{"id":2375,"date":"2023-07-22T13:25:44","date_gmt":"2023-07-22T13:25:44","guid":{"rendered":"https:\/\/statorials.org\/it\/convertire-la-variabile-categoriale-in-panda-digitali\/"},"modified":"2023-07-22T13:25:44","modified_gmt":"2023-07-22T13:25:44","slug":"convertire-la-variabile-categoriale-in-panda-digitali","status":"publish","type":"post","link":"https:\/\/statorials.org\/it\/convertire-la-variabile-categoriale-in-panda-digitali\/","title":{"rendered":"Come convertire una variabile categoriale in numerica in pandas"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u00c8 possibile utilizzare la seguente sintassi di base per convertire una variabile categoriale in una variabile numerica in un DataFrame panda:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">column_name<\/span> '] = pd. <span style=\"color: #3366ff;\">factorize<\/span> (df[' <span style=\"color: #ff0000;\">column_name<\/span> '])[0]\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Puoi anche utilizzare la seguente sintassi per convertire ciascuna variabile categoriale in un DataFrame in una variabile numerica:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <b><span style=\"color: #008080;\">#identify all categorical variables<\/span>\ncat_columns = df. <span style=\"color: #3366ff;\">select_dtypes<\/span> ([' <span style=\"color: #ff0000;\">object<\/span> ']). <span style=\"color: #3366ff;\">columns<\/span>\n\n<span style=\"color: #008080;\">#convert all categorical variables to numeric<\/span>\ndf[cat_columns] = df[cat_columns]. <span style=\"color: #3366ff;\">apply<\/span> ( <span style=\"color: #008000;\">lambda<\/span> x: <span style=\"color: #3366ff;\">pd.factorize<\/span> (x)[ <span style=\"color: #008000;\">0<\/span> ])\n<\/b><\/pre>\n<p> <span style=\"color: #000000;\">Gli esempi seguenti mostrano come utilizzare questa sintassi nella pratica.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Esempio 1: convertire una variabile categoriale in numerica<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Supponiamo di avere i seguenti panda DataFrame:<\/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 style=\"color: #000000;\">df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],\n                   ' <span style=\"color: #ff0000;\">position<\/span> ': ['G', 'G', 'F', 'G', 'F', 'C', 'G', 'F', 'C'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4, 13],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12, 10]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<span style=\"color: #000000;\">df\n\n<\/span><span style=\"color: #000000;\">team position<\/span> <span style=\"color: #000000;\">points rebounds\n0 A G<\/span> <span style=\"color: #000000;\">5 11\n1 A G<\/span> <span style=\"color: #000000;\">7 8\n2 A F<\/span> <span style=\"color: #000000;\">7 10\n3 B G<\/span> <span style=\"color: #000000;\">9 6\n4 B F<\/span> <span style=\"color: #000000;\">12 6\n5 B C<\/span> <span style=\"color: #000000;\">9 5\n6 C G<\/span> <span style=\"color: #000000;\">9 9\n7 C F<\/span> <span style=\"color: #000000;\">4 12\n8 C C<\/span> <span style=\"color: #000000;\">13 10\n<\/span><\/span><\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">Possiamo usare la seguente sintassi per convertire la colonna &#8220;team&#8221; in numerica:<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#convert 'team' column to numeric\n<\/span>df[' <span style=\"color: #ff0000;\">team<\/span> '] = pd. <span style=\"color: #3366ff;\">factorize<\/span> (df[' <span style=\"color: #ff0000;\">team<\/span> '])[ <span style=\"color: #008000;\">0<\/span> ]<\/span>\n\n#view updated DataFrame\n<span style=\"color: #000000;\">df<\/span>\n\n<span style=\"color: #000000;\">team position<\/span> <span style=\"color: #000000;\">points rebounds\n0 0 G<\/span> <span style=\"color: #000000;\">5 11\n1 0 G<\/span> <span style=\"color: #000000;\">7 8\n2 0 F<\/span> <span style=\"color: #000000;\">7 10\n3 1 G<\/span> <span style=\"color: #000000;\">9 6\n4 1 F<\/span> <span style=\"color: #000000;\">12 6\n5 1 C<\/span> <span style=\"color: #000000;\">9 5\n6 2 G<\/span> <span style=\"color: #000000;\">9 9\n7 2 F<\/span> <span style=\"color: #000000;\">4 12\n8 2 C<\/span> <span style=\"color: #000000;\">13 10\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Ecco come \u00e8 andata la conversione:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Ogni squadra che aveva un valore di &#8221; <strong>A<\/strong> &#8221; \u00e8 stata convertita in <strong>0<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">Ogni squadra che aveva un valore \u201c <strong>B<\/strong> \u201d \u00e8 stata convertita in <strong>1<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">Ogni squadra che aveva un valore di &#8221; <strong>C<\/strong> &#8221; \u00e8 stata convertita in <strong>2<\/strong> .