{"id":896,"date":"2023-07-28T09:32:44","date_gmt":"2023-07-28T09:32:44","guid":{"rendered":"https:\/\/statorials.org\/my\/%e1%80%80%e1%80%bc%e1%80%ad%e1%80%af%e1%80%b8%e1%80%90%e1%80%85%e1%80%ba%e1%80%81%e1%80%bb%e1%80%b1%e1%80%ac%e1%80%84%e1%80%ba%e1%80%b8%e1%80%80%e1%80%ad%e1%80%af-%e1%80%9b%e1%80%b1%e1%80%95%e1%80%b1\/"},"modified":"2023-07-28T09:32:44","modified_gmt":"2023-07-28T09:32:44","slug":"%e1%80%80%e1%80%bc%e1%80%ad%e1%80%af%e1%80%b8%e1%80%90%e1%80%85%e1%80%ba%e1%80%81%e1%80%bb%e1%80%b1%e1%80%ac%e1%80%84%e1%80%ba%e1%80%b8%e1%80%80%e1%80%ad%e1%80%af-%e1%80%9b%e1%80%b1%e1%80%95%e1%80%b1","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/%e1%80%80%e1%80%bc%e1%80%ad%e1%80%af%e1%80%b8%e1%80%90%e1%80%85%e1%80%ba%e1%80%81%e1%80%bb%e1%80%b1%e1%80%ac%e1%80%84%e1%80%ba%e1%80%b8%e1%80%80%e1%80%ad%e1%80%af-%e1%80%9b%e1%80%b1%e1%80%95%e1%80%b1\/","title":{"rendered":"Pandas \u1010\u103d\u1004\u103a \u1000\u103c\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u103b\u103e\u1031\u102c\u101b\u1014\u103a \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1019\u100a\u103a\u1014\u100a\u103a\u1038\u104b"},"content":{"rendered":"

<\/p>\n

\n

\u1015\u1014\u103a\u1012\u102b\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u1019\u103b\u103e\u1031\u102c\u1037\u1021\u1016\u103c\u1005\u103a\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101e\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038 1- Single Column \u1000\u102d\u102f Float \u1021\u1016\u103c\u1005\u103a\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1015\u102b\u104b<\/strong><\/span><\/p>\n

 #convert \"assists\" column from string to float<\/span>\ndf[' assists<\/span> '] = df[' assists<\/span> ']. astype<\/span> (float)\n<\/strong><\/pre>\n
 \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038 2- \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u1019\u103b\u102c\u1038\u1021\u1015\u103c\u102c\u1038\u1000\u102d\u102f Float \u1021\u1016\u103c\u1005\u103a\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1015\u102b\u104b<\/strong><\/span><\/p>\n
 #convert both \"assists\" and \"rebounds\" from strings to floats\n<\/span>df[[' assists<\/span> ', ' rebounds<\/span> ']] = df[[' assists<\/span> ', ' rebounds<\/span> ']]. astype<\/span> (float)<\/strong><\/pre>\n
 \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038 3- \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u102c\u1038\u101c\u102f\u1036\u1038\u1000\u102d\u102f Float \u1021\u1016\u103c\u1005\u103a\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1015\u102b\u104b<\/strong><\/span><\/p>\n
 #convert all columns to float\n<\/span>df = df. astype<\/span> (float)<\/strong><\/pre>\n
 \u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b\u1014\u1019\u1030\u1014\u102c\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b pandas DataFrame \u1016\u103c\u1004\u1037\u103a \u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1010\u103d\u1004\u103a \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' points<\/span> ': [np.nan, 12, 15, 14, 19],\n                   ' assists<\/span> ': ['5', np.nan, '7', '9', '12'],\n                   ' rebounds<\/span> ': ['11', '8', '10', '6', '6']})  \n\n#view DataFrame\n<\/span>df\n\n \tpoints assists rebounds\n0 NaN 5.0 11\n1 12.0 NaN 8\n2 15.0 7.0 10\n3 14.0 9.0 6\n4 19.0 12.0 6\n\n#view column data types\n<\/span>df. dtypes\n\nfloat64 points\nassists object\nrebound object\ndtype:object<\/span><\/span><\/strong><\/pre>\n
 \u1025\u1015\u1019\u102c 1- \u1000\u1031\u102c\u103a\u101c\u1036\u1010\u1005\u103a\u1001\u102f\u1010\u100a\u103a\u1038\u1000\u102d\u102f Float \u1021\u1016\u103c\u1005\u103a\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1015\u102b\u104b<\/strong><\/span><\/h2>\n
