{"id":4314,"date":"2023-07-12T01:56:40","date_gmt":"2023-07-12T01:56:40","guid":{"rendered":"https:\/\/statorials.org\/pl\/grupowanie-pand-wedlug-zasiegu\/"},"modified":"2023-07-12T01:56:40","modified_gmt":"2023-07-12T01:56:40","slug":"grupowanie-pand-wedlug-zasiegu","status":"publish","type":"post","link":"https:\/\/statorials.org\/pl\/grupowanie-pand-wedlug-zasiegu\/","title":{"rendered":"Pandy: jak grupowa\u0107 wed\u0142ug zakresu warto\u015bci"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">Mo\u017cesz u\u017cy\u0107 poni\u017cszej sk\u0142adni, aby u\u017cy\u0107 funkcji <strong>groupby()<\/strong> w pandach, aby pogrupowa\u0107 kolumn\u0119 wed\u0142ug zakresu warto\u015bci przed wykonaniem agregacji:<\/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;\">Ten konkretny przyk\u0142ad pogrupuje wiersze DataFrame wed\u0142ug nast\u0119puj\u0105cego zakresu warto\u015bci w kolumnie o nazwie <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;\">Nast\u0119pnie obliczy sum\u0119 warto\u015bci we wszystkich kolumnach DataFrame, u\u017cywaj\u0105c tych zakres\u00f3w warto\u015bci jako grup.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Poni\u017cszy przyk\u0142ad pokazuje, jak zastosowa\u0107 t\u0119 sk\u0142adni\u0119 w praktyce.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Przyk\u0142ad: Jak grupowa\u0107 wed\u0142ug zakresu warto\u015bci w Pandach<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Za\u0142\u00f3\u017cmy, \u017ce mamy nast\u0119puj\u0105c\u0105 ramk\u0119 danych pand, kt\u00f3ra zawiera informacje o rozmiarach r\u00f3\u017cnych sklep\u00f3w detalicznych i ich \u0142\u0105cznej sprzeda\u017cy:<\/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;\">Mo\u017cemy u\u017cy\u0107 poni\u017cszej sk\u0142adni, aby pogrupowa\u0107 ramk\u0119 DataFrame na podstawie okre\u015blonych zakres\u00f3w kolumny <strong>store_size<\/strong> , a nast\u0119pnie obliczy\u0107 sum\u0119 wszystkich pozosta\u0142ych kolumn w ramce DataFrame, u\u017cywaj\u0105c zakres\u00f3w jako 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;\">Z wyniku mo\u017cemy zobaczy\u0107:<\/span><\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">W przypadku wierszy z warto\u015bci\u0105 store_size z zakresu od 0 do 25 suma store_size wynosi <strong>39<\/strong> , a suma sprzeda\u017cy wynosi <strong>33<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">W przypadku wierszy z warto\u015bci\u0105 store_size z zakresu od 25 do 50 suma store_size wynosi <strong>100<\/strong> , a suma sprzeda\u017cy wynosi <strong>69<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">I tak dalej.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">Je\u015bli chcesz, mo\u017cesz r\u00f3wnie\u017c obliczy\u0107 sum\u0119 <strong>sprzeda\u017cy<\/strong> dla ka\u017cdego zakresu <strong>store_size<\/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;\">Mo\u017cesz tak\u017ce u\u017cy\u0107 funkcji NumPy <strong>arange(),<\/strong> aby podzieli\u0107 zmienn\u0105 na zakresy bez r\u0119cznego okre\u015blania ka\u017cdego punktu ci\u0119cia:<\/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;\">Nale\u017cy pami\u0119ta\u0107, \u017ce te wyniki s\u0105 zgodne z poprzednim przyk\u0142adem.<\/span><\/span><\/p>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\"><strong>Uwaga<\/strong> : pe\u0142n\u0105 dokumentacj\u0119 funkcji NumPy <strong>arange()<\/strong> mo\u017cna znale\u017a\u0107 <a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.arange.html\" target=\"_blank\" rel=\"noopener\">tutaj<\/a> .