{"id":3858,"date":"2023-07-15T03:19:01","date_gmt":"2023-07-15T03:19:01","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%a8%d8%ad%d8%ab-%d8%b9%d9%86-%d8%b3%d9%84%d8%b3%d9%84%d8%a9-%d9%81%d9%8a-dataframe-%d8%a7%d9%84%d8%a8%d8%a7%d9%86%d8%af%d8%a7\/"},"modified":"2023-07-15T03:19:01","modified_gmt":"2023-07-15T03:19:01","slug":"%d8%a7%d9%84%d8%a8%d8%ad%d8%ab-%d8%b9%d9%86-%d8%b3%d9%84%d8%b3%d9%84%d8%a9-%d9%81%d9%8a-dataframe-%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%d9%84%d8%a8%d8%ad%d8%ab-%d8%b9%d9%86-%d8%b3%d9%84%d8%b3%d9%84%d8%a9-%d9%81%d9%8a-dataframe-%d8%a7%d9%84%d8%a8%d8%a7%d9%86%d8%af%d8%a7\/","title":{"rendered":"\u0627\u0644\u0628\u0627\u0646\u062f\u0627: \u0627\u0628\u062d\u062b \u0639\u0646 \u0633\u0644\u0633\u0644\u0629 \u0641\u064a \u062c\u0645\u064a\u0639 \u0623\u0639\u0645\u062f\u0629 dataframe"},"content":{"rendered":"

<\/p>\n

\n

\u064a\u0645\u0643\u0646\u0643 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0628\u0646\u0627\u0621 \u0627\u0644\u062c\u0645\u0644\u0629 \u0627\u0644\u062a\u0627\u0644\u064a \u0644\u0644\u0628\u062d\u062b \u0639\u0646 \u0633\u0644\u0633\u0644\u0629 \u0645\u0639\u064a\u0646\u0629 \u0641\u064a \u0643\u0644 \u0639\u0645\u0648\u062f \u0645\u0646 \u0627\u0644\u0628\u0627\u0646\u062f\u0627 DataFrame \u0648\u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u0635\u0641\u0648\u0641 \u0627\u0644\u062a\u064a \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0627\u0644\u0633\u0644\u0633\u0644\u0629 \u0641\u064a \u0639\u0645\u0648\u062f \u0648\u0627\u062d\u062f \u0639\u0644\u0649 \u0627\u0644\u0623\u0642\u0644:<\/span><\/p>\n

 #define filter\n<\/span>mask = np. column_stack<\/span> ([df[col]. str<\/span> . contains<\/span> (r \" my_string<\/span> \", na= False<\/span> ) for<\/span> col in<\/span> df])\n<\/span>\n#filter for rows where any column contains 'my_string'\n<\/span>df. loc<\/span> [mask. any<\/span> (axis= 1<\/span> )]<\/strong><\/pre>\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 \u0628\u0646\u0627\u0621 \u0627\u0644\u062c\u0645\u0644\u0629 \u0647\u0630\u0627 \u0639\u0645\u0644\u064a\u064b\u0627.<\/span><\/p>\n
 \u0645\u062b\u0627\u0644: \u0627\u0628\u062d\u062b \u0639\u0646 \u0633\u0644\u0633\u0644\u0629 \u0641\u064a \u0643\u0627\u0641\u0629 \u0623\u0639\u0645\u062f\u0629 Pandas DataFrame<\/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 \u0627\u0644\u062f\u0648\u0631 \u0627\u0644\u0623\u0648\u0644 \u0648\u0627\u0644\u062f\u0648\u0631 \u0627\u0644\u062b\u0627\u0646\u064a \u0644\u0645\u062e\u062a\u0644\u0641 \u0644\u0627\u0639\u0628\u064a \u0643\u0631\u0629 \u0627\u0644\u0633\u0644\u0629 \u0641\u064a \u0627\u0644\u0641\u0631\u064a\u0642:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' player<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n                   ' first_role<\/span> ': ['P Guard', 'P Guard', 'S Guard', 'S Forward',\n                                  'P Forward', 'Center', 'Center', 'Center'],\n                   ' second_role<\/span> ': ['S Guard', 'S Guard', 'Forward', 'S Guard',\n                                   'S Guard', 'S Forward', 'P Forward', 'P Forward']})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  player first_role second_role\n0 AP Guard S Guard\n1 BP Guard S Guard\n2 CS Guard Forward\n3DS Forward S Guard\n4 EP Forward S Guard\n5 F Center S Forward\n6 G Center P Forward\n7 H Center P Forward<\/span><\/span><\/strong><\/pre>\n
