{"id":1733,"date":"2023-07-25T05:02:59","date_gmt":"2023-07-25T05:02:59","guid":{"rendered":"https:\/\/statorials.org\/pl\/pandy-upuszczaja-wiersze-z-wartoscia\/"},"modified":"2023-07-25T05:02:59","modified_gmt":"2023-07-25T05:02:59","slug":"pandy-upuszczaja-wiersze-z-wartoscia","status":"publish","type":"post","link":"https:\/\/statorials.org\/pl\/pandy-upuszczaja-wiersze-z-wartoscia\/","title":{"rendered":"Pandy: jak usun\u0105\u0107 wiersze zawieraj\u0105ce okre\u015blon\u0105 warto\u015b\u0107"},"content":{"rendered":"

<\/p>\n

\n

Mo\u017cesz u\u017cy\u0107 nast\u0119puj\u0105cej sk\u0142adni, aby usun\u0105\u0107 wiersze w ramce danych pand, kt\u00f3re zawieraj\u0105 okre\u015blon\u0105 warto\u015b\u0107 w okre\u015blonej kolumnie:<\/span><\/p>\n

 #drop rows that contain specific 'value' in 'column_name'<\/span>\ndf = df[df. column_name<\/span> !=<\/span> value<\/span> ]\n<\/strong><\/pre>\n
 Mo\u017cesz u\u017cy\u0107 nast\u0119puj\u0105cej sk\u0142adni, aby usun\u0105\u0107 wiersze w ramce danych pand, kt\u00f3re zawieraj\u0105 dowoln\u0105 warto\u015b\u0107 na okre\u015blonej li\u015bcie:<\/span><\/p>\n
 #define values<\/span>\nvalues = [value1, value2, value3, ...]\n\n#drop rows that contain any value in the list\n<\/span>df = df[df. column_name<\/span> . isin<\/span> (values) ==<\/span> False<\/span> ]<\/strong><\/pre>\n
 Poni\u017csze przyk\u0142ady pokazuj\u0105, jak u\u017cywa\u0107 tej sk\u0142adni w praktyce.<\/span><\/p>\n
 Przyk\u0142ad 1: Usu\u0144 wiersze zawieraj\u0105ce okre\u015blon\u0105 warto\u015b\u0107<\/strong><\/span><\/h3>\n
 Poni\u017cszy kod pokazuje, jak usun\u0105\u0107 wszystkie wiersze zawieraj\u0105ce okre\u015blon\u0105 warto\u015b\u0107 w kolumnie:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],\n                   ' name<\/span> ': ['Dirk', 'Kobe', 'Tim', 'Lebron'],\n                   ' rebounds<\/span> ': [11, 7, 14, 7],\n                   ' points<\/span> ': [26, 31, 22, 29]})\n\n#view DataFrame\n<\/span>df\n\n        team name rebound points\n0 Mavs Dirk 11 26\n1 Lakers Kobe 7 31\n2 Spurs Tim 14 22\n3 Cavs LeBron 7 29\n\n#drop any rows that have 7 in the rebounds column\n<\/span>df = df[df. rebounds<\/span> !=<\/span> 7<\/span> ]\n\n#view resulting DataFrame<\/span>\ndf\n\n        team name rebound points\n0 Mavs Dirk 11 26\n2 Spurs Tim 14 22<\/strong><\/span><\/pre>\n
 Przyk\u0142ad 2: Usu\u0144 wiersze zawieraj\u0105ce warto\u015bci z listy<\/strong><\/span><\/h3>\n
 Poni\u017cszy kod pokazuje, jak usun\u0105\u0107 wszystkie wiersze z ramki DataFrame zawieraj\u0105ce warto\u015b\u0107 na li\u015bcie:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],\n                   ' name<\/span> ': ['Dirk', 'Kobe', 'Tim', 'Lebron'],\n                   ' rebounds<\/span> ': [11, 7, 14, 7],\n                   ' points<\/span> ': [26, 31, 22, 29]})\n\n#view DataFrame\n<\/span>df\n\n        team name rebound points\n0 Mavs Dirk 11 26\n1 Lakers Kobe 7 31\n2 Spurs Tim 14 22\n3 Cavs LeBron 7 29\n\n#define list of values<\/span>\nvalues = [7, 11]\n\n#drop any rows that have 7 or 11 in the rebounds column\n<\/span>df = df[df. rebounds<\/span> . isin<\/span> (values) ==<\/span> False<\/span> ]\n\n#view resulting DataFrame<\/span>\ndf\n\n        team name rebound points\n2 Spurs Tim 14 22<\/strong><\/span><\/pre>\n
 Przyk\u0142ad 3: Usu\u0144 wiersze zawieraj\u0105ce okre\u015blone warto\u015bci w wielu kolumnach<\/strong><\/span><\/h3>\n
 Poni\u017cszy kod pokazuje, jak usun\u0105\u0107 wiersze z ramki DataFrame zawieraj\u0105ce okre\u015blon\u0105 warto\u015b\u0107 w jednej z kilku kolumn:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],\n                   ' name<\/span> ': ['Dirk', 'Kobe', 'Tim', 'Lebron'],\n                   ' rebounds<\/span> ': [11, 7, 14, 7],\n                   ' points<\/span> ': [26, 31, 22, 29]})\n\n#view DataFrame\n<\/span>df\n\n        team name rebound points\n0 Mavs Dirk 11 26\n1 Lakers Kobe 7 31\n2 Spurs Tim 14 22\n3 Cavs LeBron 7 29\n\n#drop any rows that have 11 in the rebounds column or 31 in the points column\n<\/span>df = df[(df. rebounds<\/span> !=<\/span> 11<\/span> ) & (df. points<\/span> !=<\/span> 31<\/span> )]\n\n#view resulting DataFrame\n<\/span>df\n\nteam name rebound points\n2 Spurs Tim 14 22\n3 Cavs LeBron 7 29\n<\/strong><\/span><\/pre>\n
 Dodatkowe zasoby<\/strong><\/span><\/h3>\n
 Jak usun\u0105\u0107 wiersze wed\u0142ug indeksu w Pandach<\/a>
 Jak usun\u0105\u0107 kolumny wed\u0142ug indeksu w Pandach<\/a>
 Jak usun\u0105\u0107 wiersze zawieraj\u0105ce okre\u015blony ci\u0105g w Pandach<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
Mo\u017cesz u\u017cy\u0107 nast\u0119puj\u0105cej sk\u0142adni, aby usun\u0105\u0107 wiersze w ramce danych pand, kt\u00f3re zawieraj\u0105 okre\u015blon\u0105 warto\u015b\u0107 w okre\u015blonej kolumnie: #drop rows that contain specific 'value’ in 'column_name’ df = df[df. column_name != value ] Mo\u017cesz u\u017cy\u0107 nast\u0119puj\u0105cej sk\u0142adni, aby usun\u0105\u0107 wiersze w ramce danych pand, kt\u00f3re zawieraj\u0105 dowoln\u0105 warto\u015b\u0107 na okre\u015blonej li\u015bcie: #define values values = […]<\/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-1733","post","type-post","status-publish","format-standard","hentry","category-przewodnik"],"yoast_head":"\nPandy: jak usun\u0105\u0107 wiersze zawieraj\u0105ce okre\u015blon\u0105 warto\u015b\u0107<\/title>\n<meta name=\"description\" content=\"W 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