{"id":3194,"date":"2023-07-18T18:13:36","date_gmt":"2023-07-18T18:13:36","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d9%82%d9%8a%d9%85%d8%a9-%d8%a7%d9%84%d8%aa%d8%b3%d9%84%d8%b3%d9%84%d9%8a%d8%a9-%d9%84%d9%80-python-verite-%d8%ba%d8%a7%d9%85%d8%b6%d8%a9\/"},"modified":"2023-07-18T18:13:36","modified_gmt":"2023-07-18T18:13:36","slug":"%d8%a7%d9%84%d9%82%d9%8a%d9%85%d8%a9-%d8%a7%d9%84%d8%aa%d8%b3%d9%84%d8%b3%d9%84%d9%8a%d8%a9-%d9%84%d9%80-python-verite-%d8%ba%d8%a7%d9%85%d8%b6%d8%a9","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d9%82%d9%8a%d9%85%d8%a9-%d8%a7%d9%84%d8%aa%d8%b3%d9%84%d8%b3%d9%84%d9%8a%d8%a9-%d9%84%d9%80-python-verite-%d8%ba%d8%a7%d9%85%d8%b6%d8%a9\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u0625\u0635\u0644\u0627\u062d \u0641\u064a \u0627\u0644\u0628\u0627\u0646\u062f\u0627: \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u062d\u0642\u064a\u0642\u064a\u0629 \u0644\u0644\u0633\u0644\u0633\u0644\u0629 \u063a\u0627\u0645\u0636\u0629"},"content":{"rendered":"

<\/p>\n

\n

\u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u0630\u064a \u0642\u062f \u062a\u0648\u0627\u062c\u0647\u0647 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646 \u0647\u0648:<\/span><\/p>\n

 ValueError<\/span> : The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(),\n            a.any() or a.all().\n<\/strong><\/pre>\n
 \u064a\u062d\u062f\u062b \u0647\u0630\u0627 \u0627\u0644\u062e\u0637\u0623 \u0639\u0627\u062f\u0629\u064b \u0639\u0646\u062f \u0645\u062d\u0627\u0648\u0644\u0629 \u062a\u0635\u0641\u064a\u0629 Pandas DataFrame \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0643\u0644\u0645\u0627\u062a \u0648<\/strong> \u0648 \u0623\u0648<\/strong> \u0628\u062f\u0644\u0627\u064b \u0645\u0646 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0623\u062d\u0631\u0641 &<\/strong> \u0648 |<\/strong> \u0627\u0644\u0645\u0634\u063a\u0644\u064a\u0646.<\/span><\/p>\n
 \u064a\u0634\u0631\u062d \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0643\u064a\u0641\u064a\u0629 \u062d\u0644 \u0647\u0630\u0627 \u0627\u0644\u062e\u0637\u0623 \u0639\u0645\u0644\u064a\u064b\u0627.<\/span><\/p>\n
 \u0643\u064a\u0641\u064a\u0629 \u0625\u0639\u0627\u062f\u0629 \u0625\u0646\u062a\u0627\u062c \u0627\u0644\u062e\u0637\u0623<\/strong><\/h3>\n
 \u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646\u0646\u0627 \u0623\u0646\u0634\u0623\u0646\u0627 DataFrame \u0627\u0644\u0628\u0627\u0646\u062f\u0627 \u0627\u0644\u062a\u0627\u0644\u064a\u0629:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' points<\/span> ': [18, 22, 19, 14, 14, 11, 20, 28],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points assists rebounds\n0 A 18 5 11\n1 to 22 7 8\n2 A 19 7 10\n3 A 14 9 6\n4 B 14 12 6\n5 B 11 9 5\n6 B 20 9 9\n7 B 28 4 12\n<\/strong><\/span><\/pre>\n
 \u0644\u0646\u0641\u062a\u0631\u0636 \u0627\u0644\u0622\u0646 \u0623\u0646\u0646\u0627 \u0646\u062d\u0627\u0648\u0644 \u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u0635\u0641\u0648\u0641 \u0627\u0644\u062a\u064a \u064a\u0633\u0627\u0648\u064a \u0641\u064a\u0647\u0627 \u0627\u0644\u0641\u0631\u064a\u0642 “A” \u0648\u062a\u0643\u0648\u0646<\/strong> \u0627\u0644\u0646\u0642\u0627\u0637 \u0623\u0642\u0644 \u0645\u0646 20:<\/span><\/p>\n
 #attempt to filter DataFrame\n<\/span>df[(df[' team<\/span> '] == ' A<\/span> ') and<\/span> (df[' points<\/span> '] < 20<\/span> )]\n\nValueError<\/span> : The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(),\n            a.any() or a.all().\n<\/strong><\/span><\/pre>\n
