{"id":3605,"date":"2023-07-16T14:11:02","date_gmt":"2023-07-16T14:11:02","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%aa%d8%ad%d8%ac%d9%8a%d9%85-%d9%85%d8%aa%d8%b9%d8%af%d8%af-%d8%a7%d9%84%d8%a7%d9%94%d8%a8%d8%b9%d8%a7%d8%af-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/"},"modified":"2023-07-16T14:11:02","modified_gmt":"2023-07-16T14:11:02","slug":"%d8%a7%d9%84%d8%aa%d8%ad%d8%ac%d9%8a%d9%85-%d9%85%d8%aa%d8%b9%d8%af%d8%af-%d8%a7%d9%84%d8%a7%d9%94%d8%a8%d8%b9%d8%a7%d8%af-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%aa%d8%ad%d8%ac%d9%8a%d9%85-%d9%85%d8%aa%d8%b9%d8%af%d8%af-%d8%a7%d9%84%d8%a7%d9%94%d8%a8%d8%b9%d8%a7%d8%af-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0642\u064a\u0627\u0633 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u0623\u0628\u0639\u0627\u062f \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646"},"content":{"rendered":"

<\/p>\n

\n

\u0641\u064a \u0627\u0644\u0625\u062d\u0635\u0627\u0621\u060c \u064a\u0639\u062f \u0627\u0644\u0642\u064a\u0627\u0633 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u0623\u0628\u0639\u0627\u062f<\/strong> \u0637\u0631\u064a\u0642\u0629 \u0644\u062a\u0635\u0648\u0631 \u062a\u0634\u0627\u0628\u0647 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0641\u064a \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0641\u064a \u0627\u0644\u0641\u0636\u0627\u0621 \u0627\u0644\u062f\u064a\u0643\u0627\u0631\u062a\u064a \u0627\u0644\u0645\u062c\u0631\u062f (\u0639\u0627\u062f\u0629\u064b \u0627\u0644\u0641\u0636\u0627\u0621 \u062b\u0646\u0627\u0626\u064a \u0627\u0644\u0623\u0628\u0639\u0627\u062f).<\/span><\/p>\n

\u0623\u0633\u0647\u0644 \u0637\u0631\u064a\u0642\u0629 \u0644\u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0642\u064a\u0627\u0633 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u0623\u0628\u0639\u0627\u062f \u0641\u064a Python \u0647\u064a \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 MDS ()<\/strong> \u0644\u0644\u0648\u062d\u062f\u0629 \u0627\u0644\u0641\u0631\u0639\u064a\u0629 sklearn.manifold<\/strong> .<\/span><\/p>\n

\u064a\u0648\u0636\u062d \u0627\u0644\u0645\u062b\u0627\u0644 \u0627\u0644\u062a\u0627\u0644\u064a \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0647\u0630\u0647 \u0627\u0644\u0648\u0638\u064a\u0641\u0629 \u0639\u0645\u0644\u064a\u064b\u0627.<\/span><\/p>\n

\u0645\u062b\u0627\u0644: \u0627\u0644\u0642\u064a\u0627\u0633 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u0623\u0628\u0639\u0627\u062f \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/strong><\/span><\/h2>\n

