{"id":4373,"date":"2023-07-11T15:20:37","date_gmt":"2023-07-11T15:20:37","guid":{"rendered":"https:\/\/statorials.org\/it\/panda-e-su-piu-colonne\/"},"modified":"2023-07-11T15:20:37","modified_gmt":"2023-07-11T15:20:37","slug":"panda-e-su-piu-colonne","status":"publish","type":"post","link":"https:\/\/statorials.org\/it\/panda-e-su-piu-colonne\/","title":{"rendered":"Panda: come utilizzare isin per pi\u00f9 colonne"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u00c8 possibile utilizzare i seguenti metodi con la funzione pandas <strong>isin()<\/strong> per filtrare in base a pi\u00f9 colonne in un DataFrame pandas:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Metodo 1: filtra quando pi\u00f9 colonne equivalgono a valori specifici<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df = df[df[[' <span style=\"color: #ff0000;\">team<\/span> ', ' <span style=\"color: #ff0000;\">position<\/span> ']]. <span style=\"color: #3366ff;\">isin<\/span> ([' <span style=\"color: #ff0000;\">A<\/span> ',' <span style=\"color: #ff0000;\">Guard<\/span> ']). <span style=\"color: #3366ff;\">all<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )]\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Questo particolare esempio filtra il DataFrame per le righe in cui la colonna <strong>della squadra<\/strong> \u00e8 uguale a &#8220;A&#8221; <b>e<\/b> la colonna <strong>di posizione<\/strong> \u00e8 uguale a &#8220;Guardia&#8221;.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Metodo 2: filtrare in cui almeno una colonna equivale a un valore specifico<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df = df[df[[' <span style=\"color: #ff0000;\">team<\/span> ', ' <span style=\"color: #ff0000;\">position<\/span> ']]. <span style=\"color: #3366ff;\">isin<\/span> ([' <span style=\"color: #ff0000;\">A<\/span> ',' <span style=\"color: #ff0000;\">Guard<\/span> ']). <span style=\"color: #3366ff;\">any<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )]<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Questo particolare esempio filtra il DataFrame per le righe in cui la colonna <strong>della squadra<\/strong> \u00e8 uguale a &#8220;A&#8221; <b>o<\/b> la colonna <strong>di posizione<\/strong> \u00e8 uguale a &#8220;Guardia&#8221;.<\/span><\/p>\n<p> <span style=\"color: #000000;\">I seguenti esempi mostrano come utilizzare ciascun metodo nella pratica con i seguenti DataFrame panda:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' <span style=\"color: #ff0000;\">position<\/span> ': ['Guard', 'Guard', 'Forward', 'Forward',\n                                'Guard', 'Guard', 'Forward', 'Forward'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [11, 18, 10, 22, 26, 35, 19, 12]})\n                   \n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n  team position points\n0 A Guard 11\n1 A Guard 18\n2 A Forward 10\n3 A Forward 22\n4 B Guard 26\n5 B Guard 35\n6 B Forward 19\n7 B Forward 12<\/strong><\/pre>\n<h2> <span style=\"color: #000000;\"><strong>Esempio 1: filtro in cui pi\u00f9 colonne equivalgono a valori specifici<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Possiamo utilizzare la seguente sintassi per filtrare DataFrame in modo che contenga solo righe in cui la colonna <strong>della squadra<\/strong> \u00e8 uguale a &#8220;A&#8221; <b>e<\/b> la colonna <strong>di posizione<\/strong> \u00e8 uguale a &#8220;Guardia&#8221;.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#filter rows where team column is 'A' and position column is 'Guard'<\/span>\ndf = df[df[[' <span style=\"color: #ff0000;\">team<\/span> ', ' <span style=\"color: #ff0000;\">position<\/span> ']]. <span style=\"color: #3366ff;\">isin<\/span> ([' <span style=\"color: #ff0000;\">A<\/span> ',' <span style=\"color: #ff0000;\">Guard<\/span> ']). <span style=\"color: #3366ff;\">all<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )]\n\n<span style=\"color: #008080;\">#view filtered DataFrame<\/span>\n<span style=\"color: #008000;\">print<\/span> (df)\n\n  team position points\n0 A Guard 11\n1 A Guard 18\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Tieni presente che solo le righe in cui la colonna <strong>della squadra<\/strong> \u00e8 uguale a &#8220;A&#8221; <b>e<\/b> la colonna di <strong>posizione<\/strong> \u00e8 uguale a &#8220;Guardia&#8221; rimangono nel DataFrame filtrato.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Esempio 2: filtro in cui almeno una colonna equivale a un valore specifico<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Possiamo utilizzare la seguente sintassi per filtrare DataFrame in modo che contenga solo righe in cui la colonna <strong>della squadra<\/strong> \u00e8 uguale a &#8220;A&#8221; <b>o<\/b> la colonna <strong>di posizione<\/strong> \u00e8 uguale a &#8220;Guardia&#8221;.