":{"rendered":"https:\/\/statorials.org\/pl\/fillna-wielokolumnowe-pandy\/"},"modified":"2023-07-26T17:12:24","modified_gmt":"2023-07-26T17:12:24","slug":"fillna-wielokolumnowe-pandy","status":"publish","type":"post","link":"https:\/\/statorials.org\/pl\/fillna-wielokolumnowe-pandy\/","title":{"rendered":"Jak wype\u0142ni\u0107 warto\u015bci na dla wielu kolumn w pandach"},"content":{"rendered":"

<\/p>\n

\n

Funkcja pandas fillna()<\/a> jest przydatna do uzupe\u0142niania brakuj\u0105cych warto\u015bci w kolumnach pandy DataFrame.<\/span><\/p>\n

W tym samouczku przedstawiono kilka przyk\u0142ad\u00f3w u\u017cycia tej funkcji do uzupe\u0142nienia brakuj\u0105cych warto\u015bci dla wielu kolumn w nast\u0119puj\u0105cej ramce DataFrame pand:<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({'team': ['A', np. nan<\/span> , 'B', 'B', 'B', 'C', 'C', 'C'],\n                   'points': [25, np. no<\/span> , 15, np. no<\/span> , 19, 23, 25, 29],\n                   'assists': [5, 7, 7, 9, 12, 9, np. no<\/span> , 4],\n                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})<\/span>\n\n#view DataFrame\nprint<\/span> (df)\n\n  team points assists rebounds\n0 A 25.0 5.0 11\n1 NaN NaN 7.0 8\n2 B 15.0 7.0 10\n3 B NaN 9.0 6\n4 B 19.0 12.0 6\n5 C 23.0 9.0 5\n6 C 25.0 NaN 9\n7 C 29.0 4.0 12<\/span>\n<\/span><\/strong><\/pre>\n
 Przyk\u0142ad 1: Uzupe\u0142nij brakuj\u0105ce warto\u015bci we wszystkich kolumnach<\/strong><\/span><\/h3>\n
 Poni\u017cszy kod pokazuje, jak wype\u0142ni\u0107 brakuj\u0105ce warto\u015bci zerem dla wszystkich<\/em> kolumn w DataFrame:<\/span><\/p>\n
 #replace all missing values with zero<\/span>\ndf. fillna<\/span> (value= 0<\/span> ,inplace= True<\/span> )\n\n#view DataFrame\n<\/span>print<\/span> (df) \n\n  team points assists rebounds\n0 A 25.0 5.0 11\n1 0 0.0 7.0 8\n2 B 15.0 7.0 10\n3 B 0.0 9.0 6\n4 B 19.0 12.0 6\n5 C 23.0 9.0 5\n6 C 25.0 0.0 9\n7 C 29.0 4.0 12<\/strong><\/pre>\n
 Przyk\u0142ad 2: Uzupe\u0142nij brakuj\u0105ce warto\u015bci dla wielu kolumn<\/strong><\/span><\/h3>\n
 Poni\u017cszy kod pokazuje, jak wype\u0142ni\u0107 brakuj\u0105ce warto\u015bci zerem tylko dla kolumn punktowych i pomocniczych DataFrame:<\/span><\/p>\n
 #replace missing values in points and assists columns with zero<\/span>\ndf[['points', 'assists']] = df[['points', 'assists']]. fillna<\/span> (value= 0<\/span> )\n\n#view DataFrame\n<\/span>print<\/span> (df) \n\n  team points assists rebounds\n0 A 25.0 5.0 11\n1 NaN 0.0 7.0 8\n2 B 15.0 7.0 10\n3 B 0.0 9.0 6\n4 B 19.0 12.0 6\n5 C 23.0 9.0 5\n6 C 25.0 0.0 9\n7 C 29.0 4.0 12\n<\/strong><\/pre>\n
 Przyk\u0142ad 3: Uzupe\u0142nij brakuj\u0105ce warto\u015bci z wielu kolumn r\u00f3\u017cnymi warto\u015bciami<\/strong><\/span><\/h3>\n
 Poni\u017cszy kod pokazuje, jak wype\u0142ni\u0107 brakuj\u0105ce warto\u015bci w trzech r\u00f3\u017cnych kolumnach trzema r\u00f3\u017cnymi warto\u015bciami:<\/span><\/p>\n
 #replace missing values in three columns with three different values<\/span>\ndf. fillna<\/span> ({'team': ' Unknown<\/span> ', 'points': 0<\/span> , 'assists': ' zero<\/span> '}, inplace= True<\/span> )\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n      team points assists rebounds\n0 A 25.0 5 11\n1 Unknown 0.0 7 8\n2 B 15.0 7 10\n3 B 0.0 9 6\n4 B 19.0 12 6\n5 C 23.0 9 5\n6 C 25.0 zero 9\n7 C 29.0 4 12\n<\/strong><\/pre>\n
 Nale\u017cy pami\u0119ta\u0107, \u017ce ka\u017cda z brakuj\u0105cych warto\u015bci w trzech kolumnach zosta\u0142a zast\u0105piona unikaln\u0105 warto\u015bci\u0105.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
