{"id":3121,"date":"2023-07-19T03:04:16","date_gmt":"2023-07-19T03:04:16","guid":{"rendered":"https:\/\/statorials.org\/my\/%e1%80%95%e1%80%94%e1%80%ba%e1%80%92%e1%80%ab%e1%80%9b%e1%80%91%e1%80%ac%e1%80%b8%e1%80%85%e1%80%99%e1%80%ba%e1%80%b8%e1%80%9e%e1%80%95%e1%80%ba%e1%80%99%e1%80%be%e1%80%af\/"},"modified":"2023-07-19T03:04:16","modified_gmt":"2023-07-19T03:04:16","slug":"%e1%80%95%e1%80%94%e1%80%ba%e1%80%92%e1%80%ab%e1%80%9b%e1%80%91%e1%80%ac%e1%80%b8%e1%80%85%e1%80%99%e1%80%ba%e1%80%b8%e1%80%9e%e1%80%95%e1%80%ba%e1%80%99%e1%80%be%e1%80%af","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%9b%e1%80%91%e1%80%ac%e1%80%b8%e1%80%85%e1%80%99%e1%80%ba%e1%80%b8%e1%80%9e%e1%80%95%e1%80%ba%e1%80%99%e1%80%be%e1%80%af\/","title":{"rendered":"Pandas dataframe \u1019\u103e \u101b\u1011\u102c\u1038\u1010\u1005\u103a\u1001\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1015\u103c\u102e\u1038 \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1014\u100a\u103a\u1038"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\" target=\"_blank\" rel=\"noopener\">\u1005\u1000\u103a\u101e\u1004\u103a\u101a\u1030\u1019\u103e\u102f\u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f<\/a> \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1016\u103c\u1005\u103a\u1005\u1031\u101e\u1031\u102c\u1021\u1001\u102b\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1014\u103e\u1005\u103a\u1005\u102f\u1036\u1021\u1016\u103c\u1005\u103a \u1001\u103d\u1032\u101c\u1031\u1037\u101b\u103e\u102d\u101e\u100a\u103a-<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. Training set-<\/strong> \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a (\u1019\u1030\u101b\u1004\u103a\u1038\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u104f 70-80%)<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1036-<\/strong> \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1005\u103d\u1019\u103a\u1038\u1006\u1031\u102c\u1004\u103a\u101b\u100a\u103a\u104f \u1018\u1000\u103a\u1019\u101c\u102d\u102f\u1000\u103a\u101e\u1031\u102c \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a (\u1019\u1030\u101b\u1004\u103a\u1038\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u104f 20-30%) \u101b\u101b\u103e\u102d\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">Python \u1010\u103d\u1004\u103a\u104a \u1015\u1014\u103a\u1012\u102b DataFrame \u1000\u102d\u102f \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1021\u1005\u102f\u1036\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1021\u1016\u103c\u1005\u103a \u1001\u103d\u1032\u101b\u1014\u103a \u1018\u102f\u1036\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1014\u103e\u1005\u103a\u1001\u102f\u101b\u103e\u102d\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038 1- sklearn \u104f train_test_split() \u1000\u102d\u102f\u101e\u102f\u1036\u1038\u1015\u102b<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n\ntrain, test = train_test_split(df, test_size= <span style=\"color: #008000;\">0.2<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038 2- \u1015\u1014\u103a\u1012\u102b\u1019\u103b\u102c\u1038\u1019\u103e sample() \u1000\u102d\u102f\u101e\u102f\u1036\u1038\u1015\u102b\u104b<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>train = df. <span style=\"color: #3366ff;\">sample<\/span> (frac= <span style=\"color: #008000;\">0.8<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\ntest = df. <span style=\"color: #3366ff;\">drop<\/span> ( <span style=\"color: #3366ff;\">train.index<\/span> )<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\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 \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<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#create DataFrame with 1,000 rows and 3 columns\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> <span style=\"color: #3366ff;\">(<\/span> {' <span style=\"color: #ff0000;\">x1<\/span> ': <span style=\"color: #3366ff;\">np.random.randint<\/span> (30,size=1000),\n                   ' <span style=\"color: #ff0000;\">x2<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (12, size=1000),\n                   ' <span style=\"color: #ff0000;\">y<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (2, size=1000)})\n\n<span style=\"color: #008080;\">#view first few rows of DataFrame<\/span>\ndf. <span style=\"color: #3366ff;\">head<\/span> ()\n\n        x1 x2 y\n0 5 1 1\n1 11 8 0\n2 12 4 1\n3 8 7 0\n4 9 0 0\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u1025\u1015\u1019\u102c 1- sklearn \u1019\u103e train_test_split() \u1000\u102d\u102f\u101e\u102f\u1036\u1038\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a pandas DataFrame \u1000\u102d\u102f \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1036\u1021\u1016\u103c\u1005\u103a \u1001\u103d\u1032\u101b\u1014\u103a <strong>sklearn<\/strong> &#8216;s <strong>train_test_split()<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102f\u1036\u1000\u102d\u102f \u1015\u103c\u101e\u101e\u100a\u103a-<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n\n<span style=\"color: #008080;\">#split original DataFrame into training and testing sets\n<\/span>train, test = train_test_split(df, test_size= <span style=\"color: #008000;\">0.2<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\n\n<span style=\"color: #008080;\">#view first few rows of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">train.head<\/span> ())\n\n     x1 x2 y\n687 16 2 0\n500 18 2 1\n332 4 10 1\n979 2 8 1\n817 11 1 0\n\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">test.head<\/span> ())\n\n     x1 x2 y\n993 22 1 1\n859 27 6 0\n298 27 8 1\n553 20 6 0\n672 9 2 1\n\n<span style=\"color: #008080;\">#print size of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> (train. <span style=\"color: #3366ff;\">shape<\/span> , test. <span style=\"color: #3366ff;\">shape<\/span> )\n\n(800, 3) (200, 3)\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u101b\u101c\u1012\u103a\u1019\u103e \u1042 \u1005\u102f\u1036\u1016\u1014\u103a\u1010\u102e\u1038\u1011\u102c\u1038\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u101e\u1004\u103a\u1010\u1014\u103a\u1038\u1021\u1005\u102f\u1036- \u1021\u1010\u1014\u103a\u1038 800 \u1014\u103e\u1004\u1037\u103a \u1000\u1031\u102c\u103a\u101c\u1036 3 \u1001\u102f<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1036- \u1021\u1010\u1014\u103a\u1038 200 \u1014\u103e\u1004\u1037\u103a \u1000\u1031\u102c\u103a\u101c\u1036 3 \u1001\u102f<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>test_size<\/strong> \u101e\u100a\u103a test set \u1014\u103e\u1004\u1037\u103a \u101e\u1000\u103a\u1006\u102d\u102f\u1004\u103a\u1019\u100a\u1037\u103a \u1019\u1030\u101b\u1004\u103a\u1038 DataFrame \u1019\u103e \u1005\u1030\u1038\u1005\u1019\u103a\u1038\u101c\u1031\u1037\u101c\u102c\u1019\u103e\u102f\u1019\u103b\u102c\u1038\u104f \u101b\u102c\u1001\u102d\u102f\u1004\u103a\u1014\u103e\u102f\u1014\u103a\u1038\u1000\u102d\u102f \u1011\u102d\u1014\u103a\u1038\u1001\u103b\u102f\u1015\u103a\u1015\u103c\u102e\u1038 <strong>random_state<\/strong> \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u101e\u100a\u103a \u1001\u103d\u1032\u1001\u103c\u1019\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u1014\u103a\u1011\u102f\u1010\u103a\u1014\u102d\u102f\u1004\u103a\u1005\u1031\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u1025\u1015\u1019\u102c 2- \u1015\u1014\u103a\u1012\u102b\u1019\u103b\u102c\u1038\u1019\u103e sample() \u1000\u102d\u102f\u101e\u102f\u1036\u1038\u1015\u102b\u104b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u1015\u1014\u103a\u1012\u102b DataFrame \u1000\u102d\u102f \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038 \u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f \u1021\u1005\u102f\u1036\u1021\u1016\u103c\u1005\u103a \u1001\u103d\u1032\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b \u1000\u102f\u1012\u103a\u101e\u100a\u103a <b>pandas<\/b> <strong>\u1014\u1019\u1030\u1014\u102c()<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\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<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#split