{"id":4161,"date":"2023-07-13T04:35:37","date_gmt":"2023-07-13T04:35:37","guid":{"rendered":"https:\/\/statorials.org\/pl\/slownik-pand-fillna\/"},"modified":"2023-07-13T04:35:37","modified_gmt":"2023-07-13T04:35:37","slug":"slownik-pand-fillna","status":"publish","type":"post","link":"https:\/\/statorials.org\/pl\/slownik-pand-fillna\/","title":{"rendered":"Pandy: jak wype\u0142ni\u0107 warto\u015bci nan za pomoc\u0105 s\u0142ownika"},"content":{"rendered":"

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Mo\u017cesz u\u017cy\u0107 funkcji fillna()<\/strong> ze s\u0142ownikiem, aby zast\u0105pi\u0107 warto\u015bci NaN w jednej kolumnie pandy DataFrame na podstawie warto\u015bci z innej kolumny.<\/span><\/p>\n

Aby to zrobi\u0107, mo\u017cesz u\u017cy\u0107 nast\u0119puj\u0105cej podstawowej sk\u0142adni:<\/span><\/p>\n

 #define dictionary<\/span>\ndict = {' A<\/span> ': 5<\/span> , ' B<\/span> ': 10<\/span> , ' C<\/span> ': 15<\/span> , ' D<\/span> ': 20<\/span> }\n\n#replace values in col2 based on dictionary values in col1\n<\/span>df[' col2<\/span> '] = df[' col2<\/span> ']. fillna<\/span> (df[' col1<\/span> ']. map<\/span> (dict))<\/strong><\/pre>\n
 Poni\u017cszy przyk\u0142ad pokazuje, jak zastosowa\u0107 t\u0119 sk\u0142adni\u0119 w praktyce.<\/span><\/p>\n
 Przyk\u0142ad: Wype\u0142nij warto\u015bci NaN w Pandach za pomoc\u0105 s\u0142ownika<\/strong><\/span><\/h2>\n
 Za\u0142\u00f3\u017cmy, \u017ce mamy nast\u0119puj\u0105c\u0105 ramk\u0119 DataFrame pand, kt\u00f3ra zawiera informacje o sprzeda\u017cy dokonanej w r\u00f3\u017cnych sklepach detalicznych:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' store<\/span> ': ['A', 'A', 'B', 'C', 'D', 'C', 'B', 'D'],\n                   ' sales<\/span> ': [12, np.nan, 30, np.nan, 24, np.nan, np.nan, 13]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  blind sales\n0 to 12.0\n1 A NaN\n2 B 30.0\n3 C NaN\n4 D 24.0\n5CNaN\n6BNaN\n7 D 13.0<\/strong><\/pre>\n
 Pami\u0119taj, \u017ce w kolumnie Sprzeda\u017c<\/strong> znajduje si\u0119 wiele warto\u015bci NaN.<\/span><\/p>\n
 Za\u0142\u00f3\u017cmy, \u017ce chcemy zape\u0142ni\u0107 te NaN w kolumnie sprzeda\u017cy<\/strong> warto\u015bciami odpowiadaj\u0105cymi konkretnym warto\u015bciom w kolumnie sklepu<\/strong> .<\/span><\/p>\n
 W tym celu mo\u017cemy u\u017cy\u0107 nast\u0119puj\u0105cej sk\u0142adni:<\/span><\/p>\n
 #define dictionary<\/span>\ndict = {' A<\/span> ': 5<\/span> , ' B<\/span> ': 10<\/span> , ' C<\/span> ': 15<\/span> , ' D<\/span> ': 20<\/span> }\n\n#replace values in sales column based on dictionary values in store column\n<\/span>df[' sales<\/span> '] = df[' sales<\/span> ']. fillna<\/span> (df[' store<\/span> ']. map<\/span> (dict))\n\n#view updated DataFrame<\/span>\nprint<\/span> (df)\n\n  blind sales\n0 to 12.0\n1 A 5.0\n2 B 30.0\n3C 15.0\n4 D 24.0\n5C 15.0\n6 B 10.0\n7 D 13.0\n<\/strong><\/pre>\n
 U\u017cyli\u015bmy s\u0142ownika, aby dokona\u0107 nast\u0119puj\u0105cych zamian w kolumnie sprzeda\u017cy:<\/span><\/p>\n