\u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646 \u0644\u062f\u064a\u0646\u0627 DataFrame \u0627\u0644\u0628\u0627\u0646\u062f\u0627 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0627\u0644\u062a\u064a \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0645\u0639\u0644\u0648\u0645\u0627\u062a \u062d\u0648\u0644 \u0645\u062e\u062a\u0644\u0641 \u0644\u0627\u0639\u0628\u064a \u0643\u0631\u0629 \u0627\u0644\u0633\u0644\u0629:<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#create DataFrane\n<\/span>df = pd. DataFrame<\/span> ({' player<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K '],\n                   ' points<\/span> ': [4, 4, 6, 7, 8, 14, 16, 19, 25, 25, 28],\n                   ' assists<\/span> ': [3, 2, 2, 5, 4, 8, 7, 6, 8, 10, 11],\n                   ' blocks<\/span> ': [7, 3, 6, 7, 5, 8, 8, 4, 2, 2, 1],\n                   ' rebounds<\/span> ': [4, 5, 5, 6, 5, 8, 10, 4, 3, 2, 2]})\n\n#set player column as index column\n<\/span>df = df. set_index<\/span> (' player<\/span> ')\n\n#view Dataframe\n<\/span>print<\/span> (df)\n\n        points assists blocks rebounds\nplayer                                   \nA 4 3 7 4\nB 4 2 3 5\nC 6 2 6 5\nD 7 5 7 6\nE 8 4 5 5\nF 14 8 8 8\nG 16 7 8 10\nH 19 6 4 4\nI 25 8 2 3\nD 25 10 2 2\nK 28 11 1 2\n<\/strong><\/pre>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0644\u0625\u062c\u0631\u0627\u0621 \u062a\u062d\u062c\u064a\u0645 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u0623\u0628\u0639\u0627\u062f \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 MDS()<\/strong> \u0627\u0644\u062e\u0627\u0635\u0629 \u0628\u0648\u062d\u062f\u0629 sklearn.manifold<\/strong> :<\/span><\/p>\n
 from<\/span> sklearn. manifold<\/span> import<\/span> MDS\n\n#perform multi-dimensional scaling\n<\/span>mds = MDS(random_state= 0<\/span> )\nscaled_df = mds. fit_transform<\/span> (df)\n\n#view results of multi-dimensional scaling\n<\/span>print<\/span> (scaled_df)\n\n[[ 7.43654469 8.10247222]\n [4.13193821 10.27360901]\n [5.20534681 7.46919526]\n [6.22323046 4.45148627]\n [3.74110999 5.25591459]\n [3.69073384 -2.88017811]\n [3.89092087 -5.19100988]\n [ -3.68593169 -3.0821144 ]\n [ -9.13631889 -6.81016012]\n [ -8.97898385 -8.50414387]\n [-12.51859044 -9.08507097]]<\/span><\/span><\/strong><\/pre>\n
 \u062a\u0645 \u062a\u0642\u0644\u064a\u0644 \u0643\u0644 \u0635\u0641 \u0645\u0646 DataFrame \u0627\u0644\u0623\u0635\u0644\u064a \u0625\u0644\u0649 \u0625\u062d\u062f\u0627\u062b\u064a\u0627\u062a (x\u060c y).<\/span><\/p>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0644\u062a\u0635\u0648\u0631 \u0647\u0630\u0647 \u0627\u0644\u0625\u062d\u062f\u0627\u062b\u064a\u0627\u062a \u0641\u064a \u0645\u0633\u0627\u062d\u0629 \u062b\u0646\u0627\u0626\u064a\u0629 \u0627\u0644\u0623\u0628\u0639\u0627\u062f:<\/span> <\/p>\n
 import<\/span> matplotlib.pyplot as<\/span> plt\n\n#create scatterplot\n<\/span>plt. scatter<\/span> (scaled_df[:,0], scaled_df[:,1])\n\n#add axis labels\n<\/span>plt. xlabel<\/span> (' Coordinate 1<\/span> ')\nplt. ylabel<\/span> (' Coordinate 2<\/span> ')\n\n#add lables to each point\n<\/span>for<\/span> i, txt in<\/span> enumerate( df.index<\/span> ):\n    plt. annotate<\/span> (txt, (scaled_df[:,0][i]+.3, scaled_df[:,1][i]))\n\n#display scatterplot\n<\/span>plt. show<\/span> ()\n<\/strong><\/pre>\n
