{"id":1360,"date":"2023-07-26T17:12:24","date_gmt":"2023-07-26T17:12:24","guid":{"rendered":"https:\/\/statorials.org\/my\/fillna-%e1%80%95%e1%80%94%e1%80%ba%e1%80%92%e1%80%ab%e1%80%99%e1%80%bb%e1%80%ad%e1%80%af%e1%80%b8%e1%80%85%e1%80%af%e1%80%b6%e1%80%80%e1%80%b1%e1%80%ac%e1%80%ba%e1%80%9c%e1%80%b6\/"},"modified":"2023-07-26T17:12:24","modified_gmt":"2023-07-26T17:12:24","slug":"fillna-%e1%80%95%e1%80%94%e1%80%ba%e1%80%92%e1%80%ab%e1%80%99%e1%80%bb%e1%80%ad%e1%80%af%e1%80%b8%e1%80%85%e1%80%af%e1%80%b6%e1%80%80%e1%80%b1%e1%80%ac%e1%80%ba%e1%80%9c%e1%80%b6","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/fillna-%e1%80%95%e1%80%94%e1%80%ba%e1%80%92%e1%80%ab%e1%80%99%e1%80%bb%e1%80%ad%e1%80%af%e1%80%b8%e1%80%85%e1%80%af%e1%80%b6%e1%80%80%e1%80%b1%e1%80%ac%e1%80%ba%e1%80%9c%e1%80%b6\/","title":{"rendered":"Pandas \u101b\u103e\u102d \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1021\u1010\u103d\u1000\u103a na \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103c\u100a\u1037\u103a\u1014\u100a\u103a\u1038"},"content":{"rendered":"

