{"id":2914,"date":"2023-07-20T03:08:41","date_gmt":"2023-07-20T03:08:41","guid":{"rendered":"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/"},"modified":"2023-07-20T03:08:41","modified_gmt":"2023-07-20T03:08:41","slug":"panda-pencocokan-kabur","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/","title":{"rendered":"Cara melakukan pencocokan fuzzy di pandas (dengan contoh)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Seringkali Anda mungkin ingin menggabungkan dua kumpulan data dalam panda berdasarkan string yang tidak cocok secara sempurna. Ini disebut <strong>pencocokan fuzzy<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Cara termudah untuk melakukan pencocokan fuzzy di panda adalah dengan menggunakan fungsi <strong>get_close_matches()<\/strong> dari paket <strong>difflib<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Contoh berikut menunjukkan cara menggunakan fungsi ini dalam praktiknya.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh: korespondensi fuzzy di panda<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Katakanlah kita memiliki dua panda DataFrames berikut yang berisi informasi tentang berbagai tim bola basket:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#create two DataFrames\n<\/span>df1 = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['Mavericks', 'Nets', 'Warriors', 'Heat', 'Lakers'],\n                    ' <span style=\"color: #ff0000;\">points<\/span> ': [99, 90, 104, 117, 100]})\n\ndf2 = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['Mavricks', 'Warrors', 'Heat', 'Netts', 'Lakes'],\n                    ' <span style=\"color: #ff0000;\">assists<\/span> ': [22, 29, 17, 40, 32]})\n\n<span style=\"color: #008080;\">#view DataFrames\n<\/span><span style=\"color: #008000;\">print<\/span> (df1)\n\n        team points\n0 Mavericks 99\n1 Nets 90\n2 Warriors 104\n3 Heat 117\n4 Lakers 100\n\n<span style=\"color: #008000;\">print<\/span> (df2)\n\n       team assists\n0 Mavricks 22\n1 Warriors 29\n2 Heat 17\n3 Netts 40\n4 Lakes 32<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Sekarang katakanlah kita ingin menggabungkan dua DataFrame berdasarkan kolom <strong>Tim<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Karena nama tim sedikit berbeda antara kedua DataFrame, kita perlu menggunakan pencocokan fuzzy untuk menemukan nama tim yang paling cocok.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kita dapat menggunakan fungsi <strong>get_close_matches()<\/strong> dari paket <strong>difflib<\/strong> untuk melakukan ini:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> difflib \n\n<span style=\"color: #008080;\">#create duplicate column to retain team name from df2\n<\/span>df2[' <span style=\"color: #ff0000;\">team_match<\/span> '] = df2[' <span style=\"color: #ff0000;\">team<\/span> ']\n\n<span style=\"color: #008080;\">#convert team name in df2 to team name it most closely matches in df1\n<\/span>df2[' <span style=\"color: #ff0000;\">team<\/span> '] = df2[' <span style=\"color: #ff0000;\">team<\/span> ']. <span style=\"color: #3366ff;\">apply<\/span> (lambda x: difflib. <span style=\"color: #3366ff;\">get_close_matches<\/span> (x, df1[' <span style=\"color: #ff0000;\">team<\/span> '])[ <span style=\"color: #008000;\">0<\/span> ])\n\n<span style=\"color: #008080;\">#merge the DataFrames into one<\/span>\ndf3 = df1. <span style=\"color: #3366ff;\">merge<\/span> (df2)\n\n<span style=\"color: #008080;\">#view final DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df3)\n\n        team points assists team_match\n0 Mavericks 99 22 Mavricks\n1 Nets 90 40 Nets\n2 Warriors 104 29 Warriors\n3 Heat 117 17 Heat\n4 Lakers 100 32 Lakes<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Hasilnya adalah bingkai data yang berisi lima nama tim dari DataFrame pertama serta tim yang paling cocok dengan DataFrame kedua.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kolom <strong>team_match<\/strong> menampilkan nama tim dari DataFrame kedua yang paling cocok dengan nama tim dari DataFrame pertama.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Catatan #1<\/strong> : Secara default, <strong>get_close_matches()<\/strong> mengembalikan tiga kecocokan terdekat. Namun, dengan menggunakan <strong>[0]<\/strong> di akhir fungsi lambda, kami hanya dapat mengembalikan nama tim yang paling cocok.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Catatan #2:<\/strong> Anda dapat menemukan dokumentasi lengkap untuk fungsi <strong>get_close_matches()<\/strong> <a href=\"https:\/\/docs.python.org\/3\/library\/difflib.html\" target=\"_blank\" rel=\"noopener\">di sini<\/a> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Sumber daya tambahan<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Tutorial berikut menjelaskan cara melakukan tugas umum lainnya di panda:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/id\/panda-menggabungkan-beberapa-kolom\/\" target=\"_blank\" rel=\"noopener\">Cara menggabungkan Pandas DataFrames di beberapa kolom<\/a><br \/> Cara menggabungkan dua Pandas DataFrames di index<br \/> <a href=\"https:\/\/statorials.org\/id\/panda-bergabung-vs-bergabung\/\" target=\"_blank\" rel=\"noopener\">Pandas Bergabung atau Bergabung: Apa bedanya?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Seringkali Anda mungkin ingin menggabungkan dua kumpulan data dalam panda berdasarkan string yang tidak cocok secara sempurna. Ini disebut pencocokan fuzzy . Cara termudah untuk melakukan pencocokan fuzzy di panda adalah dengan menggunakan fungsi get_close_matches() dari paket difflib . Contoh berikut menunjukkan cara menggunakan fungsi ini dalam praktiknya. Contoh: korespondensi fuzzy di panda Katakanlah kita [&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>Cara Melakukan Pencocokan Fuzzy di Pandas (dengan Contoh) - Statorials<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara melakukan pencocokan fuzzy di panda, termasuk contoh lengkapnya.