{"id":3120,"date":"2023-07-19T03:04:16","date_gmt":"2023-07-19T03:04:16","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d8%ae%d8%aa%d8%a8%d8%a7%d8%b1-%d9%82%d8%b7%d8%a7%d8%b1-%d8%a7%d9%84%d8%a8%d8%a7%d9%86%d8%af%d8%a7\/"},"modified":"2023-07-19T03:04:16","modified_gmt":"2023-07-19T03:04:16","slug":"%d8%a7%d8%ae%d8%aa%d8%a8%d8%a7%d8%b1-%d9%82%d8%b7%d8%a7%d8%b1-%d8%a7%d9%84%d8%a8%d8%a7%d9%86%d8%af%d8%a7","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d8%ae%d8%aa%d8%a8%d8%a7%d8%b1-%d9%82%d8%b7%d8%a7%d8%b1-%d8%a7%d9%84%d8%a8%d8%a7%d9%86%d8%af%d8%a7\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0645\u062c\u0645\u0648\u0639\u0629 \u0642\u0637\u0627\u0631 \u0648\u0627\u062e\u062a\u0628\u0627\u0631 \u0645\u0646 pandas dataframe"},"content":{"rendered":"<p><\/p>\n<hr>\n<p style=\";text-align:right;direction:rtl\"><span style=\"color: #000000;\">\u0639\u0646\u062f \u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a \u0644\u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a\u060c \u063a\u0627\u0644\u0628\u064b\u0627 \u0645\u0627 \u0646\u0642\u0648\u0645 \u0628\u062a\u0642\u0633\u064a\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u062a\u064a\u0646:<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>1. \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u062a\u062f\u0631\u064a\u0628:<\/strong> \u062a\u0633\u062a\u062e\u062f\u0645 \u0644\u062a\u062f\u0631\u064a\u0628 \u0627\u0644\u0646\u0645\u0648\u0630\u062c (70-80% \u0645\u0646 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0623\u0635\u0644\u064a\u0629)<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>2. \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631:<\/strong> \u062a\u0633\u062a\u062e\u062f\u0645 \u0644\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u062a\u0642\u062f\u064a\u0631 \u063a\u064a\u0631 \u0645\u062a\u062d\u064a\u0632 \u0644\u0623\u062f\u0627\u0621 \u0627\u0644\u0646\u0645\u0648\u0630\u062c (20-30% \u0645\u0646 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0623\u0635\u0644\u064a\u0629)<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646\u060c \u0647\u0646\u0627\u0643 \u0637\u0631\u064a\u0642\u062a\u0627\u0646 \u0634\u0627\u0626\u0639\u062a\u0627\u0646 \u0644\u062a\u0642\u0633\u064a\u0645 \u0625\u0637\u0627\u0631 \u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0628\u0627\u0646\u062f\u0627 \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0629 \u062a\u062f\u0631\u064a\u0628 \u0648\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u062e\u062a\u0628\u0627\u0631:<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u0637\u0631\u064a\u0642\u0629 \u0627\u0644\u0623\u0648\u0644\u0649: \u0627\u0633\u062a\u062e\u062f\u0627\u0645 Train_test_split() \u0627\u0644\u062e\u0627\u0635 \u0628\u0640 sklearn<\/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 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u0637\u0631\u064a\u0642\u0629 \u0627\u0644\u062b\u0627\u0646\u064a\u0629: \u0627\u0633\u062a\u062e\u062f\u0627\u0645 Sample() \u0645\u0646 \u0627\u0644\u0628\u0627\u0646\u062f\u0627<\/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 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062a\u0648\u0636\u062d \u0627\u0644\u0623\u0645\u062b\u0644\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0643\u0644 \u0637\u0631\u064a\u0642\u0629 \u0645\u0639 \u0627\u0644\u0628\u0627\u0646\u062f\u0627 DataFrame \u0627\u0644\u062a\u0627\u0644\u064a\u0629:<\/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 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0645\u062b\u0627\u0644 1: \u0627\u0633\u062a\u062e\u062f\u0645 Train_test_split() \u0645\u0646 sklearn<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u064a\u0648\u0636\u062d \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 <strong>Train_test_split()<\/strong> \u0627\u0644\u062e\u0627\u0635\u0629 \u0628\u0640 <strong>sklearn<\/strong> \u0644\u062a\u0642\u0633\u064a\u0645 Pandas DataFrame \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0627\u062a \u062a\u062f\u0631\u064a\u0628 \u0648\u0627\u062e\u062a\u0628\u0627\u0631:<\/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 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u0646 \u0627\u0644\u0646\u062a\u064a\u062c\u0629 \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646\u0647 \u062a\u0645 \u0625\u0646\u0634\u0627\u0621 \u0645\u062c\u0645\u0648\u0639\u062a\u064a\u0646:<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u062a\u062f\u0631\u064a\u0628: 800 \u0635\u0641 \u06483 \u0623\u0639\u0645\u062f\u0629<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631: 200 \u0635\u0641 \u06483 \u0623\u0639\u0645\u062f\u0629<\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0644\u0627\u062d\u0638 \u0623\u0646 <strong>test_size<\/strong> \u064a\u062a\u062d\u0643\u0645 \u0641\u064a \u0627\u0644\u0646\u0633\u0628\u0629 \u0627\u0644\u0645\u0626\u0648\u064a\u0629 \u0644\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0645\u0646 DataFrame \u0627\u0644\u0623\u0635\u0644\u064a \u0627\u0644\u062a\u064a \u0633\u062a\u0646\u062a\u0645\u064a \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631 \u0648\u0623\u0646 \u0642\u064a\u0645\u0629 <strong>Random_state<\/strong> \u062a\u062c\u0639\u0644 \u0627\u0644\u0627\u0646\u0642\u0633\u0627\u0645 \u0642\u0627\u0628\u0644\u0627\u064b \u0644\u0644\u062a\u0643\u0631\u0627\u0631.