{"id":4314,"date":"2023-07-12T01:56:40","date_gmt":"2023-07-12T01:56:40","guid":{"rendered":"https:\/\/statorials.org\/id\/kelompok-panda-berdasarkan-jangkauan\/"},"modified":"2023-07-12T01:56:40","modified_gmt":"2023-07-12T01:56:40","slug":"kelompok-panda-berdasarkan-jangkauan","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/kelompok-panda-berdasarkan-jangkauan\/","title":{"rendered":"Pandas: cara mengelompokkan berdasarkan rentang nilai"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">Anda dapat menggunakan sintaks berikut untuk menggunakan fungsi <strong>groupby()<\/strong> di panda untuk mengelompokkan kolom berdasarkan rentang nilai sebelum melakukan agregasi:<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">my_column<\/span> '], [0, 25, 50, 75, 100])). <span style=\"color: #3366ff;\">sum<\/span> ()\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Contoh khusus ini akan mengelompokkan baris DataFrame menurut rentang nilai berikut di kolom yang disebut <strong>my_column<\/strong> :<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">(0,25]<\/span><\/li>\n<li> <span style=\"color: #000000;\">(25, 50]<\/span><\/li>\n<li> <span style=\"color: #000000;\">(50, 75]<\/span><\/li>\n<li> <span style=\"color: #000000;\">(75, 100]<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Ini kemudian akan menghitung jumlah nilai di semua kolom DataFrame menggunakan rentang nilai ini sebagai grup.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Contoh berikut menunjukkan cara menggunakan sintaksis ini dalam praktiknya.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Contoh: Cara mengelompokkan berdasarkan rentang nilai di Pandas<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Mari kita asumsikan kita memiliki pandas DataFrame berikut yang berisi informasi tentang ukuran berbagai toko ritel dan total penjualannya:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><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;\">store_size<\/span> ': [14, 25, 26, 29, 45, 58, 67, 81, 90, 98],\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [15, 18, 24, 25, 20, 35, 34, 49, 44, 49]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n   store_size sales\n0 14 15\n1 25 18\n2 26 24\n3 29 25\n4 45 20\n5 58 35\n6 67 34\n7 81 49\n8 90 44\n9 98 49\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">Kita dapat menggunakan sintaks berikut untuk mengelompokkan DataFrame berdasarkan rentang spesifik kolom <strong>store_size<\/strong> , lalu menghitung jumlah semua kolom lain di DataFrame menggunakan rentang tersebut sebagai grup:<\/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;\">#group by ranges of store_size and calculate sum of all columns\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">store_size<\/span> '], [0, 25, 50, 75, 100])). <span style=\"color: #3366ff;\">sum<\/span> ()\n\n\t store_size sales\nstore_size\t\t\n(0.25] 39 33\n(25, 50] 100 69\n(50, 75] 125 69\n(75, 100] 269 142\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">Dari hasilnya kita dapat melihat:<\/span><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Untuk baris dengan nilai store_size antara 0 dan 25, jumlah store_size adalah <strong>39<\/strong> dan jumlah penjualan adalah <strong>33<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">Untuk baris dengan nilai store_size antara 25 dan 50, jumlah store_size adalah <strong>100<\/strong> dan jumlah sales adalah <strong>69<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Dan seterusnya.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">Jika mau, Anda juga dapat menghitung jumlah <strong>penjualan<\/strong> saja untuk setiap rentang <strong>ukuran_toko<\/strong> :<\/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;\">#group by ranges of store_size and calculate sum of sales\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">store_size<\/span> '], [0, 25, 50, 75, 100]))[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">sum<\/span> ()\n\nstore_size\n(0.25] 33\n(25, 50] 69\n(50, 75] 69\n(75, 100] 142\nName: sales, dtype: int64<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Anda juga dapat menggunakan fungsi NumPy <strong>arange()<\/strong> untuk memecah variabel menjadi beberapa rentang tanpa menentukan setiap titik potong secara manual:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np<\/span>\n\n#group by ranges of store_size and calculate sum of sales\n<\/span>df. <span style=\"color: #3366ff;\">groupby<\/span> (pd. <span style=\"color: #3366ff;\">cut<\/span> (df[' <span style=\"color: #ff0000;\">store_size<\/span> '], np. <span style=\"color: #3366ff;\">arange<\/span> (0, 101, 25)))[' <span style=\"color: #ff0000;\">sales<\/span> ']. <span style=\"color: #3366ff;\">sum<\/span> ()\n\nstore_size\n(0.25] 33\n(25, 50] 69\n(50, 75] 69\n(75, 100] 142\nName: sales, dtype: int64<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">Perhatikan bahwa hasil ini cocok dengan contoh sebelumnya.<\/span><\/span><\/p>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\"><strong>Catatan<\/strong> : Anda dapat menemukan dokumentasi lengkap untuk fungsi NumPy <strong>arange()<\/strong> <a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.arange.html\" target=\"_blank\" rel=\"noopener\">di sini<\/a> .<\/span><\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Sumber daya tambahan<\/strong><\/span><\/h2>\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-dikelompokkan-berdasarkan-jumlah-unik\/\" target=\"_blank\" rel=\"noopener\">Pandas: Cara menghitung nilai unik menggunakan groupby<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/grup-panda-dengan-mean-dan-std\/\" target=\"_blank\" rel=\"noopener\">Pandas: Cara menghitung mean dan norma kolom di groupby<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/panda-dikelompokkan-berdasarkan-as_index\/\" target=\"_blank\" rel=\"noopener\">Pandas: Cara menggunakan as_index di groupby<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Anda dapat menggunakan sintaks berikut untuk menggunakan fungsi groupby() di panda untuk mengelompokkan kolom berdasarkan rentang nilai sebelum melakukan agregasi: df. groupby (pd. cut (df[&#8216; my_column &#8216;], [0, 25, 50, 75, 100])). sum () Contoh khusus ini akan mengelompokkan baris DataFrame menurut rentang nilai berikut di kolom yang disebut my_column : (0,25] (25, 50] (50, [&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>Pandas: Cara mengelompokkan berdasarkan rentang nilai - Statorials<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara menggunakan fungsi groupby() di panda dengan berbagai nilai, termasuk contohnya.\" \/>\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\/kelompok-panda-berdasarkan-jangkauan\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: Cara mengelompokkan berdasarkan rentang nilai - Statorials\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara menggunakan fungsi groupby() di panda dengan berbagai nilai, termasuk contohnya.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/kelompok-panda-berdasarkan-jangkauan\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-12T01:56:40+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\/kelompok-panda-berdasarkan-jangkauan\/\",\"url\":\"https:\/\/statorials.org\/id\/kelompok-panda-berdasarkan-jangkauan\/\",\"name\":\"Pandas: Cara mengelompokkan berdasarkan rentang nilai - Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-12T01:56:40+00:00\",\"dateModified\":\"2023-07-12T01:56:40+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara menggunakan fungsi groupby() di panda dengan berbagai nilai, termasuk contohnya.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/kelompok-panda-berdasarkan-jangkauan\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/kelompok-panda-berdasarkan-jangkauan\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/kelompok-panda-berdasarkan-jangkauan\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Pandas: cara mengelompokkan berdasarkan rentang nilai\"}]},{\"@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. 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