 \u064a\u0648\u0636\u062d \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0643\u064a\u0641\u064a\u0629 \u062a\u0635\u0641\u064a\u0629 Pandas DataFrame \u0644\u0644\u0635\u0641\u0648\u0641 \u0627\u0644\u062a\u064a \u062a\u0638\u0647\u0631 \u0641\u064a\u0647\u0627 \u0627\u0644\u0633\u0644\u0633\u0644\u0629 “Guard” \u0641\u064a \u0623\u064a \u0639\u0645\u0648\u062f:<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\n\n#define filter\n<\/span>mask = np. column_stack<\/span> ([df[col]. str<\/span> . contains<\/span> (r \" Guard<\/span> \", na= False<\/span> ) for<\/span> col in<\/span> df])\n\n#filter for rows where any column contains 'Guard'\n<\/span>df. loc<\/span> [mask. any<\/span> (axis= 1<\/span> )]\n\n        player first_role second_role\n0 A P Guard S Guard\n1 B P Guard S Guard\n2 C S Guard Forward\n3 D S Forward S Guard\n4 E P Forward S Guard\n<\/span><\/span><\/strong><\/pre>\n
 \u0644\u0627\u062d\u0638 \u0623\u0646 \u0643\u0644 \u0635\u0641 \u0645\u0646 DataFrame \u0627\u0644\u0646\u0627\u062a\u062c \u064a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0627\u0644\u0633\u0644\u0633\u0644\u0629 “Guard” \u0641\u064a \u0639\u0645\u0648\u062f \u0648\u0627\u062d\u062f \u0639\u0644\u0649 \u0627\u0644\u0623\u0642\u0644.<\/span><\/p>\n
 \u064a\u0645\u0643\u0646\u0643 \u0623\u064a\u0636\u064b\u0627 \u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u0635\u0641\u0648\u0641 \u0627\u0644\u062a\u064a \u062a\u0638\u0647\u0631 \u0641\u064a\u0647\u0627 \u0625\u062d\u062f\u0649 \u0627\u0644\u0633\u0644\u0627\u0633\u0644 \u0627\u0644\u0645\u062a\u0639\u062f\u062f\u0629 \u0641\u064a \u0639\u0645\u0648\u062f \u0648\u0627\u062d\u062f \u0639\u0644\u0649 \u0627\u0644\u0623\u0642\u0644 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0639\u0627\u0645\u0644 \u0627\u0644\u062a\u0634\u063a\u064a\u0644 “OR” ( |<\/strong> ) \u0641\u064a \u0627\u0644\u0628\u0627\u0646\u062f\u0627.<\/span><\/p>\n
 \u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \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 \u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u0635\u0641\u0648\u0641 \u0627\u0644\u062a\u064a \u064a\u0638\u0647\u0631 \u0641\u064a\u0647\u0627 “P Guard” \u0623\u0648 “Center” \u0641\u064a \u0639\u0645\u0648\u062f \u0648\u0627\u062d\u062f \u0639\u0644\u0649 \u0627\u0644\u0623\u0642\u0644:<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\n\n#define filter\n<\/span>mask = np. column_stack<\/span> ([df[col]. str<\/span> . contains<\/span> (r \" P Guard|Center<\/span> \", na= False<\/span> ) for<\/span> col in<\/span> df])\n\n#filter for rows where any column contains 'P Guard' or 'Center'\n<\/span>df. loc<\/span> [mask. any<\/span> (axis= 1<\/span> )]\n\n        player first_role second_role\n0 A P Guard S Guard\n1 B P Guard S Guard\n5 F Center S Forward\n6 G Center P Forward\n7 H Center P Forward\n<\/span><\/span><\/strong><\/pre>\n
