<\/p>\n
Aby pomno\u017cy\u0107 dwie kolumny w ramce DataFrame pandy, mo\u017cesz u\u017cy\u0107 nast\u0119puj\u0105cych metod:<\/span><\/p>\n Metoda 1: Pomn\u00f3\u017c dwie kolumny<\/strong><\/span><\/p>\n Metoda 2: Pomn\u00f3\u017c dwie kolumny na podstawie warunku<\/strong><\/span><\/p>\n Poni\u017csze przyk\u0142ady pokazuj\u0105, jak zastosowa\u0107 ka\u017cd\u0105 metod\u0119 w praktyce.<\/span><\/p>\n Za\u0142\u00f3\u017cmy, \u017ce mamy nast\u0119puj\u0105c\u0105 ramk\u0119 DataFrame pand:<\/span><\/span><\/p>\n Mo\u017cemy u\u017cy\u0107 nast\u0119puj\u0105cej sk\u0142adni, aby pomno\u017cy\u0107 kolumny ceny<\/strong> i kwoty<\/strong> i utworzy\u0107 now\u0105 kolumn\u0119 o nazwie przychody<\/strong> :<\/span><\/p>\n Nale\u017cy pami\u0119ta\u0107, \u017ce warto\u015bci w kolumnie Nowy przych\u00f3d<\/b> s\u0105 iloczynem warto\u015bci w kolumnach Cena<\/strong> i Kwota<\/strong> .<\/span><\/p>\n Za\u0142\u00f3\u017cmy, \u017ce mamy nast\u0119puj\u0105c\u0105 ramk\u0119 DataFrame pand:<\/span><\/p>\n Mo\u017cemy pomno\u017cy\u0107 przez siebie kolumny cena<\/strong> i ilo\u015b\u0107<\/strong> , a nast\u0119pnie za pomoc\u0105 funkcji Where ()<\/strong> zmieni\u0107 wyniki w oparciu o warto\u015b\u0107 kolumny type<\/strong> :<\/span><\/p>\n Nale\u017cy pami\u0119ta\u0107, \u017ce kolumna Doch\u00f3d<\/b> przyjmuje nast\u0119puj\u0105ce warto\u015bci:<\/span><\/p>\n Poni\u017csze samouczki wyja\u015bniaj\u0105, jak wykonywa\u0107 inne typowe zadania w pandach:<\/span><\/p>\n Jak wybra\u0107 kolumny wed\u0142ug indeksu w ramce danych Pandas<\/a> Aby pomno\u017cy\u0107 dwie kolumny w ramce DataFrame pandy, mo\u017cesz u\u017cy\u0107 nast\u0119puj\u0105cych metod: Metoda 1: Pomn\u00f3\u017c dwie kolumny df[’ new_column ’] = df. column1 * df. column2 Metoda 2: Pomn\u00f3\u017c dwie kolumny na podstawie warunku new_column = df. column1 * df. column2 #update values based on condition df[’ new_column ’] = new_column. where (df. column2 == […]<\/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-3514","post","type-post","status-publish","format-standard","hentry","category-przewodnik"],"yoast_head":"\n df[' new_column<\/span> '] = df. column1<\/span> * df. column2<\/span><\/strong><\/pre>\n new_column = df. column1<\/span> * df. column2<\/span>\n\n#update values based on condition\n<\/span>df[' new_column<\/span> '] = new_column. where<\/span> (df. column2<\/span> == ' value1<\/span> ', other= 0<\/span> )\n<\/strong><\/pre>\n Przyk\u0142ad 1: Pomn\u00f3\u017c dwie kolumny<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' price<\/span> ': [22, 20, 25, 30, 4, 8, 12, 10],\n ' amount<\/span> ': [3, 1, 3, 3, 2, 4, 3, 5]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n price amount\n0 22 3\n1 20 1\n2 25 3\n3 30 3\n4 4 2\n5 8 4\n6 12 3\n7 10 5<\/strong><\/pre>\n #multiply price and amount columns\n<\/span>df[' revenue<\/span> '] = df. price<\/span> * df. amount<\/span>\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n price amount revenue\n0 22 3 66\n1 20 1 20\n2 25 3 75\n3 30 3 90\n4 4 2 8\n5 8 4 32\n6 12 3 36\n7 10 5 50<\/strong><\/pre>\n Przyk\u0142ad 2: Pomn\u00f3\u017c dwie kolumny na podstawie warunku<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd<\/span>\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' price<\/span> ': [22, 20, 25, 30, 4, 8, 12, 10],\n ' amount<\/span> ': [3, 1, 3, 3, 2, 4, 3, 5],\n ' type<\/span> ': ['Sale', 'Refund', 'Sale', 'Sale',\n 'Sale', 'Refund', 'Refund', 'Sale']})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n price amount type\n0 22 3 Dirty\n1 20 1 Refund\n2 25 3 Dirty\n3 30 3 Dirty\n4 4 2 Dirty\n5 8 4 Refund\n6 12 3 Return\n7 10 5 Dirty<\/strong><\/pre>\n #multiply price and amount columns\n<\/span>income = df. price<\/span> * df. amount<\/span>\n\n#update values based on type\n<\/span>df[' revenue<\/span> '] = revenue. where<\/span> (df. type<\/span> == ' Sale<\/span> ', other= 0<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n price amount type revenue\n0 22 3 Dirty 66\n1 20 1 Refund 0\n2 25 3 Dirty 75\n3 30 3 Dirty 90\n4 4 2 Dirty 8\n5 8 4 Refund 0\n6 12 3 Refund 0\n7 10 5 Dirty 50<\/strong><\/pre>\n\n
Dodatkowe zasoby<\/strong><\/span><\/h2>\n
Jak zmieni\u0107 nazw\u0119 indeksu w Pandas DataFrame<\/a>
Jak usun\u0105\u0107 kolumny wed\u0142ug indeksu w Pandach<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"