Funkcja pandas fillna() jest przydatna do uzupe\u0142niania brakuj\u0105cych warto\u015bci w kolumnach pandy DataFrame. W tym samouczku przedstawiono kilka przyk\u0142ad\u00f3w u\u017cycia tej funkcji do uzupe\u0142nienia brakuj\u0105cych warto\u015bci dla wielu kolumn w nast\u0119puj\u0105cej ramce DataFrame pand: import pandas as pd import numpy as np #createDataFrame df = pd. DataFrame ({’team’: [’A’, np. nan , 'B’, 'B’, 'B’, […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-1359","post","type-post","status-publish","format-standard","hentry","category-przewodnik"],"yoast_head":"\nJak wype\u0142ni\u0107 warto\u015bci NA dla wielu kolumn w Pandach<\/title>\n<meta name=\"description\" content=\"W tym samouczku wyja\u015bniono, jak wype\u0142ni\u0107 warto\u015bci NA dla wielu kolumn w ramce DataFrame pandy, \u0142\u0105cznie z przyk\u0142adami.\" \/>\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\/pl\/fillna-wielokolumnowe-pandy\/\" \/>\n<meta property=\"og:locale\" content=\"pl_PL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Jak wype\u0142ni\u0107 warto\u015bci NA dla wielu kolumn w Pandach\" \/>\n<meta property=\"og:description\" content=\"W tym samouczku wyja\u015bniono, jak wype\u0142ni\u0107 warto\u015bci NA dla wielu kolumn w ramce DataFrame pandy, \u0142\u0105cznie z przyk\u0142adami.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/pl\/fillna-wielokolumnowe-pandy\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-26T17:12:24+00:00\" \/>\n<meta name=\"author\" content=\"Benjamin Anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Napisane przez\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin Anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Szacowany czas czytania\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minuty\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/pl\/fillna-wielokolumnowe-pandy\/\",\"url\":\"https:\/\/statorials.org\/pl\/fillna-wielokolumnowe-pandy\/\",\"name\":\"Jak wype\u0142ni\u0107 warto\u015bci NA dla wielu kolumn w Pandach\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/pl\/#website\"},\"datePublished\":\"2023-07-26T17:12:24+00:00\",\"dateModified\":\"2023-07-26T17:12:24+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965\"},\"description\":\"W tym samouczku wyja\u015bniono, jak wype\u0142ni\u0107 warto\u015bci NA dla wielu kolumn w ramce DataFrame pandy, \u0142\u0105cznie z przyk\u0142adami.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/pl\/fillna-wielokolumnowe-pandy\/#breadcrumb\"},\"inLanguage\":\"pl-PL\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/pl\/fillna-wielokolumnowe-pandy\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/pl\/fillna-wielokolumnowe-pandy\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Dom\",\"item\":\"https:\/\/statorials.org\/pl\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Jak wype\u0142ni\u0107 warto\u015bci na dla wielu kolumn w pandach\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/pl\/#website\",\"url\":\"https:\/\/statorials.org\/pl\/\",\"name\":\"Statorials\",\"description\":\"Tw\u00f3j przewodnik po kompetencjach statystycznych!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/pl\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"pl-PL\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965\",\"name\":\"Benjamin Anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pl-PL\",\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/statorials.org\/pl\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"https:\/\/statorials.org\/pl\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin Anderson\"},\"description\":\"Cze\u015b\u0107, jestem Benjamin i jestem emerytowanym profesorem statystyki, kt\u00f3ry zosta\u0142 oddanym nauczycielem Statorials. 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