original DataFrame into training and testing sets\n<\/span>train = df. <span style=\"color: #3366ff;\">sample<\/span> (frac= <span style=\"color: #008000;\">0.8<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\ntest = df. <span style=\"color: #3366ff;\">drop<\/span> ( <span style=\"color: #3366ff;\">train.index<\/span> )\n\n<span style=\"color: #008080;\">#view first few rows of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">train.head<\/span> ())\n\n     x1 x2 y\n993 22 1 1\n859 27 6 0\n298 27 8 1\n553 20 6 0\n672 9 2 1\n\n<span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">test.head<\/span> ())\n\n    x1 x2 y\n9 16 5 0\n11 12 10 0\n19 5 9 0\n23 28 1 1\n28 18 0 1\n\n<span style=\"color: #008080;\">#print size of each set<\/span>\n<span style=\"color: #008000;\">print<\/span> (train. <span style=\"color: #3366ff;\">shape<\/span> , test. <span style=\"color: #3366ff;\">shape<\/span> )\n\n(800, 3) (200, 3)\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u101b\u101c\u1012\u103a\u1019\u103e \u1042 \u1005\u102f\u1036\u1016\u1014\u103a\u1010\u102e\u1038\u1011\u102c\u1038\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u101e\u1004\u103a\u1010\u1014\u103a\u1038\u1021\u1005\u102f\u1036- \u1021\u1010\u1014\u103a\u1038 800 \u1014\u103e\u1004\u1037\u103a \u1000\u1031\u102c\u103a\u101c\u1036 3 \u1001\u102f<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1036- \u1021\u1010\u1014\u103a\u1038 200 \u1014\u103e\u1004\u1037\u103a \u1000\u1031\u102c\u103a\u101c\u1036 3 \u1001\u102f<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><b>frac \u101e\u100a\u103a<\/b> \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1014\u103e\u1004\u1037\u103a \u101e\u1000\u103a\u1006\u102d\u102f\u1004\u103a\u101e\u100a\u1037\u103a \u1019\u1030\u101b\u1004\u103a\u1038 DataFrame \u1019\u103e \u1000\u103c\u100a\u1037\u103a\u101b\u103e\u102f\u1019\u103e\u102f\u101b\u102c\u1001\u102d\u102f\u1004\u103a\u1014\u103e\u102f\u1014\u103a\u1038\u1000\u102d\u102f \u1011\u102d\u1014\u103a\u1038\u1001\u103b\u102f\u1015\u103a\u1015\u103c\u102e\u1038 <strong>random_state<\/strong> \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u101e\u100a\u103a \u1001\u103d\u1032\u1001\u103c\u1019\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u1014\u103a\u101c\u100a\u103a\u1011\u102f\u1010\u103a\u101c\u102f\u1015\u103a\u1014\u102d\u102f\u1004\u103a\u1005\u1031\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\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<p> <span style=\"color: #000000;\">\u1021\u1031\u102c\u1000\u103a\u1015\u102b \u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1019\u103b\u102c\u1038\u101e\u100a\u103a Python \u1010\u103d\u1004\u103a \u1021\u1001\u103c\u102c\u1038\u101e\u1031\u102c \u1021\u101e\u102f\u1036\u1038\u1019\u103b\u102c\u1038\u101e\u1031\u102c \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-<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/my\/logistic-regression-python\/\" target=\"_blank\" rel=\"noopener\">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><br \/> <a href=\"https:\/\/statorials.org\/my\/python-matrix-\u101b\u103e\u102f\u1015\u103a\u1011\u103d\u1031\u1038\u1019\u103e\u102f\u1019\u103b\u102c\u1038\/\" target=\"_blank\" rel=\"noopener\">Python \u1010\u103d\u1004\u103a Confusion Matrix \u1016\u1014\u103a\u1010\u102e\u1038\u1014\u100a\u103a\u1038<\/a><br \/> <a href=\"https:\/\/statorials.org\/my\/\u1019\u103b\u103e\u1010\u101e\u1031\u102c\u1010\u102d\u1000\u103b\u101e\u1031\u102c-python-sklearn\/\">Python \u1010\u103d\u1004\u103a \u1019\u103b\u103e\u1010\u101e\u1031\u102c\u1010\u102d\u1000\u103b\u1019\u103e\u102f\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1014\u100a\u103a\u1038<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u1005\u1000\u103a\u101e\u1004\u103a\u101a\u1030\u1019\u103e\u102f\u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1016\u103c\u1005\u103a\u1005\u1031\u101e\u1031\u102c\u1021\u1001\u102b\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1014\u103e\u1005\u103a\u1005\u102f\u1036\u1021\u1016\u103c\u1005\u103a \u1001\u103d\u1032\u101c\u1031\u1037\u101b\u103e\u102d\u101e\u100a\u103a- 1. Training set- \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a (\u1019\u1030\u101b\u1004\u103a\u1038\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u104f 70-80%) 2. \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1036- \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1005\u103d\u1019\u103a\u1038\u1006\u1031\u102c\u1004\u103a\u101b\u100a\u103a\u104f \u1018\u1000\u103a\u1019\u101c\u102d\u102f\u1000\u103a\u101e\u1031\u102c \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a (\u1019\u1030\u101b\u1004\u103a\u1038\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u104f 20-30%) \u101b\u101b\u103e\u102d\u101b\u1014\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a\u104b Python \u1010\u103d\u1004\u103a\u104a \u1015\u1014\u103a\u1012\u102b DataFrame \u1000\u102d\u102f \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1021\u1005\u102f\u1036\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1021\u1016\u103c\u1005\u103a \u1001\u103d\u1032\u101b\u1014\u103a \u1018\u102f\u1036\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1014\u103e\u1005\u103a\u1001\u102f\u101b\u103e\u102d\u101e\u100a\u103a\u104b \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038 1- sklearn \u104f train_test_split() \u1000\u102d\u102f\u101e\u102f\u1036\u1038\u1015\u102b from sklearn. model_selection import train_test_split train, test = train_test_split(df, test_size= 0.2 , random_state= 0 ) \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038 [&hellip;]<\/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":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Pandas DataFrame - Statorials \u1019\u103e \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u1019\u103e\u102f\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1014\u100a\u103a\u1038<\/title>\n<meta name=\"description\" content=\"\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a \u1015\u1014\u103a\u1012\u102b\u101d\u1000\u103a\u101d\u1036 DataFrame \u1010\u1005\u103a\u1001\u102f\u1010\u100a\u103a\u1038\u1019\u103e \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u1019\u103e\u102f\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1036\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u101b\u1014\u103a \u101e\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u1037\u103a \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1019\u103b\u102c\u1038\u1005\u103d\u102c\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\u101b\u1011\u102c\u1038\u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\/\" \/>\n<meta property=\"og:locale\" content=\"my_MM\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas DataFrame - Statorials \u1019\u103e \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u1019\u103e\u102f\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1014\u100a\u103a\u1038\" \/>\n<meta property=\"og:description\" content=\"\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a \u1015\u1014\u103a\u1012\u102b\u101d\u1000\u103a\u101d\u1036 DataFrame \u1010\u1005\u103a\u1001\u102f\u1010\u100a\u103a\u1038\u1019\u103e \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u1019\u103e\u102f\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1036\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u101b\u1014\u103a \u101e\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u1037\u103a \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1011\u102c\u1038\u101e\u100a\u103a\u104b\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/my\/\u1015\u1014\u103a\u1012\u102b\u101b\u1011\u102c\u1038\u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-19T03:04:16+00:00\" \/>\n<meta name=\"author\" content=\"Benjamin Anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin Anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" 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