{"id":2236,"date":"2023-07-23T03:24:57","date_gmt":"2023-07-23T03:24:57","guid":{"rendered":"https:\/\/statorials.org\/my\/python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-hot-encoding\/"},"modified":"2023-07-23T03:24:57","modified_gmt":"2023-07-23T03:24:57","slug":"python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-hot-encoding","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-hot-encoding\/","title":{"rendered":"Python \u1010\u103d\u1004\u103a one-hot encoding \u1015\u103c\u102f\u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038"},"content":{"rendered":"

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One-hot encoding<\/strong> \u1000\u102d\u102f machine learning algorithms<\/a> \u1019\u103e \u1021\u101c\u103d\u101a\u103a\u1010\u1000\u1030 \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c \u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1021\u1016\u103c\u1005\u103a \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c \u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1021\u1016\u103c\u1005\u103a \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n

one-hot coding \u104f \u1021\u1001\u103c\u1031\u1001\u1036\u1021\u101a\u1030\u1021\u1006\u1019\u103e\u102c 0 \u1014\u103e\u1004\u1037\u103a 1 \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u1030\u101b\u1004\u103a\u1038\u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f\u1000\u102d\u102f\u101a\u103a\u1005\u102c\u1038\u1015\u103c\u102f\u101b\u1014\u103a\u1021\u1010\u103d\u1000\u103a variable \u1021\u101e\u1005\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u101b\u1014\u103a\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1025\u1015\u1019\u102c\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a\u104a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1015\u102f\u1036\u101e\u100a\u103a 0 \u1014\u103e\u1004\u1037\u103a 1 \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u101e\u102c\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1021\u101e\u1005\u103a\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1016\u103d\u1032\u1037\u1021\u1019\u100a\u103a\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1000\u103d\u1032\u1015\u103c\u102c\u1038\u101e\u1031\u102c variable \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u101b\u1014\u103a\u1021\u1010\u103d\u1000\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a one-hot \u1000\u102f\u1012\u103a\u1014\u1036\u1015\u102b\u1010\u103a\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1015\u103c\u101e\u1011\u102c\u1038\u101e\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span> <\/p>\n

\"\"<\/p>\n

\u1021\u1031\u102c\u1000\u103a\u1015\u102b \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a \u1025\u1015\u1019\u102c\u101e\u100a\u103a Python \u101b\u103e\u102d \u1024\u1021\u1010\u102d\u1021\u1000\u103b\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1021\u1010\u103d\u1000\u103a one-hot encoding \u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1021\u1006\u1004\u1037\u103a 1: \u1012\u1031\u1010\u102c\u1000\u102d\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u104b<\/strong><\/span><\/h3>\n

