{"id":902,"date":"2023-07-28T09:10:08","date_gmt":"2023-07-28T09:10:08","guid":{"rendered":"https:\/\/statorials.org\/it\/digitalizzare-numpy\/"},"modified":"2023-07-28T09:10:08","modified_gmt":"2023-07-28T09:10:08","slug":"digitalizzare-numpy","status":"publish","type":"post","link":"https:\/\/statorials.org\/it\/digitalizzare-numpy\/","title":{"rendered":"Come raggruppare le variabili in python usando numpy.digitize()"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Spesso potresti essere interessato a mettere i valori di una variabile in &#8220;bin&#8221; in Python.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Fortunatamente, questo \u00e8 facile da fare utilizzando la funzione <a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.digitize.html\" target=\"_blank\" rel=\"noopener\">numpy.digitize()<\/a> , che utilizza la seguente sintassi:<br \/><\/span><\/p>\n<p> <strong><span style=\"color: #000000;\">numpy.digitize(x, bin, destra=False)<\/span><\/strong><\/p>\n<p> <span style=\"color: #000000;\">Oro:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>x:<\/strong> array da raggruppare.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>bin:<\/strong> array di bin.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>destra:<\/strong> indica se gli intervalli includono il bordo destro o sinistro del contenitore. Per impostazione predefinita, l&#8217;intervallo non include il bordo destro.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Questo tutorial mostra diversi esempi di utilizzo pratico di questa funzione.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Esempio 1: posiziona tutti i valori in due contenitori<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Il codice seguente mostra come posizionare i valori di un array in due contenitori:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>0<\/strong> se x &lt; 20<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>1<\/strong> se x \u2265 20<\/span><\/li>\n<\/ul>\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\n<span style=\"color: #008080;\">#create data<\/span>\ndata = [2, 4, 4, 7, 12, 14, 19, 20, 24, 31, 34]\n\n<span style=\"color: #008080;\">#place values into bins\n<span style=\"color: #000000;\">n.p. <span style=\"color: #3366ff;\">digitize<\/span> (data, bins=[20])\n\narray([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1])\n<\/span><\/span><\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Esempio 2: posizionare tutti i valori in tre contenitori<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Il codice seguente mostra come posizionare i valori di un array in tre contenitori:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>0<\/strong> se x &lt; 10<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>1<\/strong> se 10 \u2264 x &lt; 20<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>2<\/strong> se x \u2265 20<\/span><\/li>\n<\/ul>\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\n<span style=\"color: #008080;\">#create data<\/span>\ndata = [2, 4, 4, 7, 12, 14, 20, 22, 24, 31, 34]\n\n<span style=\"color: #008080;\">#place values into bins\n<span style=\"color: #000000;\">n.p. <span style=\"color: #3366ff;\">digitize<\/span> (data, bins=[10, 20])\n\narray([0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2])<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Tieni presente che se specifichiamo right= <strong>True<\/strong> , i valori verranno inseriti nei seguenti contenitori:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>0<\/strong> se x \u2264 10<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>1<\/strong> se 10 &lt; x \u2264 20<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>2<\/strong> se x &gt; 20<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Ogni intervallo includerebbe il bordo destro del contenitore. Ecco come appare:<\/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\n<span style=\"color: #008080;\">#createdata<\/span>\ndata = [2, 4, 4, 7, 12, 14, 20, 22, 24, 31, 34]\n\n<span style=\"color: #008080;\">#place values into bins\n<span style=\"color: #000000;\">n.p. <span style=\"color: #3366ff;\">digitize<\/span> (data, bins=[10, 20], right= <span style=\"color: #008000;\">True<\/span> )\n\narray([0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2])<\/span><\/span><\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Esempio 3: posizionare tutti i valori in quattro contenitori<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Il codice seguente mostra come posizionare i valori di un array in tre contenitori:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>0<\/strong> se x &lt; 10<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>1<\/strong> se 10 \u2264 x &lt; 20<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>2<\/strong> se 20 \u2264 x &lt; 