\"\u0627\u0644\u062a\u062d\u062c\u064a\u0645<\/p>\n
 \u0627\u0644\u0644\u0627\u0639\u0628\u0648\u0646 \u0641\u064a DataFrame \u0627\u0644\u0623\u0635\u0644\u064a \u0627\u0644\u0630\u064a\u0646 \u0644\u062f\u064a\u0647\u0645 \u0642\u064a\u0645 \u0645\u0645\u0627\u062b\u0644\u0629 \u0641\u064a \u0627\u0644\u0623\u0639\u0645\u062f\u0629 \u0627\u0644\u0623\u0631\u0628\u0639\u0629 \u0627\u0644\u0623\u0635\u0644\u064a\u0629 (\u0627\u0644\u0646\u0642\u0627\u0637\u060c \u0627\u0644\u062a\u0645\u0631\u064a\u0631\u0627\u062a \u0627\u0644\u062d\u0627\u0633\u0645\u0629\u060c \u0627\u0644\u0643\u062a\u0644\u060c \u0648\u0627\u0644\u0645\u0631\u062a\u062f\u0627\u062a) \u0642\u0631\u064a\u0628\u0648\u0646 \u0645\u0646 \u0628\u0639\u0636\u0647\u0645 \u0627\u0644\u0628\u0639\u0636 \u0641\u064a \u0627\u0644\u0645\u0624\u0627\u0645\u0631\u0629.<\/span><\/p>\n
 \u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0627\u0644\u0644\u0627\u0639\u0628\u0627\u0646 F<\/strong> \u0648 G<\/strong> \u0642\u0631\u064a\u0628\u0627\u0646 \u0645\u0646 \u0628\u0639\u0636\u0647\u0645\u0627 \u0627\u0644\u0628\u0639\u0636. \u0641\u064a\u0645\u0627 \u064a\u0644\u064a \u0642\u064a\u0645\u0647\u0627 \u0645\u0646 DataFrame \u0627\u0644\u0623\u0635\u0644\u064a:<\/span><\/p>\n
 #select rows with index labels 'F' and 'G'\ndf. loc<\/span> [[' F<\/span> ',' G<\/span> ']]\n\n        points assists blocks rebounds\nplayer\t\t\t\t\nF 14 8 8 8\nG 16 7 8 10\n<\/span><\/span><\/strong><\/pre>\n
 \u0625\u0646 \u0642\u064a\u0645 \u0627\u0644\u0646\u0642\u0627\u0637 \u0648\u0627\u0644\u062a\u0645\u0631\u064a\u0631\u0627\u062a \u0627\u0644\u062d\u0627\u0633\u0645\u0629 \u0648\u0627\u0644\u0643\u062a\u0644 \u0648\u0627\u0644\u0645\u0631\u062a\u062f\u0627\u062a \u0643\u0644\u0647\u0627 \u0645\u062a\u0634\u0627\u0628\u0647\u0629 \u062a\u0645\u0627\u0645\u064b\u0627\u060c \u0648\u0647\u0648 \u0645\u0627 \u064a\u0641\u0633\u0631 \u0633\u0628\u0628 \u0642\u0631\u0628\u0647\u0627 \u0645\u0646 \u0628\u0639\u0636\u0647\u0627 \u0627\u0644\u0628\u0639\u0636 \u0641\u064a \u0627\u0644\u0645\u062e\u0637\u0637 \u062b\u0646\u0627\u0626\u064a \u0627\u0644\u0623\u0628\u0639\u0627\u062f.<\/span><\/p>\n
 \u0641\u064a \u0627\u0644\u0645\u0642\u0627\u0628\u0644\u060c \u062e\u0630 \u0628\u0639\u064a\u0646 \u0627\u0644\u0627\u0639\u062a\u0628\u0627\u0631 \u0627\u0644\u0644\u0627\u0639\u0628\u064a\u0646 B<\/strong> \u0648 K<\/strong> \u0627\u0644\u0645\u062a\u0628\u0627\u0639\u062f\u064a\u0646 \u0641\u064a \u0627\u0644\u062d\u0628\u0643\u0629.<\/span><\/p>\n
 \u0625\u0630\u0627 \u0623\u0634\u0631\u0646\u0627 \u0625\u0644\u0649 \u0642\u064a\u0645\u0647\u0627 \u0641\u064a DataFrame \u0627\u0644\u0623\u0635\u0644\u064a\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646\u0647\u0627 \u0645\u062e\u062a\u0644\u0641\u0629 \u062a\u0645\u0627\u0645\u064b\u0627:<\/span><\/p>\n
