<\/p>\n
Mo\u017cesz u\u017cy\u0107 nast\u0119puj\u0105cej podstawowej sk\u0142adni, aby obliczy\u0107 skumulowany procent warto\u015bci w kolumnie pandy DataFrame:<\/span><\/p>\n Poni\u017cszy przyk\u0142ad pokazuje, jak zastosowa\u0107 t\u0119 sk\u0142adni\u0119 w praktyce.<\/span><\/p>\n Za\u0142\u00f3\u017cmy, \u017ce mamy nast\u0119puj\u0105c\u0105 ramk\u0119 danych pand, kt\u00f3ra pokazuje liczb\u0119 jednostek sprzedawanych przez firm\u0119 w kolejnych latach:<\/span><\/p>\n Nast\u0119pnie mo\u017cemy u\u017cy\u0107 poni\u017cszego kodu, aby doda\u0107 kolumn\u0119 wy\u015bwietlaj\u0105c\u0105 skumulowan\u0105 liczb\u0119 sprzedanych jednostek i skumulowany procent sprzedanych jednostek:<\/span><\/span><\/p>\n Skumulowane warto\u015bci procentowe interpretujemy w nast\u0119puj\u0105cy spos\u00f3b:<\/span><\/p>\n I tak dalej.<\/span><\/p>\n Pami\u0119taj, \u017ce mo\u017cesz po prostu zmieni\u0107 warto\u015b\u0107 w funkcji round(),<\/strong> aby zmieni\u0107 tak\u017ce liczb\u0119 wy\u015bwietlanych miejsc dziesi\u0119tnych.<\/span><\/p>\n Na przyk\u0142ad mo\u017cemy zamiast tego zaokr\u0105gli\u0107 skumulowany procent do zera miejsc po przecinku:<\/span><\/p>\n Skumulowane warto\u015bci procentowe s\u0105 teraz zaokr\u0105glane do zera miejsc po przecinku.<\/span><\/p>\n Poni\u017csze samouczki wyja\u015bniaj\u0105, jak wykonywa\u0107 inne typowe operacje w Pythonie:<\/span><\/p>\n Jak tworzy\u0107 tabele cz\u0119sto\u015bci w Pythonie<\/a> Mo\u017cesz u\u017cy\u0107 nast\u0119puj\u0105cej podstawowej sk\u0142adni, aby obliczy\u0107 skumulowany procent warto\u015bci w kolumnie pandy DataFrame: #calculate cumulative sum of column df[’ cum_sum ’] = df[’ col1 ’]. cumsum () #calculate cumulative percentage of column (rounded to 2 decimal places) df[’ cum_percent ’] = round( 100 *df. cum_sum \/df[’ col1 ’]. sum (), 2 ) Poni\u017cszy przyk\u0142ad […]<\/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-2570","post","type-post","status-publish","format-standard","hentry","category-przewodnik"],"yoast_head":"\n #calculate cumulative sum of column\n<\/span>df[' cum_sum<\/span> '] = df[' col1<\/span> ']. cumsum<\/span> ()\n\n#calculate cumulative percentage of column (rounded to 2 decimal places)\n<\/span>df[' cum_percent<\/span> '] = round( 100<\/span> *df. cum_sum<\/span> \/df[' col1<\/span> ']. sum<\/span> (), 2<\/span> )\n<\/strong><\/pre>\n Przyk\u0142ad: Oblicz skumulowany odsetek pand<\/strong><\/span><\/h3>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' year<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n ' units_sold<\/span> ': [60, 75, 77, 87, 104, 134, 120, 125, 140, 150]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n year units_sold\n0 1 60\n1 2 75\n2 3 77\n3 4 87\n4 5 104\n5 6 134\n6 7 120\n7 8 125\n8 9 140\n9 10 150\n<\/span><\/span><\/strong><\/pre>\n #calculate cumulative sum of units sold\n<\/span>df[' cum_sum<\/span> '] = df[' units_sold<\/span> ']. cumsum<\/span> ()\n\n#calculate cumulative percentage of units sold\n<\/span>df[' cum_percent<\/span> '] = round( 100<\/span> *df. cum_sum<\/span> \/df[' units_sold<\/span> ']. sum<\/span> (), 2<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n year units_sold cum_sum cum_percent\n0 1 60 60 5.60\n1 2 75 135 12.59\n2 3 77 212 19.78\n3 4 87 299 27.89\n4 5 104 403 37.59\n5 6 134 537 50.09\n6 7 120 657 61.29\n7 8 125 782 72.95\n8 9 140 922 86.01\n9 10 150 1072 100.00\n<\/span><\/span><\/strong><\/pre>\n\n
#calculate cumulative sum of units sold\n<\/span>df[' cum_sum<\/span> '] = df[' units_sold<\/span> ']. cumsum<\/span> ()\n\n#calculate cumulative percentage of units sold\n<\/span>df[' cum_percent<\/span> '] = round( 100<\/span> *df. cum_sum<\/span> \/df[' units_sold<\/span> ']. sum<\/span> (), 0<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n year units_sold cum_sum cum_percent\n0 1 60 60 6.0\n1 2 75 135 13.0\n2 3 77 212 20.0\n3 4 87 299 28.0\n4 5 104 403 38.0\n5 6 134 537 50.0\n6 7 120 657 61.0\n7 8 125 782 73.0\n8 9 140 922 86.0\n9 10 150 1072 100.0<\/span><\/span><\/strong><\/pre>\n Dodatkowe zasoby<\/strong><\/span><\/h3>\n
Jak obliczy\u0107 cz\u0119stotliwo\u015b\u0107 wzgl\u0119dn\u0105 w Pythonie<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"