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u101e\u100a\u103a helper<\/strong> \u1000\u1031\u102c\u103a\u101c\u1036\u1000\u102d\u102f string \u1010\u1005\u103a\u1001\u102f\u1019\u103e float \u101e\u102d\u102f\u1037 \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span><\/p>\n
 #convert \"assists\" from string to float<\/span>\ndf[' assists<\/span> '] = df[' assists<\/span> ']. astype<\/span> (float)\n\n#view column data types<\/span>\ndf. dtypes<\/span>\n\nfloat64 points\nassist float64\nrebound object\ndtype:object\n<\/strong><\/pre>\n
 \u1025\u1015\u1019\u102c 2- \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u1019\u103b\u102c\u1038\u1021\u1015\u103c\u102c\u1038\u1000\u102d\u102f float \u1021\u1016\u103c\u1005\u103a\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1015\u102b\u104b<\/strong><\/span><\/h2>\n
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u101e\u100a\u103a helper<\/strong> \u1014\u103e\u1004\u1037\u103a \u1000\u1031\u102c\u103a\u101c\u1036<\/strong> \u1019\u103b\u102c\u1038\u1000\u102d\u102f strings \u1019\u103e float \u1021\u1016\u103c\u1005\u103a\u101e\u102d\u102f\u1037 \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u100a\u103a-<\/span><\/p>\n
 #convert both \"assists\" and \"rebounds\" from strings to floats\n<\/span>df[[' assists<\/span> ', ' rebounds<\/span> ']] = df[[' assists<\/span> ', ' rebounds<\/span> ']]. astype<\/span> (float)\n\n#view column data types\n<\/span>df. dtypes\n\n<\/span>float64 points\nassist float64\nrebounds float64\ndtype:object\n<\/strong><\/pre>\n
 \u1025\u1015\u1019\u102c 3- \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u102c\u1038\u101c\u102f\u1036\u1038\u1000\u102d\u102f float \u1021\u1016\u103c\u1005\u103a\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1015\u102b\u104b<\/strong><\/span><\/h2>\n
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u101e\u100a\u103a DataFrame \u101b\u103e\u102d \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u102c\u1038\u101c\u102f\u1036\u1038\u1000\u102d\u102f float \u1021\u1016\u103c\u1005\u103a\u101e\u102d\u102f\u1037 \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u100a\u103a-<\/span><\/p>\n
 #convert all columns to float\n<\/span>df = df. astype<\/span> (float)\n\n#view column data types\n<\/span>df. dtypes\n\n<\/span>float64 points\nassist float64\nrebounds float64\ndtype:object<\/strong><\/pre>\n
 \u1021\u1015\u102d\u102f\u1006\u102f- string \u1000\u102d\u102f float \u1014\u103e\u1004\u1037\u103a pad NaN \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u101e\u102d\u102f\u1037 \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1015\u102b\u104b<\/strong><\/span><\/h2>\n
 \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u101e\u100a\u103a helper<\/strong> \u1000\u1031\u102c\u103a\u101c\u1036\u1000\u102d\u102f string \u1019\u103e float \u101e\u102d\u102f\u1037 \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1014\u100a\u103a\u1038\u1014\u103e\u1004\u1037\u103a NaN \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101e\u102f\u100a\u1016\u103c\u1004\u1037\u103a \u1010\u1015\u103c\u102d\u102f\u1004\u103a\u1014\u1000\u103a pad \u101c\u102f\u1015\u103a\u1015\u102f\u1036\u1000\u102d\u102f \u1015\u103c\u101e\u100a\u103a-<\/span><\/p>\n
 #convert \"assists\" from string to float and fill in NaN values with zeros\n<\/span>df[' assists<\/span> '] = df[' assists<\/span> ']. astype<\/span> (float). fillna<\/span> (0)\n\n#view DataFrame\n<\/span>df\n<\/span>\n        points assists rebounds\n0 NaN 5.0 11\n1 12.0 0.0 8\n2 15.0 7.0 10\n3 14.0 9.0 6\n4 19.0 12.0 6<\/strong><\/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><\/h2>\n