 \u0623\u0648 \u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646\u0646\u0627 \u0646\u062d\u0627\u0648\u0644 \u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u0635\u0641\u0648\u0641 \u0627\u0644\u062a\u064a \u064a\u0633\u0627\u0648\u064a \u0641\u064a\u0647\u0627 \u0627\u0644\u0641\u0631\u064a\u0642 “A” \u0623\u0648<\/b> \u062d\u064a\u062b \u062a\u0643\u0648\u0646 \u0627\u0644\u0646\u0642\u0627\u0637 \u0623\u0642\u0644 \u0645\u0646 20:<\/span><\/p>\n
 #attempt to filter DataFrame\n<\/span>df[(df['<\/span> team<\/span> '] == '<\/span> A<\/span> ')<\/span> or<\/span> (df['<\/span> points<\/span> '] <<\/span> 20<\/span> )]\n\n<\/span>ValueError<\/span> : The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(),\n            a.any() or a.all().<\/span><\/strong><\/span><\/pre>\n
 \u0641\u064a \u0643\u0644\u0627 \u0627\u0644\u0633\u064a\u0646\u0627\u0631\u064a\u0648\u0647\u064a\u0646\u060c \u0646\u062a\u0644\u0642\u0649 \u062e\u0637\u0623 \u064a\u062e\u0628\u0631\u0646\u0627 \u0623\u0646 \u0642\u064a\u0645\u0629 \u0627\u0644\u062d\u0642\u064a\u0642\u0629 \u0644\u0644\u0645\u062a\u0633\u0644\u0633\u0644\u0629 \u063a\u0627\u0645\u0636\u0629.<\/span><\/p>\n
 \u0643\u064a\u0641\u064a\u0629 \u0625\u0635\u0644\u0627\u062d \u0627\u0644\u062e\u0637\u0623<\/strong><\/h3>\n
 \u0644\u062a\u062c\u0646\u0628 \u0647\u0630\u0627 \u0627\u0644\u062e\u0637\u0623 \u0639\u0646\u062f \u0627\u0644\u062a\u0635\u0641\u064a\u0629\u060c \u0646\u062d\u062a\u0627\u062c \u0625\u0644\u0649 \u0627\u0644\u062a\u0623\u0643\u062f \u0645\u0646 \u0623\u0646\u0646\u0627 \u0646\u0633\u062a\u062e\u062f\u0645 &<\/strong> \u0648 |<\/strong> \u0639\u0646\u0627\u0635\u0631. \u0627\u0644\u0645\u0634\u063a\u0644\u064a\u0646.<\/span><\/p>\n
 \u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \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\u0641\u064a\u0629 \u0627\u0644\u0635\u0641\u0648\u0641 \u0627\u0644\u062a\u064a \u064a\u0633\u0627\u0648\u064a \u0641\u064a\u0647\u0627 \u0627\u0644\u0641\u0631\u064a\u0642 “A” \u0648\u0627\u0644\u0646\u0642\u0627\u0637<\/strong> \u0623\u0642\u0644 \u0645\u0646 20:<\/span><\/p>\n
 #filter DataFrame\n<\/span>df[(df['<\/span> team<\/span> '] == '<\/span> A<\/span> ')<\/span> &<\/span> (df['<\/span> points<\/span> '] <<\/span> 20<\/span> )]\n\n<\/span>team points assists rebounds\n0 A 18 5 11\n2 A 19 7 10\n3 A 14 9 6<\/span><\/strong><\/span><\/pre>\n
 \u0623\u0648 \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\u0641\u064a\u0629 \u0627\u0644\u0635\u0641\u0648\u0641 \u0627\u0644\u062a\u064a \u064a\u0633\u0627\u0648\u064a \u0641\u064a\u0647\u0627 \u0627\u0644\u0641\u0631\u064a\u0642 “A” \u0623\u0648<\/b> \u062a\u0643\u0648\u0646 \u0627\u0644\u0646\u0642\u0627\u0637 \u0623\u0642\u0644 \u0645\u0646 20:<\/span><\/p>\n
 #filter DataFrame\n<\/span>df[(df[' team<\/span> '] == ' A<\/span> ') |<\/span> (df[' points<\/span> '] < 20<\/span> )]\n\n        team points assists rebounds\n0 A 18 5 11\n1 to 22 7 8\n2 A 19 7 10\n3 A 14 9 6\n4 B 14 12 6\n5 B 11 9 5<\/strong><\/span><\/pre>\n
 \u0641\u064a \u0643\u0644\u0627 \u0627\u0644\u0633\u064a\u0646\u0627\u0631\u064a\u0648\u0647\u064a\u0646\u060c \u0644\u0627 \u0646\u062a\u0644\u0642\u0649 \u062e\u0637\u0623 \u0644\u0623\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0645\u0646\u0627 &<\/strong> \u0648 |<\/strong> \u0639\u0646\u0627\u0635\u0631. \u0627\u0644\u0645\u0634\u063a\u0644\u064a\u0646.<\/span><\/p>\n
 \u0645\u0644\u0627\u062d\u0638\u0629<\/strong> : \u0645\u0646 \u0627\u0644\u0645\u0647\u0645 \u062a\u0636\u0645\u064a\u0646 \u0642\u0648\u0633\u064a\u0646 \u062d\u0648\u0644 \u0643\u0644 \u0634\u0631\u0637 \u0639\u0644\u0649 \u062d\u062f\u0629 \u0639\u0646\u062f \u062a\u0635\u0641\u064a\u0629 \u0625\u0637\u0627\u0631 \u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0628\u0627\u0646\u062f\u0627 \u062d\u0633\u0628 \u0634\u0631\u0648\u0637 \u0645\u062a\u0639\u062f\u062f\u0629\u060c \u0648\u0625\u0644\u0627 \u0641\u0633\u0648\u0641 \u062a\u062a\u0644\u0642\u0649 \u062e\u0637\u0623.<\/span><\/p>\n