\u1015\u1011\u1019\u1026\u1038\u1005\u103d\u102c\u104a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1015\u1014\u103a\u1012\u102b DataFrame \u1000\u102d\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u1000\u103c\u1015\u102b\u1005\u102d\u102f\u1037\u104b<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'B', 'B', 'B', 'B', 'C', 'C'],\n                   ' points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points\n0 to 25\n1 to 12\n2 B 15\n3 B 14\n4 B 19\n5 B 23\n6 C 25\n7 C 29<\/strong><\/pre>\n
 \u1021\u1006\u1004\u1037\u103a 2- \u1015\u1030\u1015\u103c\u1004\u103a\u1038\u101e\u1031\u102c \u1000\u102f\u1012\u103a\u1014\u1036\u1015\u102b\u1010\u103a\u1000\u102d\u102f \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n
 \u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a sklearn<\/strong> \u1005\u102c\u1000\u103c\u100a\u1037\u103a\u1010\u102d\u102f\u1000\u103a\u1019\u103e OneHotEncoder()<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1010\u1004\u103a\u101e\u103d\u1004\u103a\u1038\u1015\u103c\u102e\u1038 pandas DataFrame \u101b\u103e\u102d ‘team’ variable \u1010\u103d\u1004\u103a hot encoding \u1000\u102d\u102f\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1014\u103a \u104e\u1004\u103a\u1038\u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1000\u103c\u1015\u102b\u1005\u102d\u102f\u1037\u104b<\/span><\/p>\n
 from<\/span> sklearn. preprocessing<\/span> import<\/span> OneHotEncoder\n\n#creating instance of one-hot-encoder\n<\/span>encoder = OneHotEncoder(handle_unknown=' ignore<\/span> ')\n\n#perform one-hot encoding on 'team' column \n<\/span>encoder_df = pd. DataFrame<\/span> ( encoder.fit_transform<\/span> (df[[' team<\/span> ']]). toarray<\/span> ())\n\n#merge one-hot encoded columns back with original DataFrame\n<\/span>final_df = df. join<\/span> (encoder_df)\n\n#view final df\n<\/span>print<\/span> (final_df)\n\n  team points 0 1 2\n0 to 25 1.0 0.0 0.0\n1 to 12 1.0 0.0 0.0\n2 B 15 0.0 1.0 0.0\n3 B 14 0.0 1.0 0.0\n4 B 19 0.0 1.0 0.0\n5 B 23 0.0 1.0 0.0\n6 C 25 0.0 0.0 1.0\n7 C 29 0.0 0.0 1.0\n<\/strong><\/pre>\n
 \u1019\u1030\u101b\u1004\u103a\u1038 “ \u1021\u1016\u103d\u1032\u1037”  \u1000\u1031\u102c\u103a\u101c\u1036\u1010\u103d\u1004\u103a \u1011\u1030\u1038\u1001\u103c\u102c\u1038\u101e\u1031\u102c\u1010\u1014\u103a\u1016\u102d\u102f\u1038 \u101e\u102f\u1036\u1038\u1001\u102f\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c\u1000\u103c\u1031\u102c\u1004\u1037\u103a \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u101e\u1005\u103a\u101e\u102f\u1036\u1038\u1001\u102f\u1000\u102d\u102f DataFrame \u101e\u102d\u102f\u1037 \u1015\u1031\u102b\u1004\u103a\u1038\u1011\u100a\u1037\u103a\u1001\u1032\u1037\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b<\/span><\/p>\n
 \u1021\u1006\u1004\u1037\u103a 3- \u1019\u1030\u101b\u1004\u103a\u1038 categorical variable \u1000\u102d\u102f \u1016\u101a\u103a\u101b\u103e\u102c\u1038\u1015\u102b\u104b<\/strong><\/span><\/h3>\n
 \u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038\u1010\u103d\u1004\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u104e\u1004\u103a\u1038\u1000\u102d\u102f \u1019\u101c\u102d\u102f\u1021\u1015\u103a\u1010\u1031\u102c\u1037\u101e\u1031\u102c\u1000\u103c\u1031\u102c\u1004\u1037\u103a \u1019\u1030\u101b\u1004\u103a\u1038 ‘\u1021\u1016\u103d\u1032\u1037’ variable \u1000\u102d\u102f DataFrame \u1019\u103e \u1016\u101a\u103a\u101b\u103e\u102c\u1038\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\n
 #drop 'team' column\n<\/span>final_df. drop<\/span> (' team<\/span> ', axis= 1<\/span> , inplace= True<\/span> )\n\n#view final df\n<\/span>print<\/span> (final_df)\n\n   points 0 1 2\n0 25 1.0 0.0 0.0\n1 12 1.0 0.0 0.0\n2 15 0.0 1.0 0.0\n3 14 0.0 1.0 0.0\n4 19 0.0 1.0 0.0\n5 23 0.0 1.0 0.0\n6 25 0.0 0.0 1.0\n7 29 0.0 0.0 1.0\n<\/strong><\/pre>\n
 \u104e\u1004\u103a\u1038\u1010\u102d\u102f\u1037\u1000\u102d\u102f\u1016\u1010\u103a\u101b\u101c\u103d\u101a\u103a\u1000\u1030\u1005\u1031\u101b\u1014\u103a \u1014\u1031\u102c\u1000\u103a\u1006\u102f\u1036\u1038 DataFrame \u104f\u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1000\u102d\u102f\u101c\u100a\u103a\u1038 \u1021\u1019\u100a\u103a\u1015\u103c\u1031\u102c\u1004\u103a\u1038\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\n
 #rename columns\n<\/span>final_df. columns<\/span> = ['points', 'teamA', 'teamB', 'teamC']\n\n#view final df<\/span>\nprint<\/span> (final_df)\n\n   points teamA teamB teamC\n0 25 1.0 0.0 0.0\n1 12 1.0 0.0 0.0\n2 15 0.0 1.0 0.0\n3 14 0.0 1.0 0.0\n4 19 0.0 1.0 0.0\n5 23 0.0 1.0 0.0\n6 25 0.0 0.0 1.0\n7 29 0.0 0.0 1.0\n<\/strong><\/pre>\n