 \u0644\u0627\u062d\u0638 \u0623\u0646 \u0643\u0644 \u0635\u0641 \u0641\u064a DataFrame \u0627\u0644\u0646\u0627\u062a\u062c \u064a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 “P Guard” \u0623\u0648 “Center” \u0641\u064a \u0639\u0645\u0648\u062f \u0648\u0627\u062d\u062f \u0639\u0644\u0649 \u0627\u0644\u0623\u0642\u0644.<\/span><\/p>\n
 \u0645\u0644\u062d\u0648\u0638\u0629<\/strong> : \u0645\u0646 \u0627\u0644\u0645\u0647\u0645 \u062a\u0636\u0645\u064a\u0646 \u0627\u0644\u0648\u0633\u064a\u0637\u0629 na=False<\/strong> \u0641\u064a \u0627\u0644\u062f\u0627\u0644\u0629 contain()<\/strong> \u0648\u0625\u0644\u0627 \u0641\u0633\u0648\u0641 \u062a\u0648\u0627\u062c\u0647 \u062e\u0637\u0623\u064b<\/a> \u0641\u064a \u062d\u0627\u0644\u0629 \u0648\u062c\u0648\u062f \u0642\u064a\u0645 NaN \u0641\u064a DataFrame.<\/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 \u0625\u062c\u0631\u0627\u0621 \u0639\u0645\u0644\u064a\u0627\u062a \u0627\u0644\u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u0634\u0627\u0626\u0639\u0629 \u0627\u0644\u0623\u062e\u0631\u0649 \u0641\u064a \u0627\u0644\u0628\u0627\u0646\u062f\u0627:<\/span><\/p>\n
 \u0643\u064a\u0641\u064a\u0629 \u062a\u0635\u0641\u064a\u0629 Pandas DataFrame \u062d\u0633\u0628 \u0642\u064a\u0645 \u0627\u0644\u0623\u0639\u0645\u062f\u0629
 \u0643\u064a\u0641\u064a\u0629 \u062a\u0635\u0641\u064a\u0629 \u0635\u0641\u0648\u0641 Pandas DataFrame \u062d\u0633\u0628 \u0627\u0644\u062a\u0627\u0631\u064a\u062e
 \u0643\u064a\u0641\u064a\u0629 \u062a\u0635\u0641\u064a\u0629 Pandas DataFrame \u0628\u0634\u0631\u0648\u0637 \u0645\u062a\u0639\u062f\u062f\u0629<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u064a\u0645\u0643\u0646\u0643 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0628\u0646\u0627\u0621 \u0627\u0644\u062c\u0645\u0644\u0629 \u0627\u0644\u062a\u0627\u0644\u064a \u0644\u0644\u0628\u062d\u062b \u0639\u0646 \u0633\u0644\u0633\u0644\u0629 \u0645\u0639\u064a\u0646\u0629 \u0641\u064a \u0643\u0644 \u0639\u0645\u0648\u062f \u0645\u0646 \u0627\u0644\u0628\u0627\u0646\u062f\u0627 DataFrame \u0648\u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u0635\u0641\u0648\u0641 \u0627\u0644\u062a\u064a \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0627\u0644\u0633\u0644\u0633\u0644\u0629 \u0641\u064a \u0639\u0645\u0648\u062f \u0648\u0627\u062d\u062f \u0639\u0644\u0649 \u0627\u0644\u0623\u0642\u0644: #define filter mask = np. column_stack ([df[col]. str . contains (r ” my_string “, na= False ) for col in df]) #filter for rows where any column contains ‘my_string’ […]<\/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\u0627\u0644\u0628\u0627\u0646\u062f\u0627: \u0627\u0628\u062d\u062b \u0639\u0646 \u0633\u0644\u0633\u0644\u0629 \u0641\u064a \u0643\u0627\u0641\u0629 \u0623\u0639\u0645\u062f\u0629 DataFrame - 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