 \u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h3>\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 \u0625\u0635\u0644\u0627\u062d \u0627\u0644\u0623\u062e\u0637\u0627\u0621 \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 \u0627\u0644\u0625\u0635\u0644\u0627\u062d: \u0644\u0627 \u062a\u062d\u062a\u0648\u064a \u0648\u062d\u062f\u0629 “pandas” \u0639\u0644\u0649 \u0633\u0645\u0629 “dataframe”.<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u0625\u0635\u0644\u0627\u062d \u0641\u064a Pandas:SettingWithCopyWarning<\/a>
 \u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u0625\u0635\u0644\u0627\u062d \u0641\u064a Pandas: \u062e\u0637\u0623 \u0641\u064a \u0627\u0644\u0643\u062a\u0627\u0628\u0629: \u0644\u0627 \u062a\u0648\u062c\u062f \u0628\u064a\u0627\u0646\u0627\u062a \u0631\u0642\u0645\u064a\u0629 \u0644\u0644\u062a\u062e\u0637\u064a\u0637<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u0630\u064a \u0642\u062f \u062a\u0648\u0627\u062c\u0647\u0647 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646 \u0647\u0648: ValueError : The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). \u064a\u062d\u062f\u062b \u0647\u0630\u0627 \u0627\u0644\u062e\u0637\u0623 \u0639\u0627\u062f\u0629\u064b \u0639\u0646\u062f \u0645\u062d\u0627\u0648\u0644\u0629 \u062a\u0635\u0641\u064a\u0629 Pandas DataFrame \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0643\u0644\u0645\u0627\u062a \u0648 \u0648 \u0623\u0648 \u0628\u062f\u0644\u0627\u064b \u0645\u0646 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0623\u062d\u0631\u0641 & \u0648 | \u0627\u0644\u0645\u0634\u063a\u0644\u064a\u0646. \u064a\u0634\u0631\u062d \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0643\u064a\u0641\u064a\u0629 \u062d\u0644 \u0647\u0630\u0627 \u0627\u0644\u062e\u0637\u0623 \u0639\u0645\u0644\u064a\u064b\u0627. […]<\/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":"\n\u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u0625\u0635\u0644\u0627\u062d \u0641\u064a Pandas: \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u062d\u0642\u064a\u0642\u064a\u0629 \u0644\u0644\u0633\u0644\u0633\u0644\u0629 \u063a\u0627\u0645\u0636\u0629 - Statorials<\/title>\n<meta name=\"description\" content=\"\u064a\u0634\u0631\u062d \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0643\u064a\u0641\u064a\u0629 \u0625\u0635\u0644\u0627\u062d \u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u062a\u0627\u0644\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646: ValueError: \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u062d\u0642\u064a\u0642\u064a\u0629 \u0644\u0644\u0633\u0644\u0633\u0644\u0629 \u063a\u0627\u0645\u0636\u0629. \u0627\u0633\u062a\u062e\u062f\u0645 a.empty \u0623\u0648 a.bool() \u0623\u0648 a.item() \u0623\u0648 a.any() \u0623\u0648 a.all().\" \/>\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\/ar\/\u0627\u0644\u0642\u064a\u0645\u0629-\u0627\u0644\u062a\u0633\u0644\u0633\u0644\u064a\u0629-\u0644\u0640-python-verite-\u063a\u0627\u0645\u0636\u0629\/\" \/>\n<meta property=\"og:locale\" content=\"az_AZ\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u0625\u0635\u0644\u0627\u062d \u0641\u064a Pandas: \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u062d\u0642\u064a\u0642\u064a\u0629 \u0644\u0644\u0633\u0644\u0633\u0644\u0629 \u063a\u0627\u0645\u0636\u0629 - Statorials\" \/>\n<meta property=\"og:description\" content=\"\u064a\u0634\u0631\u062d \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0643\u064a\u0641\u064a\u0629 \u0625\u0635\u0644\u0627\u062d \u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u062a\u0627\u0644\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646: ValueError: \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u062d\u0642\u064a\u0642\u064a\u0629 \u0644\u0644\u0633\u0644\u0633\u0644\u0629 \u063a\u0627\u0645\u0636\u0629. \u0627\u0633\u062a\u062e\u062f\u0645 a.empty \u0623\u0648 a.bool() \u0623\u0648 a.item() \u0623\u0648 a.any() \u0623\u0648 a.all().