 One-hot encoding \u1015\u103c\u102e\u1038\u1015\u102b\u1015\u103c\u102e\u104a \u101a\u1001\u102f\u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101c\u102d\u102f\u1001\u103b\u1004\u103a\u101e\u1031\u102c \u1019\u100a\u103a\u101e\u100a\u1037\u103a machine learning algorithm \u1010\u103d\u1004\u103a \u1024\u1015\u1014\u103a\u1012\u102b DataFrame \u1000\u102d\u102f \u1011\u100a\u1037\u103a\u101e\u103d\u1004\u103a\u1038\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u1015\u103c\u102e\u104b<\/span><\/p>\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
 Python \u1010\u103d\u1004\u103a \u1016\u103c\u1010\u103a\u1010\u1031\u102c\u1000\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1006\u102d\u102f\u101c\u102d\u102f\u101b\u1004\u103a\u1038\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1014\u100a\u103a\u1038<\/a>
 Python \u1010\u103d\u1004\u103a linear regression \u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038<\/a>
 Python \u1010\u103d\u1004\u103a Logistic Regression \u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1019\u100a\u103a\u1014\u100a\u103a\u1038\u104b<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
One-hot encoding \u1000\u102d\u102f machine learning algorithms \u1019\u103e \u1021\u101c\u103d\u101a\u103a\u1010\u1000\u1030 \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c \u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1021\u1016\u103c\u1005\u103a \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c \u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1021\u1016\u103c\u1005\u103a \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u101e\u100a\u103a\u104b one-hot coding \u104f \u1021\u1001\u103c\u1031\u1001\u1036\u1021\u101a\u1030\u1021\u1006\u1019\u103e\u102c 0 \u1014\u103e\u1004\u1037\u103a 1 \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u1030\u101b\u1004\u103a\u1038\u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f\u1000\u102d\u102f\u101a\u103a\u1005\u102c\u1038\u1015\u103c\u102f\u101b\u1014\u103a\u1021\u1010\u103d\u1000\u103a variable \u1021\u101e\u1005\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u101b\u1014\u103a\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u1025\u1015\u1019\u102c\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a\u104a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1015\u102f\u1036\u101e\u100a\u103a 0 \u1014\u103e\u1004\u1037\u103a 1 \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u101e\u102c\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1021\u101e\u1005\u103a\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1016\u103d\u1032\u1037\u1021\u1019\u100a\u103a\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1021\u1019\u103b\u102d\u102f\u1038\u1021\u1005\u102c\u1038\u1000\u103d\u1032\u1015\u103c\u102c\u1038\u101e\u1031\u102c variable \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u101b\u1014\u103a\u1021\u1010\u103d\u1000\u103a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a one-hot \u1000\u102f\u1012\u103a\u1014\u1036\u1015\u102b\u1010\u103a\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1015\u103c\u101e\u1011\u102c\u1038\u101e\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a- \u1021\u1031\u102c\u1000\u103a\u1015\u102b \u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a \u1025\u1015\u1019\u102c\u101e\u100a\u103a Python \u101b\u103e\u102d \u1024\u1021\u1010\u102d\u1021\u1000\u103b\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1021\u1010\u103d\u1000\u103a one-hot encoding \u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u1011\u102c\u1038\u101e\u100a\u103a\u104b \u1021\u1006\u1004\u1037\u103a 1: \u1012\u1031\u1010\u102c\u1000\u102d\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u104b […]<\/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":"\nPython - Statology \u1010\u103d\u1004\u103a One-Hot Encoding \u101c\u102f\u1015\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 Python \u1010\u103d\u1004\u103a one-hot encoding \u101c\u102f\u1015\u103a\u1014\u100a\u103a\u1038\u1000\u102d\u102f 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 \u1006\u1000\u103a\u1005\u1015\u103a-<\/strong><\/span> Pandas \u101b\u103e\u102d \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1016\u103b\u1000\u103a\u1014\u100a\u103a\u1038 (\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038 4 \u1001\u102f)<\/a><\/p>\n
 \u1019\u103e\u1010\u103a\u1001\u103b\u1000\u103a<\/strong> – OneHotEncoder()<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1021\u1010\u103d\u1000\u103a \u1005\u102c\u101b\u103d\u1000\u103a\u1005\u102c\u1010\u1019\u103a\u1038\u1021\u1015\u103c\u100a\u1037\u103a\u1021\u1005\u102f\u1036\u1000\u102d\u102f \u1024\u1014\u1031\u101b\u102c\u1010\u103d\u1004\u103a<\/a> \u101b\u103e\u102c\u1016\u103d\u1031\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n