{"id":2663,"date":"2023-07-21T06:47:49","date_gmt":"2023-07-21T06:47:49","guid":{"rendered":"https:\/\/statorials.org\/pl\/pandy-fillna-ze-srednia\/"},"modified":"2023-07-21T06:47:49","modified_gmt":"2023-07-21T06:47:49","slug":"pandy-fillna-ze-srednia","status":"publish","type":"post","link":"https:\/\/statorials.org\/pl\/pandy-fillna-ze-srednia\/","title":{"rendered":"Pandy: jak wype\u0142ni\u0107 warto\u015bci nan \u015bredni\u0105 (3 przyk\u0142ady)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Mo\u017cesz u\u017cy\u0107 funkcji <strong>fillna()<\/strong> , aby zast\u0105pi\u0107 warto\u015bci NaN w ramce DataFrame pandy.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Oto trzy typowe sposoby korzystania z tej funkcji:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Metoda 1: Wype\u0142nij warto\u015bci NaN w kolumnie \u015bredni\u0105<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">col1<\/span> '] = df[' <span style=\"color: #ff0000;\">col1<\/span> ']. <span style=\"color: #3366ff;\">fillna<\/span> (df[' <span style=\"color: #ff0000;\">col1<\/span> ']. <span style=\"color: #3366ff;\">mean<\/span> ())<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>Metoda 2: Wype\u0142nij warto\u015bci NaN w wielu kolumnach \u015bredni\u0105<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[[' <span style=\"color: #ff0000;\">col1<\/span> ', ' <span style=\"color: #ff0000;\">col2<\/span> ']] = df[[' <span style=\"color: #ff0000;\">col1<\/span> ', ' <span style=\"color: #ff0000;\">col2<\/span> ']]. <span style=\"color: #3366ff;\">fillna<\/span> (df[[' <span style=\"color: #ff0000;\">col1<\/span> ',' <span style=\"color: #ff0000;\">col2<\/span> ']]. <span style=\"color: #3366ff;\">mean<\/span> ())<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>Metoda 3: Wype\u0142nij warto\u015bci NaN we wszystkich kolumnach \u015bredni\u0105<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df = df. <span style=\"color: #3366ff;\">fillna<\/span> ( <span style=\"color: #3366ff;\">df.mean<\/span> ())<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Poni\u017csze przyk\u0142ady pokazuj\u0105, jak w praktyce u\u017cywa\u0107 ka\u017cdej metody z nast\u0119puj\u0105c\u0105 ramk\u0105 DataFrame pand:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n<span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#create DataFrame with some NaN values<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">rating<\/span> ': [np.nan, 85, np.nan, 88, 94, 90, 76, 75, 87, 86],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [25, np.nan, 14, 16, 27, 20, 12, 15, 14, 19],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [5, 7, 7, np.nan, 5, 7, 6, 9, 9, 5],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n<span style=\"color: #008080;\">\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<h3> <span style=\"color: #000000;\"><strong>Przyk\u0142ad 1: Wype\u0142nij warto\u015bci NaN w kolumnie \u015bredni\u0105<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Poni\u017cszy kod pokazuje, jak wype\u0142ni\u0107 warto\u015bci NaN w kolumnie <strong>oceny<\/strong> \u015bredni\u0105 warto\u015bci\u0105 kolumny <strong>oceny<\/strong> :<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fill NaNs with column mean in 'rating' column<\/span>\n<span style=\"color: #008080;\"><span style=\"color: #000000;\">df[' <span style=\"color: #ff0000;\">rating<\/span> '] = df[' <span style=\"color: #ff0000;\">rating<\/span> ']. <span style=\"color: #3366ff;\">fillna<\/span> (df[' <span style=\"color: #ff0000;\">rating<\/span> ']. <span