{"id":4306,"date":"2023-07-12T03:18:22","date_gmt":"2023-07-12T03:18:22","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%a8%d8%a7%d9%86%d8%af%d8%a7-%d8%b9%d8%a8%d8%b1-%d8%a7%d9%84%d8%ac%d8%af%d9%88%d9%84-aggfunc\/"},"modified":"2023-07-12T03:18:22","modified_gmt":"2023-07-12T03:18:22","slug":"%d8%a7%d9%84%d8%a8%d8%a7%d9%86%d8%af%d8%a7-%d8%b9%d8%a8%d8%b1-%d8%a7%d9%84%d8%ac%d8%af%d9%88%d9%84-aggfunc","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%a8%d8%a7%d9%86%d8%af%d8%a7-%d8%b9%d8%a8%d8%b1-%d8%a7%d9%84%d8%ac%d8%af%d9%88%d9%84-aggfunc\/","title":{"rendered":"\u0627\u0644\u0628\u0627\u0646\u062f\u0627: \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 aggfunc \u0641\u064a \u0648\u0638\u064a\u0641\u0629 \u0627\u0644\u062c\u062f\u0648\u0644 \u0627\u0644\u062a\u0631\u0627\u0641\u0642\u064a ()."},"content":{"rendered":"

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\u064a\u0645\u0643\u0646\u0643 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0648\u0633\u064a\u0637\u0629 aggfunc<\/b> \u0641\u064a \u0648\u0638\u064a\u0641\u0629 pandas crosstab()<\/strong> \u0644\u0625\u0646\u0634\u0627\u0621 \u062c\u062f\u0648\u0644 \u062a\u0631\u0627\u0641\u0642\u064a \u064a\u062c\u0645\u0639 \u0627\u0644\u0642\u064a\u0645 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0645\u0642\u064a\u0627\u0633 \u0645\u062d\u062f\u062f:<\/span><\/p>\n

 p.d. crosstab<\/span> (index=df. col1<\/span> , columns=df. col2<\/span> , values=df. col3<\/span> , aggfunc=' count<\/span> ')\n<\/strong><\/pre>\n
 \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u0627\u0641\u062a\u0631\u0627\u0636\u064a\u0629 \u0644\u0640 aggfunc<\/strong> \u0647\u064a “count”\u060c \u0648\u0644\u0643\u0646 \u064a\u0645\u0643\u0646\u0643 \u062a\u062d\u062f\u064a\u062f \u0637\u0631\u0642 \u062a\u062c\u0645\u064a\u0639 \u0623\u062e\u0631\u0649 \u0645\u062b\u0644 \u0627\u0644\u0645\u062a\u0648\u0633\u0637\u060c \u0648\u0627\u0644\u0648\u0633\u064a\u0637\u060c \u0648\u0627\u0644\u0645\u062c\u0645\u0648\u0639\u060c \u0648\u0627\u0644\u062d\u062f \u0627\u0644\u0623\u062f\u0646\u0649\u060c \u0648\u0627\u0644\u062d\u062f \u0627\u0644\u0623\u0642\u0635\u0649\u060c \u0648\u0645\u0627 \u0625\u0644\u0649 \u0630\u0644\u0643.<\/span><\/p>\n
 \u064a\u0645\u0643\u0646\u0643 \u0623\u064a\u0636\u064b\u0627 \u062a\u062d\u062f\u064a\u062f \u0637\u0631\u0642 \u062a\u062c\u0645\u064a\u0639 \u0645\u062a\u0639\u062f\u062f\u0629 \u0641\u064a \u0627\u0644\u0648\u0633\u064a\u0637\u0629 aggfunc<\/strong> :<\/span><\/p>\n
 p.d. crosstab<\/span> (index=df. col1<\/span> , columns=df. col2<\/span> , values=df. col3<\/span> , aggfunc=[' min<\/span> ', ' max<\/span> '])<\/strong><\/pre>\n
 \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 \u0645\u0646 \u0647\u0630\u0647 \u0627\u0644\u0637\u0631\u0642 \u0639\u0645\u0644\u064a\u064b\u0627 \u0645\u0639 \u0648\u062d\u062f\u0627\u062a \u0627\u0644\u0628\u0627\u0646\u062f\u0627 DataFrame \u0627\u0644\u062a\u0627\u0644\u064a\u0629:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C '],\n                   ' position<\/span> ':['G', 'G', 'F', 'G', 'F', 'F', 'F', 'G', 'G', 'F', 'F'],\n                   ' points<\/span> ': [22, 25, 24, 39, 34, 20, 18, 17, 20, 19, 22]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n   team position points\n0 AG 22\n1 AG 25\n2 AF 24\n3 BG 39\n4 BF 34\n5 BF 20\n6 BF 18\n7 GC 17\n8 GC 20\n9 CF 19\n10 CF 22<\/strong>\n<\/pre>\n
 \u0627\u0644\u0645\u062b\u0627\u0644 1: \u0625\u0646\u0634\u0627\u0621 \u062c\u062f\u0648\u0644 \u062a\u0631\u0627\u0641\u0642\u064a \u0628\u0642\u064a\u0645\u0629 \u0641\u064a aggfunc<\/strong><\/span><\/h2>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 \u0627\u0644\u062c\u062f\u0648\u0644\u064a\u0629 ()<\/strong> \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0645\u0639 \u0627\u0644\u0648\u0633\u064a\u0637\u0629 aggfunc=’mean’<\/strong> \u0644\u0625\u0646\u0634\u0627\u0621 \u062c\u062f\u0648\u0644 \u062a\u0631\u0627\u0641\u0642\u064a \u064a\u0639\u0631\u0636 \u0645\u062a\u0648\u0633\u0637 \u0646\u0642\u0627\u0637 \u0627\u0644\u0642\u064a\u0645\u0629 \u0644\u0643\u0644 \u0645\u0646\u0635\u0628 \u0648\u0645\u062c\u0645\u0648\u0639\u0629 \u0641\u0631\u064a\u0642:<\/span><\/p>\n
 #create crosstab that displays mean points value by team and position\n<\/span><\/span>p.d. crosstab<\/span> (index=df. team<\/span> , columns=df. position<\/span> , values=df. points<\/span> , aggfunc=' mean<\/span> ')\n\nposition F G\nteam\t\t\nA 24.0 23.5\nB 24.0 39.0\nC 20.5 18.5\n<\/strong><\/pre>\n
 \u0648\u0625\u0644\u064a\u0643 \u0643\u064a\u0641\u064a\u0629 \u062a\u0641\u0633\u064a\u0631 \u0627\u0644\u0646\u062a\u064a\u062c\u0629:<\/span><\/p>\n