 #select rows with index labels 'B' and 'K'\ndf. loc<\/span> [[' B<\/span> ',' K<\/span> ']]<\/span><\/span>\n\n        points assists blocks rebounds\nplayer\t\t\t\t\nB 4 2 3 5\nK 28 11 1 2<\/strong><\/pre>\n
 \u0644\u0630\u0627 \u0641\u0625\u0646 \u0627\u0644\u062d\u0628\u0643\u0629 \u062b\u0646\u0627\u0626\u064a\u0629 \u0627\u0644\u0623\u0628\u0639\u0627\u062f \u0647\u064a \u0637\u0631\u064a\u0642\u0629 \u062c\u064a\u062f\u0629 \u0644\u062a\u0635\u0648\u0631 \u0645\u062f\u0649 \u062a\u0634\u0627\u0628\u0647 \u0643\u0644 \u0644\u0627\u0639\u0628 \u0639\u0628\u0631 \u062c\u0645\u064a\u0639 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0641\u064a DataFframe.<\/span><\/p>\n
 \u064a\u062a\u0645 \u062a\u062c\u0645\u064a\u0639 \u0627\u0644\u0644\u0627\u0639\u0628\u064a\u0646 \u0630\u0648\u064a \u0627\u0644\u0625\u062d\u0635\u0627\u0626\u064a\u0627\u062a \u0627\u0644\u0645\u062a\u0634\u0627\u0628\u0647\u0629 \u0628\u0627\u0644\u0642\u0631\u0628 \u0645\u0646 \u0628\u0639\u0636\u0647\u0645 \u0627\u0644\u0628\u0639\u0636 \u0628\u064a\u0646\u0645\u0627 \u064a\u0643\u0648\u0646 \u0627\u0644\u0644\u0627\u0639\u0628\u0648\u0646 \u0630\u0648\u0648 \u0627\u0644\u0625\u062d\u0635\u0627\u0626\u064a\u0627\u062a \u0627\u0644\u0645\u062e\u062a\u0644\u0641\u0629 \u062c\u062f\u064b\u0627 \u0628\u0639\u064a\u062f\u064b\u0627 \u0639\u0646 \u0628\u0639\u0636\u0647\u0645 \u0627\u0644\u0628\u0639\u0636 \u0641\u064a \u0627\u0644\u0645\u0624\u0627\u0645\u0631\u0629.<\/span><\/p>\n
 \u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h2>\n
 \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
 \u0643\u064a\u0641\u064a\u0629 \u062a\u0637\u0628\u064a\u0639 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0625\u0632\u0627\u0644\u0629 \u0627\u0644\u0642\u064a\u0645 \u0627\u0644\u0645\u062a\u0637\u0631\u0641\u0629 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0627\u062e\u062a\u0628\u0627\u0631 \u0627\u0644\u062d\u0627\u0644\u0629 \u0627\u0644\u0637\u0628\u064a\u0639\u064a\u0629 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0641\u064a \u0627\u0644\u0625\u062d\u0635\u0627\u0621\u060c \u064a\u0639\u062f \u0627\u0644\u0642\u064a\u0627\u0633 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u0623\u0628\u0639\u0627\u062f \u0637\u0631\u064a\u0642\u0629 \u0644\u062a\u0635\u0648\u0631 \u062a\u0634\u0627\u0628\u0647 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0641\u064a \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0641\u064a \u0627\u0644\u0641\u0636\u0627\u0621 \u0627\u0644\u062f\u064a\u0643\u0627\u0631\u062a\u064a \u0627\u0644\u0645\u062c\u0631\u062f (\u0639\u0627\u062f\u0629\u064b \u0627\u0644\u0641\u0636\u0627\u0621 \u062b\u0646\u0627\u0626\u064a \u0627\u0644\u0623\u0628\u0639\u0627\u062f). \u0623\u0633\u0647\u0644 \u0637\u0631\u064a\u0642\u0629 \u0644\u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0642\u064a\u0627\u0633 \u0645\u062a\u0639\u062f\u062f \u0627\u0644\u0623\u0628\u0639\u0627\u062f \u0641\u064a Python \u0647\u064a \u0627\u0633\u062a\u062e\u062f\u0627\u0645 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