style=\"color: #3366ff;\">mean<\/span> ())\n\n<\/span>#view updated DataFrame<\/span>\ndf\n\n\trating points assists rebounds\n0 85.125 25.0 5.0 11\n1 85,000 NaN 7.0 8\n2 85.125 14.0 7.0 10\n3 88,000 16.0 NaN 6\n4 94,000 27.0 5.0 6\n5 90,000 20.0 7.0 9\n6 76,000 12.0 6.0 6\n7 75,000 15.0 9.0 10\n8 87,000 14.0 9.0 10\n9 86,000 19.0 5.0 7\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u015arednia warto\u015b\u0107 w kolumnie <strong>oceny<\/strong> wynios\u0142a <strong>85,125<\/strong> , wi\u0119c ka\u017cda z warto\u015bci NaN w kolumnie <strong>oceny<\/strong> zosta\u0142a wype\u0142niona t\u0105 warto\u015bci\u0105.<\/span><\/p>\n<h3> <strong>Przyk\u0142ad 2:<\/strong> <span style=\"color: #000000;\"><strong>Wype\u0142nij warto\u015bci NaN w wielu kolumnach \u015bredni\u0105<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Poni\u017cszy kod pokazuje, jak wype\u0142ni\u0107 warto\u015bci NaN w kolumnach <strong>ocen<\/strong> i <strong>punkt\u00f3w<\/strong> odpowiednimi \u015brednimi kolumnami:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fill NaNs with column means in 'rating' and 'points' columns<\/span>\ndf[[' <span style=\"color: #ff0000;\">rating<\/span> ', ' <span style=\"color: #ff0000;\">points<\/span> ']] = df[[' <span style=\"color: #ff0000;\">rating<\/span> ', ' <span style=\"color: #ff0000;\">points<\/span> ']]. <span style=\"color: #3366ff;\">fillna<\/span> (df[[' <span style=\"color: #ff0000;\">rating<\/span> ',' <span style=\"color: #ff0000;\">points<\/span> ']]. <span style=\"color: #3366ff;\">mean<\/span> ())\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span>df\n\n\trating points assists rebounds\n0 85.125 25.0 5.0 11\n1 85,000 18.0 7.0 8\n2 85.125 14.0 7.0 10\n3 88,000 16.0 NaN 6\n4 94,000 27.0 5.0 6\n5 90,000 20.0 7.0 9\n6 76,000 12.0 6.0 6\n7 75,000 15.0 9.0 10\n8 87,000 14.0 9.0 10\n9 86,000 19.0 5.0 7\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Warto\u015bci NaN w kolumnach <strong>ocen<\/strong> i <strong>punkt\u00f3w<\/strong> zosta\u0142y wype\u0142nione odpowiednimi \u015brednimi kolumnami.<\/span><\/p>\n<h3> <strong>Przyk\u0142ad 3: Wype\u0142nij warto\u015bci NaN we wszystkich kolumnach \u015bredni\u0105<\/strong><\/h3>\n<p> <span style=\"color: #000000;\">Poni\u017cszy kod pokazuje, jak wype\u0142ni\u0107 warto\u015bci NaN w ka\u017cdej kolumnie \u015brednimi kolumn:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fill NaNs with column means in each column<\/span> \ndf = df. <span style=\"color: #3366ff;\">fillna<\/span> ( <span style=\"color: #3366ff;\">df.mean<\/span> ())\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span>df\n\n        rating points assists rebounds\n0 85.125 25.0 5.000000 11\n1 85,000 18.0 7,000000 8\n2 85.125 14.0 7.000000 10\n3 88,000 16.0 6.666667 6\n4 94,000 27.0 5,000000 6\n5 90,000 20.0 7,000000 9\n6 76,000 12.0 6,000000 6\n7 75,000 15.0 9,000000 10\n8 87,000 14.0 9,000000 10\n9 86,000 19.0 5,000000 7\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Nale\u017cy pami\u0119ta\u0107, \u017ce warto\u015bci NaN w ka\u017cdej kolumnie zosta\u0142y wype\u0142nione \u015bredni\u0105 z ich kolumny.