<\/span><\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Dodatkowe zasoby<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Poni\u017csze samouczki wyja\u015bniaj\u0105, jak wykonywa\u0107 inne typowe zadania w pandach:<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/pl\/liczba-grup-pand-jest-wyjatkowa\/\" target=\"_blank\" rel=\"noopener\">Pandy: Jak liczy\u0107 unikalne warto\u015bci za pomoc\u0105 groupby<\/a><br \/> <a href=\"https:\/\/statorials.org\/pl\/grupa-pand-wed\u0142ug-sredniej-i-std\/\" target=\"_blank\" rel=\"noopener\">Pandy: Jak obliczy\u0107 \u015bredni\u0105 i norm\u0119 kolumny w groupby<\/a><br \/> <a href=\"https:\/\/statorials.org\/pl\/pandy-grupuja-as_index\/\" target=\"_blank\" rel=\"noopener\">Pandy: Jak u\u017cywa\u0107 as_index w groupby<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Mo\u017cesz u\u017cy\u0107 poni\u017cszej sk\u0142adni, aby u\u017cy\u0107 funkcji groupby() w pandach, aby pogrupowa\u0107 kolumn\u0119 wed\u0142ug zakresu warto\u015bci przed wykonaniem agregacji: df. groupby (pd. cut (df[&#8217; my_column &#8217;], [0, 25, 50, 75, 100])). sum () Ten konkretny przyk\u0142ad pogrupuje wiersze DataFrame wed\u0142ug nast\u0119puj\u0105cego zakresu warto\u015bci w kolumnie o nazwie my_column : (0,25] (25, 50] (50, 75] (75, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-4314","post","type-post","status-publish","format-standard","hentry","category-przewodnik"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pandy: Jak grupowa\u0107 wed\u0142ug zakresu warto\u015bci \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"W tym samouczku wyja\u015bniono, jak u\u017cywa\u0107 funkcji groupby() w pandach z zakresem warto\u015bci, \u0142\u0105cznie z przyk\u0142adem.\" \/>\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\/pl\/grupowanie-pand-wedlug-zasiegu\/\" \/>\n<meta property=\"og:locale\" content=\"pl_PL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandy: Jak grupowa\u0107 wed\u0142ug zakresu warto\u015bci \u2013 Statorials\" \/>\n<meta property=\"og:description\" content=\"W tym samouczku wyja\u015bniono, jak u\u017cywa\u0107 funkcji groupby() w pandach z zakresem warto\u015bci, \u0142\u0105cznie z przyk\u0142adem.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/pl\/grupowanie-pand-wedlug-zasiegu\/\" \/>\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=\"Napisane przez\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin Anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Szacowany czas czytania\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minuty\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/pl\/grupowanie-pand-wedlug-zasiegu\/\",\"url\":\"https:\/\/statorials.org\/pl\/grupowanie-pand-wedlug-zasiegu\/\",\"name\":\"Pandy: Jak grupowa\u0107 wed\u0142ug zakresu warto\u015bci \u2013 Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/pl\/#website\"},\"datePublished\":\"2023-07-12T01:56:40+00:00\",\"dateModified\":\"2023-07-12T01:56:40+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965\"},\"description\":\"W tym samouczku wyja\u015bniono, jak u\u017cywa\u0107 funkcji groupby() w pandach z zakresem warto\u015bci, \u0142\u0105cznie z przyk\u0142adem.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/pl\/grupowanie-pand-wedlug-zasiegu\/#breadcrumb\"},\"inLanguage\":\"pl-PL\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/pl\/grupowanie-pand-wedlug-zasiegu\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/pl\/grupowanie-pand-wedlug-zasiegu\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Dom\",\"item\":\"https:\/\/statorials.org\/pl\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Pandy: jak grupowa\u0107 wed\u0142ug zakresu warto\u015bci\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/pl\/#website\",\"url\":\"https:\/\/statorials.org\/pl\/\",\"name\":\"Statorials\",\"description\":\"Tw\u00f3j przewodnik po kompetencjach statystycznych!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/pl\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"pl-PL\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965\",\"name\":\"Benjamin Anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pl-PL\",\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/statorials.org\/pl\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"https:\/\/statorials.org\/pl\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin Anderson\"},\"description\":\"Cze\u015b\u0107, jestem Benjamin i jestem emerytowanym profesorem statystyki, kt\u00f3ry zosta\u0142 oddanym nauczycielem Statorials. 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