\" \/>\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\/id\/panda-pencocokan-kabur\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara Melakukan Pencocokan Fuzzy di Pandas (dengan Contoh) - Statorials\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara melakukan pencocokan fuzzy di panda, termasuk contoh lengkapnya.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-20T03:08:41+00:00\" \/>\n<meta name=\"author\" content=\"Benjamin anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Ditulis oleh\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimasi waktu membaca\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 menit\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/\",\"url\":\"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/\",\"name\":\"Cara Melakukan Pencocokan Fuzzy di Pandas (dengan Contoh) - Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-20T03:08:41+00:00\",\"dateModified\":\"2023-07-20T03:08:41+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara melakukan pencocokan fuzzy di panda, termasuk contoh lengkapnya.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara melakukan pencocokan fuzzy di pandas (dengan contoh)\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/id\/#website\",\"url\":\"https:\/\/statorials.org\/id\/\",\"name\":\"Statorials\",\"description\":\"Panduan anda untuk kompetensi statistik!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/id\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"id\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\",\"name\":\"Benjamin anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"id\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/image\/\",\"url\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin anderson\"},\"description\":\"Halo, saya Benjamin, pensiunan profesor statistika yang menjadi guru Statorial yang berdedikasi. Dengan pengalaman dan keahlian yang luas di bidang statistika, saya ingin berbagi ilmu untuk memberdayakan mahasiswa melalui Statorials. Baca selengkapnya\",\"sameAs\":[\"http:\/\/statorials.org\/id\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Cara Melakukan Pencocokan Fuzzy di Pandas (dengan Contoh) - Statorials","description":"Tutorial ini menjelaskan cara melakukan pencocokan fuzzy di panda, termasuk contoh lengkapnya.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/","og_locale":"id_ID","og_type":"article","og_title":"Cara Melakukan Pencocokan Fuzzy di Pandas (dengan Contoh) - Statorials","og_description":"Tutorial ini menjelaskan cara melakukan pencocokan fuzzy di panda, termasuk contoh lengkapnya.","og_url":"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/","og_site_name":"Statorials","article_published_time":"2023-07-20T03:08:41+00:00","author":"Benjamin anderson","twitter_card":"summary_large_image","twitter_misc":{"Ditulis oleh":"Benjamin anderson","Estimasi waktu membaca":"2 menit"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/","url":"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/","name":"Cara Melakukan Pencocokan Fuzzy di Pandas (dengan Contoh) - Statorials","isPartOf":{"@id":"https:\/\/statorials.org\/id\/#website"},"datePublished":"2023-07-20T03:08:41+00:00","dateModified":"2023-07-20T03:08:41+00:00","author":{"@id":"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81"},"description":"Tutorial ini menjelaskan cara melakukan pencocokan fuzzy di panda, termasuk contoh lengkapnya.","breadcrumb":{"@id":"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/#breadcrumb"},"inLanguage":"id","potentialAction":[{"@type":"ReadAction","target":["https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/statorials.org\/id\/panda-pencocokan-kabur\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/statorials.org\/id\/"},{"@type":"ListItem","position":2,"name":"Cara melakukan pencocokan fuzzy di pandas (dengan contoh)"}]},{"@type":"WebSite","@id":"https:\/\/statorials.org\/id\/#website","url":"https:\/\/statorials.org\/id\/","name":"Statorials","description":"Panduan anda untuk kompetensi statistik!","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/statorials.org\/id\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"id"},{"@type":"Person","@id":"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81","name":"Benjamin anderson","image":{"@type":"ImageObject","inLanguage":"id","@id":"https:\/\/statorials.org\/id\/#\/schema\/person\/image\/","url":"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg","contentUrl":"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg","caption":"Benjamin anderson"},"description":"Halo, saya Benjamin, pensiunan profesor statistika yang menjadi guru Statorial yang berdedikasi. Dengan pengalaman dan keahlian yang luas di bidang statistika, saya ingin berbagi ilmu untuk memberdayakan mahasiswa melalui Statorials. Baca selengkapnya","sameAs":["http:\/\/statorials.org\/id"]}]}},"yoast_meta":{"yoast_wpseo_title":"","yoast_wpseo_metadesc":"","yoast_wpseo_canonical":""},"_links":{"self":[{"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/posts\/2914"}],"collection":[{"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/comments?post=2914"}],"version-history":[{"count":0,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/posts\/2914\/revisions"}],"wp:attachment":[{"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/media?parent=2914"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/categories?post=2914"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/tags?post=2914"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}