<\/p>\n

\n

pandas fillna()<\/a> function \u101e\u100a\u103a pandas DataFrame \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u1015\u103b\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1014\u1031\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f\u1016\u103c\u100a\u1037\u103a\u101b\u1014\u103a\u1021\u1010\u103d\u1000\u103a \u1021\u101e\u102f\u1036\u1038\u101d\u1004\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1015\u1014\u103a\u1012\u102b DataFrame \u101b\u103e\u102d \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u1019\u103b\u102c\u1038\u1021\u1015\u103c\u102c\u1038\u1021\u1010\u103d\u1000\u103a \u1015\u103b\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1014\u1031\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f\u1016\u103c\u100a\u1037\u103a\u101b\u1014\u103a \u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038\u104f\u1014\u1019\u1030\u1014\u102c\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1000\u102d\u102f \u1015\u1031\u1038\u1006\u1031\u102c\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({'team': ['A', np. nan<\/span> , 'B', 'B', 'B', 'C', 'C', 'C'],\n                   'points': [25, np. no<\/span> , 15, np. no<\/span> , 19, 23, 25, 29],\n                   'assists': [5, 7, 7, 9, 12, 9, np. no<\/span> , 4],\n                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})<\/span>\n\n#view DataFrame\nprint<\/span> (df)\n\n  team points assists rebounds\n0 A 25.0 5.0 11\n1 NaN NaN 7.0 8\n2 B 15.0 7.0 10\n3 B NaN 9.0 6\n4 B 19.0 12.0 6\n5 C 23.0 9.0 5\n6 C 25.0 NaN 9\n7 C 29.0 4.0 12<\/span>\n<\/span><\/strong><\/pre>\n
 \u1025\u1015\u1019\u102c 1- \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u102c\u1038\u101c\u102f\u1036\u1038\u104f \u1015\u103b\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1014\u1031\u101e\u1031\u102c \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103c\u100a\u1037\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a DataFrame \u101b\u103e\u102d \u1000\u1031\u102c\u103a\u101c\u1036 \u1021\u102c\u1038\u101c\u102f\u1036\u1038\u1021\u1010\u103d\u1000\u103a<\/em> \u1015\u103b\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1014\u1031\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101e\u102f\u100a\u1016\u103c\u1004\u1037\u103a \u1016\u103c\u100a\u1037\u103a\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span><\/p>\n
 #replace all missing values with zero<\/span>\ndf. fillna<\/span> (value= 0<\/span> ,inplace= True<\/span> )\n\n#view DataFrame\n<\/span>print<\/span> (df) \n\n  team points assists rebounds\n0 A 25.0 5.0 11\n1 0 0.0 7.0 8\n2 B 15.0 7.0 10\n3 B 0.0 9.0 6\n4 B 19.0 12.0 6\n5 C 23.0 9.0 5\n6 C 25.0 0.0 9\n7 C 29.0 4.0 12<\/strong><\/pre>\n
 \u1025\u1015\u1019\u102c 2- \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1021\u1010\u103d\u1000\u103a \u1015\u103b\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1014\u1031\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103c\u100a\u1037\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a DataFrame \u104f \u1021\u1019\u103e\u1010\u103a\u1014\u103e\u1004\u1037\u103a \u1021\u1011\u1031\u102c\u1000\u103a\u1021\u1000\u1030\u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1021\u1010\u103d\u1000\u103a \u101e\u102f\u100a\u1016\u103c\u1004\u1037\u103a \u1015\u103b\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1014\u1031\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1016\u103c\u100a\u1037\u103a\u1005\u103d\u1000\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span><\/p>\n
 #replace missing values in points and assists columns with zero<\/span>\ndf[['points', 'assists']] = df[['points', 'assists']]. fillna<\/span> (value= 0<\/span> )\n\n#view DataFrame\n<\/span>print<\/span> (df) \n\n  team points assists rebounds\n0 A 25.0 5.0 11\n1 NaN 0.0 7.0 8\n2 B 15.0 7.0 10\n3 B 0.0 9.0 6\n4 B 19.0 12.0 6\n5 C 23.0 9.0 5\n6 C 25.0 0.0 9\n7 C 29.0 4.0 12\n<\/strong><\/pre>\n
 \u1025\u1015\u1019\u102c 3- \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1016\u103c\u1004\u1037\u103a \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u1019\u103b\u102c\u1038\u1021\u1015\u103c\u102c\u1038\u1019\u103e \u1015\u103b\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1014\u1031\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103c\u100a\u1037\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u101e\u102f\u1036\u1038\u1001\u102f\u1016\u103c\u1004\u1037\u103a \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1000\u1031\u102c\u103a\u101c\u1036\u101e\u102f\u1036\u1038\u1001\u102f\u1010\u103d\u1004\u103a \u1015\u103b\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1014\u1031\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1016\u103c\u100a\u1037\u103a\u1005\u103d\u1000\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span><\/p>\n
 #replace missing values in three columns with three different values<\/span>\ndf. fillna<\/span> ({'team': ' Unknown<\/span> ', 'points': 0<\/span> , 'assists': ' zero<\/span> '}, inplace= True<\/span> )\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n      team points assists rebounds\n0 A 25.0 5 11\n1 Unknown 0.0 7 8\n2 B 15.0 7 10\n3 B 0.0 9 6\n4 B 19.0 12 6\n5 C 23.0 9 5\n6 C 25.0 zero 9\n7 C 29.0 4 12\n<\/strong><\/pre>\n
 \u1000\u1031\u102c\u103a\u101c\u1036\u101e\u102f\u1036\u1038\u1001\u102f\u101b\u103e\u102d \u1015\u103b\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1014\u1031\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f \u1011\u1030\u1038\u1001\u103c\u102c\u1038\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u1016\u103c\u1004\u1037\u103a \u1021\u1005\u102c\u1038\u1011\u102d\u102f\u1038\u1011\u102c\u1038\u101e\u100a\u103a\u1000\u102d\u102f \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
pandas fillna() function \u101e\u100a\u103a pandas DataFrame \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u1015\u103b\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1014\u1031\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f\u1016\u103c\u100a\u1037\u103a\u101b\u1014\u103a\u1021\u1010\u103d\u1000\u103a \u1021\u101e\u102f\u1036\u1038\u101d\u1004\u103a\u101e\u100a\u103a\u104b \u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1015\u1014\u103a\u1012\u102b DataFrame \u101b\u103e\u102d \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u1019\u103b\u102c\u1038\u1021\u1015\u103c\u102c\u1038\u1021\u1010\u103d\u1000\u103a \u1015\u103b\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1014\u1031\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f\u1016\u103c\u100a\u1037\u103a\u101b\u1014\u103a \u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038\u104f\u1014\u1019\u1030\u1014\u102c\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1000\u102d\u102f \u1015\u1031\u1038\u1006\u1031\u102c\u1004\u103a\u101e\u100a\u103a- import pandas as pd import numpy as np #createDataFrame df = pd. DataFrame ({‘team’: [‘A’, np. nan , ‘B’, ‘B’, ‘B’, ‘C’, ‘C’, ‘C’], ‘points’: [25, np. no , 15, np. no , 19, 23, 25, 29], ‘assists’: […]<\/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":[],"yoast_head":"\nPandas \u101b\u103e\u102d \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1021\u1010\u103d\u1000\u103a NA \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103c\u100a\u1037\u103a\u1014\u100a\u103a\u1038<\/title>\n<meta name=\"description\" content=\"\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a \u1014\u1019\u1030\u1014\u102c\u1019\u103b\u102c\u1038\u1021\u1015\u102b\u1021\u101d\u1004\u103a \u1015\u1014\u103a\u1012\u102b DataFrame \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u1019\u103b\u102c\u1038\u1021\u1015\u103c\u102c\u1038\u1021\u1010\u103d\u1000\u103a NA \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1016\u103c\u100a\u1037\u103a\u101e\u103d\u1004\u103a\u1038\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1011\u102c\u1038\u101e\u100a\u103a\u104b\" \/>\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\/my\/fillna-\u1015\u1014\u103a\u1012\u102b\u1019\u103b\u102d\u102f\u1038\u1005\u102f\u1036\u1000\u1031\u102c\u103a\u101c\u1036\/\" \/>\n<meta property=\"og:locale\" content=\"my_MM\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas \u101b\u103e\u102d \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1021\u1010\u103d\u1000\u103a NA \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103c\u100a\u1037\u103a\u1014\u100a\u103a\u1038\" \/>\n<meta property=\"og:description\" content=\"\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a \u1014\u1019\u1030\u1014\u102c\u1019\u103b\u102c\u1038\u1021\u1015\u102b\u1021\u101d\u1004\u103a \u1015\u1014\u103a\u1012\u102b DataFrame \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u1019\u103b\u102c\u1038\u1021\u1015\u103c\u102c\u1038\u1021\u1010\u103d\u1000\u103a NA \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1016\u103c\u100a\u1037\u103a\u101e\u103d\u1004\u103a\u1038\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1011\u102c\u1038\u101e\u100a\u103a\u104b\" \/>\n<meta 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