\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ar\/\u0627\u0644\u0642\u064a\u0645\u0629-\u0627\u0644\u062a\u0633\u0644\u0633\u0644\u064a\u0629-\u0644\u0640-python-verite-\u063a\u0627\u0645\u0636\u0629\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-18T18:13:36+00:00\" \/>\n<meta name=\"author\" content=\"\u062f\u0643\u062a\u0648\u0631 \u0628\u0646\u064a\u0627\u0645\u064a\u0646 \u0623\u0646\u062f\u0631\u0633\u0648\u0646\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u062f\u0643\u062a\u0648\u0631 \u0628\u0646\u064a\u0627\u0645\u064a\u0646 \u0623\u0646\u062f\u0631\u0633\u0648\u0646\" \/>\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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\u0627\u0644\u0648\u0627\u0633\u0639\u0629 \u0641\u064a \u0645\u062c\u0627\u0644 \u0627\u0644\u0625\u062d\u0635\u0627\u0621\u060c \u0641\u0623\u0646\u0627 \u062d\u0631\u064a\u0635 \u0639\u0644\u0649 \u0645\u0634\u0627\u0631\u0643\u0629 \u0645\u0639\u0631\u0641\u062a\u064a \u0644\u062a\u0645\u0643\u064a\u0646 \u0627\u0644\u0637\u0644\u0627\u0628 \u0645\u0646 \u062e\u0644\u0627\u0644 Statorials. \u062a\u0639\u0631\u0641 \u0623\u0643\u062b\u0631\",\"sameAs\":[\"https:\/\/statorials.org\/ar\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"\u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u0625\u0635\u0644\u0627\u062d \u0641\u064a Pandas: \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u062d\u0642\u064a\u0642\u064a\u0629 \u0644\u0644\u0633\u0644\u0633\u0644\u0629 \u063a\u0627\u0645\u0636\u0629 - Statorials","description":"\u064a\u0634\u0631\u062d \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0643\u064a\u0641\u064a\u0629 \u0625\u0635\u0644\u0627\u062d \u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u062a\u0627\u0644\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646: ValueError: \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u062d\u0642\u064a\u0642\u064a\u0629 \u0644\u0644\u0633\u0644\u0633\u0644\u0629 \u063a\u0627\u0645\u0636\u0629. \u0627\u0633\u062a\u062e\u062f\u0645 a.empty \u0623\u0648 a.bool() \u0623\u0648 a.item() \u0623\u0648 a.any() \u0623\u0648 a.all().","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/statorials.org\/ar\/\u0627\u0644\u0642\u064a\u0645\u0629-\u0627\u0644\u062a\u0633\u0644\u0633\u0644\u064a\u0629-\u0644\u0640-python-verite-\u063a\u0627\u0645\u0636\u0629\/","og_locale":"az_AZ","og_type":"article","og_title":"\u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u0625\u0635\u0644\u0627\u062d \u0641\u064a Pandas: 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minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d9%82%d9%8a%d9%85%d8%a9-%d8%a7%d9%84%d8%aa%d8%b3%d9%84%d8%b3%d9%84%d9%8a%d8%a9-%d9%84%d9%80-python-verite-%d8%ba%d8%a7%d9%85%d8%b6%d8%a9\/","url":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d9%82%d9%8a%d9%85%d8%a9-%d8%a7%d9%84%d8%aa%d8%b3%d9%84%d8%b3%d9%84%d9%8a%d8%a9-%d9%84%d9%80-python-verite-%d8%ba%d8%a7%d9%85%d8%b6%d8%a9\/","name":"\u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u0625\u0635\u0644\u0627\u062d \u0641\u064a Pandas: \u0627\u0644\u0642\u064a\u0645\u0629 \u0627\u0644\u062d\u0642\u064a\u0642\u064a\u0629 \u0644\u0644\u0633\u0644\u0633\u0644\u0629 \u063a\u0627\u0645\u0636\u0629 - 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