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Pe\u0142n\u0105 dokumentacj\u0119 online funkcji <strong>fillna()<\/strong> mo\u017cna znale\u017a\u0107 <a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/api\/pandas.DataFrame.fillna.html\" target=\"_blank\" rel=\"noopener\">tutaj<\/a> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Dodatkowe zasoby<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Poni\u017csze samouczki wyja\u015bniaj\u0105, jak wykonywa\u0107 inne typowe operacje na pandach:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/pl\/pandy-licza-brakujace-wartosci\/\" target=\"_blank\" rel=\"noopener\">Jak policzy\u0107 brakuj\u0105ce warto\u015bci w pandach<\/a><br \/> <a href=\"https:\/\/statorials.org\/pl\/upusc-pandy\/\" target=\"_blank\" rel=\"noopener\">Jak usun\u0105\u0107 wiersze z warto\u015bciami NaN w Pandach<\/a><br \/> <a href=\"https:\/\/statorials.org\/pl\/pandy-upuszczaja-wiersze-z-wartoscia\/\" target=\"_blank\" rel=\"noopener\">Jak usun\u0105\u0107 wiersze zawieraj\u0105ce okre\u015blon\u0105 warto\u015b\u0107 w Pandach<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Mo\u017cesz u\u017cy\u0107 funkcji fillna() , aby zast\u0105pi\u0107 warto\u015bci NaN w ramce DataFrame pandy. Oto trzy typowe sposoby korzystania z tej funkcji: Metoda 1: Wype\u0142nij warto\u015bci NaN w kolumnie \u015bredni\u0105 df[&#8217; col1 &#8217;] = df[&#8217; col1 &#8217;]. fillna (df[&#8217; col1 &#8217;]. mean ()) Metoda 2: Wype\u0142nij warto\u015bci NaN w wielu kolumnach \u015bredni\u0105 df[[&#8217; col1 &#8217;, &#8217; [&hellip;]<\/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-2663","post","type-post","status-publish","format-standard","hentry","category-przewodnik"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pandy: Jak wype\u0142ni\u0107 warto\u015bci NaN \u015bredni\u0105 (3 przyk\u0142ady) - Statorials<\/title>\n<meta name=\"description\" content=\"W tym samouczku wyja\u015bniono, jak wype\u0142ni\u0107 warto\u015bci NaN \u015bredni\u0105 w ramce DataFrame pandy, podaj\u0105c kilka przyk\u0142ad\u00f3w.\" \/>\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\/pandy-fillna-ze-srednia\/\" \/>\n<meta property=\"og:locale\" content=\"pl_PL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandy: Jak wype\u0142ni\u0107 warto\u015bci NaN \u015bredni\u0105 (3 przyk\u0142ady) - Statorials\" \/>\n<meta property=\"og:description\" content=\"W tym samouczku wyja\u015bniono, jak wype\u0142ni\u0107 warto\u015bci NaN \u015bredni\u0105 w ramce DataFrame pandy, podaj\u0105c kilka przyk\u0142ad\u00f3w.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/pl\/pandy-fillna-ze-srednia\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-21T06:47:49+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\/pandy-fillna-ze-srednia\/\",\"url\":\"https:\/\/statorials.org\/pl\/pandy-fillna-ze-srednia\/\",\"name\":\"Pandy: Jak wype\u0142ni\u0107 warto\u015bci NaN \u015bredni\u0105 (3 przyk\u0142ady) - Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/pl\/#website\"},\"datePublished\":\"2023-07-21T06:47:49+00:00\",\"dateModified\":\"2023-07-21T06:47:49+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965\"},\"description\":\"W tym samouczku wyja\u015bniono, jak wype\u0142ni\u0107 warto\u015bci NaN \u015bredni\u0105 w ramce DataFrame pandy, podaj\u0105c kilka przyk\u0142ad\u00f3w.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/pl\/pandy-fillna-ze-srednia\/#breadcrumb\"},\"inLanguage\":\"pl-PL\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/pl\/pandy-fillna-ze-srednia\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/pl\/pandy-fillna-ze-srednia\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Dom\",\"item\":\"https:\/\/statorials.org\/pl\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Pandy: jak wype\u0142ni\u0107 warto\u015bci nan \u015bredni\u0105 (3 przyk\u0142ady)\"}]},{\"@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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