<\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>Esempio 2: convertire pi\u00f9 variabili categoriali in valori numerici<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Supponiamo ancora una volta di avere 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 style=\"color: #000000;\">df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C'],\n                   ' <span style=\"color: #ff0000;\">position<\/span> ': ['G', 'G', 'F', 'G', 'F', 'C', 'G', 'F', 'C'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4, 13],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12, 10]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span>df\n\n        team position points rebounds\n0 A G 5 11\n1 A G 7 8\n2 A F 7 10\n3 B G 9 6\n4 B F 12 6\n5 B C 9 5\n6 C G 9 9\n7 C F 4 12\n8 C C 13 10\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Possiamo utilizzare la seguente sintassi per convertire ciascuna variabile categoriale nel DataFrame in una variabile numerica:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#get all categorical columns\n<\/span>cat_columns = df. <span style=\"color: #3366ff;\">select_dtypes<\/span> ([' <span style=\"color: #ff0000;\">object<\/span> ']). <span style=\"color: #3366ff;\">columns<\/span>\n\n<span style=\"color: #008080;\">#convert all categorical columns to numeric\n<\/span>df[cat_columns] = df[cat_columns]. <span style=\"color: #3366ff;\">apply<\/span> ( <span style=\"color: #008000;\">lambda<\/span> x: <span style=\"color: #3366ff;\">pd.factorize<\/span> (x)[ <span style=\"color: #008000;\">0<\/span> ])\n\n<span style=\"color: #008080;\">#view updated DataFrame\n<\/span>df\n\n\tteam position points rebounds\n0 0 0 5 11\n1 0 0 7 8\n2 0 1 7 10\n3 1 0 9 6\n4 1 1 12 6\n5 1 2 9 5\n6 2 0 9 9\n7 2 1 4 12\n8 2 2 13 10\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Da notare che le due colonne categoriali (squadra e posizione) sono state entrambe convertite in numeri mentre le colonne punti e rimbalzi sono rimaste le stesse.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Nota<\/strong> : puoi trovare la documentazione completa della funzione pandas <strong>factorize()<\/strong> <a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.factorize.html\" target=\"_blank\" rel=\"noopener\">qui<\/a> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Risorse addizionali<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">I seguenti tutorial spiegano come eseguire altre operazioni comuni nei panda:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/it\/panda-su-corda\/\" target=\"_blank\" rel=\"noopener\">Come convertire le colonne Pandas DataFrame in stringhe<\/a><br \/> <a href=\"https:\/\/statorials.org\/it\/i-panda-convertono-la-colonna-in-int\/\" target=\"_blank\" rel=\"noopener\">Come convertire le colonne Pandas DataFrame in numeri interi<\/a><br \/> <a href=\"https:\/\/statorials.org\/it\/convertire-una-stringa-in-panda-fluttuanti\/\" target=\"_blank\" rel=\"noopener\">Come convertire le stringhe in float in Pandas DataFrame<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u00c8 possibile utilizzare la seguente sintassi di base per convertire una variabile categoriale in una variabile numerica in un DataFrame panda: df[&#8216; column_name &#8216;] = pd. factorize (df[&#8216; column_name &#8216;])[0] Puoi anche utilizzare la seguente sintassi per convertire ciascuna variabile categoriale in un DataFrame in una variabile numerica: #identify all categorical variables cat_columns = df. [&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>Come convertire una variabile categoriale in numerica in Pandas - Stology<\/title>\n<meta name=\"description\" content=\"Questo tutorial spiega come convertire una variabile categoriale in una variabile numerica in un DataFrame panda, con un esempio.\" \/>\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\/convertire-la-variabile-categoriale-in-panda-digitali\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Come convertire una variabile categoriale in numerica in Pandas - Stology\" \/>\n<meta property=\"og:description\" content=\"Questo tutorial spiega come convertire una variabile categoriale in una variabile numerica in un DataFrame panda, con un esempio.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/it\/convertire-la-variabile-categoriale-in-panda-digitali\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-22T13:25:44+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\/convertire-la-variabile-categoriale-in-panda-digitali\/\",\"url\":\"https:\/\/statorials.org\/it\/convertire-la-variabile-categoriale-in-panda-digitali\/\",\"name\":\"Come convertire una variabile categoriale in numerica in Pandas - Stology\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/it\/#website\"},\"datePublished\":\"2023-07-22T13:25:44+00:00\",\"dateModified\":\"2023-07-22T13:25:44+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/it\/#\/schema\/person\/0896f191fb9fb019f2cd8623112cb3ae\"},\"description\":\"Questo tutorial spiega come convertire una variabile categoriale in una variabile numerica in un DataFrame panda, con un esempio.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/it\/convertire-la-variabile-categoriale-in-panda-digitali\/#breadcrumb\"},\"inLanguage\":\"it-IT\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/it\/convertire-la-variabile-categoriale-in-panda-digitali\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/it\/convertire-la-variabile-categoriale-in-panda-digitali\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Casa\",\"item\":\"https:\/\/statorials.org\/it\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Come convertire una variabile categoriale in numerica in pandas\"}]},{\"@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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