30<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>3<\/strong> se x \u2265 30<\/span><\/li>\n<\/ul>\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\n<span style=\"color: #008080;\">#createdata<\/span>\ndata = [2, 4, 4, 7, 12, 14, 20, 22, 24, 31, 34]\n\n<span style=\"color: #008080;\">#place values into bins\n<span style=\"color: #000000;\">n.p. <span style=\"color: #3366ff;\">digitize<\/span> (data, bins=[10, 20, 30])\n\narray([0, 0, 0, 0, 1, 1, 2, 2, 2, 3, 3])\n<\/span><\/span><\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Esempio 4: contare la frequenza di ciascun contenitore<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Un&#8217;altra utile funzione NumPy che integra la funzione numpy.digitize() \u00e8 la funzione <a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.bincount.html#numpy.bincount\" target=\"_blank\" rel=\"noopener\">numpy.bincount()<\/a> , che conta le frequenze di ciascun contenitore.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Il codice seguente mostra come posizionare i valori di un array in tre gruppi, quindi contare la frequenza di ciascun gruppo:<\/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\n<span style=\"color: #008080;\">#createdata<\/span>\ndata = [2, 4, 4, 7, 12, 14, 20, 22, 24, 31, 34]\n\n<span style=\"color: #008080;\">#place values into bins\n<span style=\"color: #000000;\">bin_data = np. <span style=\"color: #3366ff;\">digitize<\/span> (data, bins=[10, 20])\n\n<span style=\"color: #008080;\">#view binned data<\/span>\nbin_data\n\narray([0, 0, 0, 0, 1, 1, 2, 2, 2, 2, 2])\n\n<span style=\"color: #008080;\">#count frequency of each bin\n<span style=\"color: #000000;\">n.p. <span style=\"color: #3366ff;\">bincount<\/span> (bin_data)\n\narray([4, 2, 5])\n<\/span><\/span><\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">L&#8217;output ci dice che:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Il contenitore \u201c0\u201d contiene <strong>4<\/strong> valori di dati.<\/span><\/li>\n<li> <span style=\"color: #000000;\">Il contenitore \u201c1\u201d contiene <strong>2<\/strong> valori di dati.<\/span><\/li>\n<li> <span style=\"color: #000000;\">Il contenitore \u201c2\u201d contiene <strong>5<\/strong> valori di dati.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><em><span style=\"color: #000000;\">Trova altri tutorial Python qui .<\/span><\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Spesso potresti essere interessato a mettere i valori di una variabile in &#8220;bin&#8221; in Python. Fortunatamente, questo \u00e8 facile da fare utilizzando la funzione numpy.digitize() , che utilizza la seguente sintassi: numpy.digitize(x, bin, destra=False) Oro: x: array da raggruppare. bin: array di bin. destra: indica se gli intervalli includono il bordo destro o sinistro del [&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 raggruppare le variabili in Python usando numpy.digitize() - Statorials<\/title>\n<meta name=\"description\" content=\"Una semplice spiegazione su come raggruppare le variabili in Python utilizzando la funzione numpy.digitize().\" \/>\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\/digitalizzare-numpy\/\" \/>\n<meta property=\"og:locale\" content=\"it_IT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Come raggruppare le variabili in Python usando numpy.digitize() - Statorials\" \/>\n<meta property=\"og:description\" content=\"Una semplice spiegazione su come raggruppare le variabili in Python utilizzando la funzione numpy.digitize().\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/it\/digitalizzare-numpy\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-28T09:10:08+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\/digitalizzare-numpy\/\",\"url\":\"https:\/\/statorials.org\/it\/digitalizzare-numpy\/\",\"name\":\"Come raggruppare le variabili in Python usando numpy.digitize() - Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/it\/#website\"},\"datePublished\":\"2023-07-28T09:10:08+00:00\",\"dateModified\":\"2023-07-28T09:10:08+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/it\/#\/schema\/person\/0896f191fb9fb019f2cd8623112cb3ae\"},\"description\":\"Una semplice spiegazione su come raggruppare le variabili in Python utilizzando la funzione numpy.digitize().\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/it\/digitalizzare-numpy\/#breadcrumb\"},\"inLanguage\":\"it-IT\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/it\/digitalizzare-numpy\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/it\/digitalizzare-numpy\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Casa\",\"item\":\"https:\/\/statorials.org\/it\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Come raggruppare le variabili in python usando numpy.digitize()\"}]},{\"@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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