<\/span><\/p>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u0645\u062b\u0627\u0644 2: \u0627\u0633\u062a\u062e\u062f\u0645 Sample() \u0645\u0646 \u0627\u0644\u0628\u0627\u0646\u062f\u0627<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0648\u0636\u062d \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 <b>pandas<\/b> <strong>Sample()<\/strong> \u0644\u062a\u0642\u0633\u064a\u0645 Pandas DataFrame \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0627\u062a \u062a\u062f\u0631\u064a\u0628 \u0648\u0627\u062e\u062a\u0628\u0627\u0631:<\/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 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u0646 \u0627\u0644\u0646\u062a\u064a\u062c\u0629 \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646\u0647 \u062a\u0645 \u0625\u0646\u0634\u0627\u0621 \u0645\u062c\u0645\u0648\u0639\u062a\u064a\u0646:<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u062a\u062f\u0631\u064a\u0628: 800 \u0635\u0641 \u06483 \u0623\u0639\u0645\u062f\u0629<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631: 200 \u0635\u0641 \u06483 \u0623\u0639\u0645\u062f\u0629<\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0644\u0627\u062d\u0638 \u0623\u0646 <b>frac<\/b> \u064a\u062a\u062d\u0643\u0645 \u0641\u064a \u0627\u0644\u0646\u0633\u0628\u0629 \u0627\u0644\u0645\u0626\u0648\u064a\u0629 \u0644\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0645\u0646 DataFrame \u0627\u0644\u0623\u0635\u0644\u064a \u0627\u0644\u0630\u064a \u0633\u064a\u0646\u062a\u0645\u064a \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u062a\u062f\u0631\u064a\u0628 \u0648\u0623\u0646 \u0642\u064a\u0645\u0629 <strong>Random_state<\/strong> \u062a\u062c\u0639\u0644 \u0627\u0644\u0627\u0646\u0642\u0633\u0627\u0645 \u0642\u0627\u0628\u0644\u0627\u064b \u0644\u0644\u062a\u0643\u0631\u0627\u0631.<\/span><\/p>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062a\u0634\u0631\u062d \u0627\u0644\u0628\u0631\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0627\u0644\u0645\u0647\u0627\u0645 \u0627\u0644\u0634\u0627\u0626\u0639\u0629 \u0627\u0644\u0623\u062e\u0631\u0649 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646:<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <a href=\"https:\/\/statorials.org\/ar\/\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631-\u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a-\u0628\u064a\u062b\u0648\u0646\/\" target=\"_blank\" rel=\"noopener\">\u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a><br \/> <a href=\"https:\/\/statorials.org\/ar\/\u0627\u0631\u062a\u0628\u0627\u0643-\u0645\u0635\u0641\u0648\u0641\u0629-\u0628\u0627\u064a\u062b\u0648\u0646\/\" target=\"_blank\" rel=\"noopener\">\u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0645\u0635\u0641\u0648\u0641\u0629 \u0627\u0644\u0627\u0631\u062a\u0628\u0627\u0643 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a><br \/> <a href=\"https:\/\/statorials.org\/ar\/\u062f\u0642\u0629-\u0645\u062a\u0648\u0627\u0632\u0646\u0629-\u0628\u064a\u062b\u0648\u0646-sklearn\/\">\u0643\u064a\u0641\u064a\u0629 \u062d\u0633\u0627\u0628 \u0627\u0644\u062f\u0642\u0629 \u0627\u0644\u0645\u062a\u0648\u0627\u0632\u0646\u0629 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0639\u0646\u062f \u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a \u0644\u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a\u060c \u063a\u0627\u0644\u0628\u064b\u0627 \u0645\u0627 \u0646\u0642\u0648\u0645 \u0628\u062a\u0642\u0633\u064a\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u062a\u064a\u0646: 1. \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u062a\u062f\u0631\u064a\u0628: \u062a\u0633\u062a\u062e\u062f\u0645 \u0644\u062a\u062f\u0631\u064a\u0628 \u0627\u0644\u0646\u0645\u0648\u0630\u062c (70-80% \u0645\u0646 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0623\u0635\u0644\u064a\u0629) 2. \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631: \u062a\u0633\u062a\u062e\u062f\u0645 \u0644\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u062a\u0642\u062f\u064a\u0631 \u063a\u064a\u0631 \u0645\u062a\u062d\u064a\u0632 \u0644\u0623\u062f\u0627\u0621 \u0627\u0644\u0646\u0645\u0648\u0630\u062c (20-30% \u0645\u0646 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0623\u0635\u0644\u064a\u0629) \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646\u060c \u0647\u0646\u0627\u0643 \u0637\u0631\u064a\u0642\u062a\u0627\u0646 \u0634\u0627\u0626\u0639\u062a\u0627\u0646 \u0644\u062a\u0642\u0633\u064a\u0645 \u0625\u0637\u0627\u0631 \u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0628\u0627\u0646\u062f\u0627 \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0629 \u062a\u062f\u0631\u064a\u0628 \u0648\u0645\u062c\u0645\u0648\u0639\u0629 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ 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