 \u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1015\u1014\u103a\u1012\u102b\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u1021\u1001\u103c\u102c\u1038\u1018\u102f\u1036\u1021\u101c\u102f\u1015\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u101e\u100a\u103a-<\/p>\n
 Pandas- \u1021\u101b\u102c\u101d\u1010\u1039\u1011\u102f\u1010\u1005\u103a\u1001\u102f\u1021\u102c\u1038 \u1000\u102d\u1014\u103a\u1038\u1015\u103c\u100a\u1037\u103a\u1021\u1016\u103c\u1005\u103a\u101e\u102d\u102f\u1037 \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1019\u100a\u103a\u1014\u100a\u103a\u1038\u104b<\/a>
 \u1015\u1014\u103a\u1012\u102b\u1019\u103b\u102c\u1038- float \u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1000\u102d\u1014\u103a\u1038\u1015\u103c\u100a\u1037\u103a\u1021\u1016\u103c\u1005\u103a\u101e\u102d\u102f\u1037 \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1014\u100a\u103a\u1038<\/a>
 Pandas- \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c\u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1000\u102d\u102f NumPy \u1021\u1001\u1004\u103a\u1038\u1021\u1000\u103b\u1004\u103a\u1038\u101e\u102d\u102f\u1037 \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101b\u1019\u100a\u103a\u1014\u100a\u103a\u1038\u104b<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u1015\u1014\u103a\u1012\u102b\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u1019\u103b\u103e\u1031\u102c\u1037\u1021\u1016\u103c\u1005\u103a\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101e\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038 1- Single Column \u1000\u102d\u102f Float \u1021\u1016\u103c\u1005\u103a\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1015\u102b\u104b #convert “ assists”  column from string to float df[‘ assists ‘] = df[‘ assists ‘]. astype (float) \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038 2- \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u1019\u103b\u102c\u1038\u1021\u1015\u103c\u102c\u1038\u1000\u102d\u102f Float \u1021\u1016\u103c\u1005\u103a\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1015\u102b\u104b #convert both “ assists”  and “ rebounds”  from strings to floats df[[‘ assists ‘, ‘ rebounds ‘]] = df[[‘ assists ‘, ‘ rebounds ‘]]. astype […]<\/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 \u1010\u103d\u1004\u103a strings \u1019\u103b\u102c\u1038\u1000\u102d\u102f float \u1021\u1016\u103c\u1005\u103a\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1014\u100a\u103a\u1038 - Statology<\/title>\n<meta name=\"description\" content=\"pandas DataFrame \u1010\u103d\u1004\u103a string \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038 \u1000\u102d\u102f float \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1021\u1016\u103c\u1005\u103a\u101e\u102d\u102f\u1037 \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1014\u100a\u103a\u1038 \u101b\u102d\u102f\u1038\u101b\u103e\u1004\u103a\u1038\u101e\u1031\u102c \u101b\u103e\u1004\u103a\u1038\u101c\u1004\u103a\u1038\u1001\u103b\u1000\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\/\u1000\u103c\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u103b\u1031\u102c\u1004\u103a\u1038\u1000\u102d\u102f-\u101b\u1031\u1015\u1031\/\" \/>\n<meta property=\"og:locale\" content=\"my_MM\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas \u1010\u103d\u1004\u103a strings \u1019\u103b\u102c\u1038\u1000\u102d\u102f float \u1021\u1016\u103c\u1005\u103a\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1014\u100a\u103a\u1038 - Statology\" \/>\n<meta property=\"og:description\" content=\"pandas DataFrame \u1010\u103d\u1004\u103a 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