{"id":1734,"date":"2023-07-25T05:02:59","date_gmt":"2023-07-25T05:02:59","guid":{"rendered":"https:\/\/statorials.org\/my\/%e1%80%95%e1%80%94%e1%80%ba%e1%80%92%e1%80%ab%e1%80%99%e1%80%bb%e1%80%ac%e1%80%b8%e1%80%9e%e1%80%8a%e1%80%ba-%e1%80%90%e1%80%94%e1%80%ba%e1%80%96%e1%80%ad%e1%80%af%e1%80%b8%e1%80%96%e1%80%bc%e1%80%84\/"},"modified":"2023-07-25T05:02:59","modified_gmt":"2023-07-25T05:02:59","slug":"%e1%80%95%e1%80%94%e1%80%ba%e1%80%92%e1%80%ab%e1%80%99%e1%80%bb%e1%80%ac%e1%80%b8%e1%80%9e%e1%80%8a%e1%80%ba-%e1%80%90%e1%80%94%e1%80%ba%e1%80%96%e1%80%ad%e1%80%af%e1%80%b8%e1%80%96%e1%80%bc%e1%80%84","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/%e1%80%95%e1%80%94%e1%80%ba%e1%80%92%e1%80%ab%e1%80%99%e1%80%bb%e1%80%ac%e1%80%b8%e1%80%9e%e1%80%8a%e1%80%ba-%e1%80%90%e1%80%94%e1%80%ba%e1%80%96%e1%80%ad%e1%80%af%e1%80%b8%e1%80%96%e1%80%bc%e1%80%84\/","title":{"rendered":"Pandas- \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c\u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103b\u1000\u103a\u1014\u100a\u103a\u1038"},"content":{"rendered":"

<\/p>\n

\n

\u1000\u1031\u102c\u103a\u101c\u1036\u1010\u1005\u103a\u1001\u102f\u101b\u103e\u102d \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c pandas DataFrame \u101b\u103e\u102d \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103b\u1000\u103a\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f \u101e\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/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
 \u1021\u1001\u103b\u102d\u102f\u1037\u101e\u1031\u102c\u1005\u102c\u101b\u1004\u103a\u1038\u1010\u103d\u1004\u103a \u1019\u100a\u103a\u101e\u100a\u1037\u103a\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c pandas DataFrame \u101b\u103e\u102d \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103b\u1000\u103a\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f \u101e\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/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
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1025\u1015\u1019\u102c\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1024 syntax \u1000\u102d\u102f\u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1010\u103d\u1004\u103a\u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1019\u100a\u103a\u1000\u102d\u102f\u1015\u103c\u101e\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1025\u1015\u1019\u102c 1- \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103b\u1000\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a \u1000\u1031\u102c\u103a\u101c\u1036\u1010\u1005\u103a\u1001\u102f\u101b\u103e\u102d \u101e\u102e\u1038\u1001\u103c\u102c\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1010\u1014\u103a\u1038\u1021\u102c\u1038\u101c\u102f\u1036\u1038\u1000\u102d\u102f \u1016\u103b\u1000\u103a\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/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
 \u1025\u1015\u1019\u102c 2- \u1005\u102c\u101b\u1004\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1010\u103d\u1004\u103a \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u101a\u103a\u101b\u103e\u102c\u1038\u1015\u102b\u104b<\/strong><\/span><\/h3>\n
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a \u1005\u102c\u101b\u1004\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u101b\u103e\u102d \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c DataFrame \u1019\u103e\u1021\u1010\u1014\u103a\u1038\u1021\u102c\u1038\u101c\u102f\u1036\u1038\u1000\u102d\u102f \u1016\u101a\u103a\u101b\u103e\u102c\u1038\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/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
 \u1025\u1015\u1019\u102c 3- \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1010\u103d\u1004\u103a \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u101a\u103a\u101b\u103e\u102c\u1038\u1015\u102b\u104b<\/strong><\/span><\/h3>\n
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1011\u1032\u1019\u103e \u1010\u1005\u103a\u1001\u102f\u1010\u103d\u1004\u103a \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c DataFrame \u1019\u103e\u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u101a\u103a\u101b\u103e\u102c\u1038\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/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
 \u1011\u1015\u103a\u101c\u1031\u102c\u1004\u103a\u1038\u1021\u101b\u1004\u103a\u1038\u1021\u1019\u103c\u1005\u103a\u1019\u103b\u102c\u1038<\/strong><\/span><\/h3>\n
 Pandas \u101b\u103e\u102d \u1021\u100a\u103d\u103e\u1014\u103a\u1038\u1021\u101c\u102d\u102f\u1000\u103a \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103b\u1000\u103a\u1014\u100a\u103a\u1038<\/a>
 Pandas \u101b\u103e\u102d \u1021\u100a\u103d\u103e\u1014\u103a\u1038\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u1000\u1032\u1037\u101e\u102d\u102f\u1037 \u1016\u103b\u1000\u103a\u1019\u100a\u103a\u1014\u100a\u103a\u1038\u104b<\/a>
 Pandas \u1010\u103d\u1004\u103a \u101e\u102e\u1038\u1001\u103c\u102c\u1038\u1005\u102c\u1000\u103c\u1031\u102c\u1004\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103b\u1000\u103a\u1014\u100a\u103a\u1038<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u1000\u1031\u102c\u103a\u101c\u1036\u1010\u1005\u103a\u1001\u102f\u101b\u103e\u102d \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c pandas DataFrame \u101b\u103e\u102d \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103b\u1000\u103a\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f \u101e\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a- #drop rows that contain specific ‘value’ in ‘column_name’ df = df[df. column_name != value ] \u1021\u1001\u103b\u102d\u102f\u1037\u101e\u1031\u102c\u1005\u102c\u101b\u1004\u103a\u1038\u1010\u103d\u1004\u103a \u1019\u100a\u103a\u101e\u100a\u1037\u103a\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c pandas DataFrame \u101b\u103e\u102d \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103b\u1000\u103a\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f \u101e\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a- #define values values = [value1, value2, value3, …] #drop rows that contain any value in the list […]<\/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- \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103b\u1000\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\u1005\u103d\u102c\u1021\u1015\u102b\u1021\u101d\u1004\u103a \u101e\u102e\u1038\u1001\u103c\u102c\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1015\u102b\u101d\u1004\u103a\u101e\u1031\u102c pandas DataFrame \u1019\u103e\u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103b\u1000\u103a\u1014\u100a\u103a\u1038\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\/\u1015\u1014\u103a\u1012\u102b\u1019\u103b\u102c\u1038\u101e\u100a\u103a-\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1016\u103c\u1004\/\" \/>\n<meta property=\"og:locale\" content=\"my_MM\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas- \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103b\u1000\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\u1005\u103d\u102c\u1021\u1015\u102b\u1021\u101d\u1004\u103a \u101e\u102e\u1038\u1001\u103c\u102c\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1015\u102b\u101d\u1004\u103a\u101e\u1031\u102c pandas DataFrame \u1019\u103e\u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103b\u1000\u103a\u1014\u100a\u103a\u1038\u1000\u102d\u102f 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