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#filter rows where team column is 'A' or position column is 'Guard'<\/span>\ndf = df[df[[' <span style=\"color: #ff0000;\">team<\/span> ', ' <span style=\"color: #ff0000;\">position<\/span> ']]. <span style=\"color: #3366ff;\">isin<\/span> ([' <span style=\"color: #ff0000;\">A<\/span> ',' <span style=\"color: #ff0000;\">Guard<\/span> ']). <span style=\"color: #3366ff;\">any<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )]\n\n<span style=\"color: #008080;\">#view filtered DataFrame<\/span>\n<span style=\"color: #008000;\">print<\/span> (df)\n\n  team position points\n0 A Guard 11\n1 A Guard 18\n2 A Forward 10\n3 A Forward 22\n4 B Guard 26\n5 B Guard 35\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Tieni presente che solo le righe in cui la colonna <strong>della squadra<\/strong> \u00e8 uguale a &#8220;A&#8221; <b>o<\/b> la colonna di <strong>posizione<\/strong> \u00e8 uguale a &#8220;Guardia&#8221; rimangono nel DataFrame filtrato.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Nota<\/strong> : puoi trovare la documentazione completa per la funzione panda <strong>isin()<\/strong> <a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/api\/pandas.DataFrame.isin.html\" target=\"_blank\" rel=\"noopener\">qui<\/a> .<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Risorse addizionali<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">I seguenti tutorial spiegano come eseguire altre attivit\u00e0 comuni nei panda:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/it\/filtro-tabella-pivot-panda\/\" target=\"_blank\" rel=\"noopener\">Panda: come aggiungere un filtro alla tabella pivot<\/a><br \/> <a href=\"https:\/\/statorials.org\/it\/i-panda-non-contengono\/\" target=\"_blank\" rel=\"noopener\">Panda: come filtrare \u201cNon contiene\u201d<\/a><br \/> <a href=\"https:\/\/statorials.org\/it\/linee-di-filtro-panda-contenenti-stringa\/\" target=\"_blank\" rel=\"noopener\">Panda: come filtrare le righe contenenti una stringa specifica<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u00c8 possibile utilizzare i seguenti metodi con la funzione pandas isin() per filtrare in base a pi\u00f9 colonne in un DataFrame pandas: Metodo 1: filtra quando pi\u00f9 colonne equivalgono a valori specifici df = df[df[[&#8216; team &#8216;, &#8216; position &#8216;]]. isin ([&#8216; A &#8216;,&#8217; Guard &#8216;]). all (axis= 1 )] Questo particolare esempio filtra il [&hellip;]<\/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":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Panda: come utilizzare l&#039;isin per pi\u00f9 colonne - Statorials<\/title>\n<meta name=\"description\" content=\"Questo tutorial spiega come utilizzare la funzione isin() con pi\u00f9 colonne in un DataFrame panda, inclusi esempi.\" \/>\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\/it\/panda-e-su-piu-colonne\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Panda: come utilizzare l&#039;isin per pi\u00f9 colonne - Statorials\" \/>\n<meta property=\"og:description\" content=\"Questo tutorial spiega come utilizzare la funzione isin() con pi\u00f9 colonne in un DataFrame panda, inclusi esempi.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/it\/panda-e-su-piu-colonne\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-11T15:20:37+00:00\" \/>\n<meta name=\"author\" content=\"Benjamin anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minuti\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/it\/panda-e-su-piu-colonne\/\",\"url\":\"https:\/\/statorials.org\/it\/panda-e-su-piu-colonne\/\",\"name\":\"Panda: come utilizzare l&#39;isin per pi\u00f9 colonne - Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/it\/#website\"},\"datePublished\":\"2023-07-11T15:20:37+00:00\",\"dateModified\":\"2023-07-11T15:20:37+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/it\/#\/schema\/person\/0896f191fb9fb019f2cd8623112cb3ae\"},\"description\":\"Questo tutorial spiega come utilizzare la funzione isin() con pi\u00f9 colonne in un DataFrame panda, inclusi esempi.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/it\/panda-e-su-piu-colonne\/#breadcrumb\"},\"inLanguage\":\"it-IT\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/it\/panda-e-su-piu-colonne\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/it\/panda-e-su-piu-colonne\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Casa\",\"item\":\"https:\/\/statorials.org\/it\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Panda: come utilizzare isin per pi\u00f9 colonne\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/it\/#website\",\"url\":\"https:\/\/statorials.org\/it\/\",\"name\":\"Statorials\",\"description\":\"La tua guida all&#039;alfabetizzazione statistica!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/it\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"it-IT\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/it\/#\/schema\/person\/0896f191fb9fb019f2cd8623112cb3ae\",\"name\":\"Benjamin anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"it-IT\",\"@id\":\"https:\/\/statorials.org\/it\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/statorials.org\/it\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"https:\/\/statorials.org\/it\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin anderson\"},\"description\":\"Ciao, sono Benjamin, un professore di statistica in pensione diventato insegnante dedicato di Statorials. 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