{"id":3780,"date":"2023-07-15T14:29:24","date_gmt":"2023-07-15T14:29:24","guid":{"rendered":"https:\/\/statorials.org\/th\/%e0%b8%95%e0%b8%b2%e0%b8%a3%e0%b8%b2%e0%b8%87%e0%b9%80%e0%b8%94%e0%b8%b7%e0%b8%ad%e0%b8%a2%e0%b9%81%e0%b8%9e%e0%b8%99%e0%b8%94%e0%b9%89%e0%b8%b2%e0%b8%a5%e0%b8%9a-multiindex\/"},"modified":"2023-07-15T14:29:24","modified_gmt":"2023-07-15T14:29:24","slug":"%e0%b8%95%e0%b8%b2%e0%b8%a3%e0%b8%b2%e0%b8%87%e0%b9%80%e0%b8%94%e0%b8%b7%e0%b8%ad%e0%b8%a2%e0%b9%81%e0%b8%9e%e0%b8%99%e0%b8%94%e0%b9%89%e0%b8%b2%e0%b8%a5%e0%b8%9a-multiindex","status":"publish","type":"post","link":"https:\/\/statorials.org\/th\/%e0%b8%95%e0%b8%b2%e0%b8%a3%e0%b8%b2%e0%b8%87%e0%b9%80%e0%b8%94%e0%b8%b7%e0%b8%ad%e0%b8%a2%e0%b9%81%e0%b8%9e%e0%b8%99%e0%b8%94%e0%b9%89%e0%b8%b2%e0%b8%a5%e0%b8%9a-multiindex\/","title":{"rendered":"Pandas: \u0e27\u0e34\u0e18\u0e35\u0e25\u0e1a multiindex \u0e43\u0e19\u0e15\u0e32\u0e23\u0e32\u0e07\u0e40\u0e14\u0e37\u0e2d\u0e22"},"content":{"rendered":"

<\/p>\n

\n

\u0e2b\u0e32\u0e01\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e25\u0e1a multiIndex \u0e2d\u0e2d\u0e01\u0e08\u0e32\u0e01\u0e15\u0e32\u0e23\u0e32\u0e07 Pivot \u0e02\u0e2d\u0e07 pandas \u0e04\u0e38\u0e13\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e43\u0e0a\u0e49\u0e2d\u0e32\u0e23\u0e4c\u0e01\u0e34\u0e27\u0e40\u0e21\u0e19\u0e15\u0e4c \u0e04\u0e48\u0e32<\/strong> \u0e01\u0e31\u0e1a\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19 reset_index()<\/strong> \u0e44\u0e14\u0e49:<\/span><\/p>\n

 p.d. pivot_table<\/span> (df, index=' col1<\/span> ', columns=' col2<\/span> ', values=' col3<\/span> '). reset_index<\/span> ()\n<\/strong><\/pre>\n
 \u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49\u0e41\u0e2a\u0e14\u0e07\u0e27\u0e34\u0e18\u0e35\u0e43\u0e0a\u0e49\u0e44\u0e27\u0e22\u0e32\u0e01\u0e23\u0e13\u0e4c\u0e19\u0e35\u0e49\u0e43\u0e19\u0e17\u0e32\u0e07\u0e1b\u0e0f\u0e34\u0e1a\u0e31\u0e15\u0e34<\/span><\/p>\n
 \u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07: \u0e25\u0e1a MultiIndex \u0e43\u0e19 Pandas PivotTable<\/strong><\/span><\/h2>\n
 \u0e2a\u0e21\u0e21\u0e15\u0e34\u0e27\u0e48\u0e32\u0e40\u0e23\u0e32\u0e21\u0e35 DataFrame \u0e41\u0e1e\u0e19\u0e14\u0e49\u0e32\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49\u0e0b\u0e36\u0e48\u0e07\u0e21\u0e35\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e40\u0e01\u0e35\u0e48\u0e22\u0e27\u0e01\u0e31\u0e1a\u0e1c\u0e39\u0e49\u0e40\u0e25\u0e48\u0e19\u0e1a\u0e32\u0e2a\u0e40\u0e01\u0e15\u0e1a\u0e2d\u0e25\u0e15\u0e48\u0e32\u0e07\u0e46:<\/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                   ' position<\/span> ': ['G', 'G', 'F', 'F', 'G', 'F', 'F', 'F'],\n                   ' points<\/span> ': [4, 4, 6, 8, 9, 5, 5, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team position points\n0 AG 4\n1 GA 4\n2 AF 6\n3AF 8\n4 BG 9\n5 BF 5\n6 BF 5\n7 BF 12<\/strong><\/pre>\n
 \u0e15\u0e2d\u0e19\u0e19\u0e35\u0e49 \u0e2a\u0e21\u0e21\u0e15\u0e34\u0e27\u0e48\u0e32\u0e40\u0e23\u0e32\u0e2a\u0e23\u0e49\u0e32\u0e07\u0e15\u0e32\u0e23\u0e32\u0e07\u0e2a\u0e23\u0e38\u0e1b\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e2a\u0e23\u0e38\u0e1b\u0e04\u0e48\u0e32 \u0e04\u0e30\u0e41\u0e19\u0e19<\/strong> \u0e40\u0e09\u0e25\u0e35\u0e48\u0e22\u0e15\u0e32\u0e21 \u0e17\u0e35\u0e21<\/strong> \u0e41\u0e25\u0e30 \u0e15\u0e33\u0e41\u0e2b\u0e19\u0e48\u0e07<\/strong> :<\/span><\/p>\n
 #create pivot table to summarize mean points by team and position\n<\/span>p.d. pivot_table<\/span> (df, index=' team<\/span> ', columns=' position<\/span> ')\n\n\t        points\nFG position\nteam\t\t\nAt 7.000000 4.0\nB 7.333333 9.0\n<\/span><\/span><\/strong><\/pre>\n
 \u0e15\u0e32\u0e23\u0e32\u0e07\u0e2a\u0e23\u0e38\u0e1b\u0e1c\u0e25\u0e25\u0e31\u0e1e\u0e18\u0e4c\u0e08\u0e30\u0e2a\u0e23\u0e38\u0e1b\u0e04\u0e48\u0e32 \u0e04\u0e30\u0e41\u0e19\u0e19<\/strong> \u0e40\u0e09\u0e25\u0e35\u0e48\u0e22\u0e15\u0e32\u0e21 \u0e17\u0e35\u0e21<\/strong> \u0e41\u0e25\u0e30 \u0e15\u0e33\u0e41\u0e2b\u0e19\u0e48\u0e07<\/strong> \u0e41\u0e15\u0e48\u0e21\u0e35 multiIndex<\/span><\/p>\n
 \u0e2b\u0e32\u0e01\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e25\u0e1a multiIndex \u0e40\u0e23\u0e32\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e43\u0e0a\u0e49\u0e2d\u0e32\u0e23\u0e4c\u0e01\u0e34\u0e27\u0e40\u0e21\u0e19\u0e15\u0e4c \u0e04\u0e48\u0e32<\/strong> \u0e43\u0e19\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19 pivot_table()<\/strong> \u0e41\u0e25\u0e30\u0e40\u0e1e\u0e34\u0e48\u0e21 reset_index()<\/strong> \u0e17\u0e35\u0e48\u0e2a\u0e48\u0e27\u0e19\u0e17\u0e49\u0e32\u0e22:<\/span><\/p>\n
 #create pivot table to summarize mean points by team and position\n<\/span>p.d. pivot_table<\/span> (df, index=' team<\/span> ', columns=' position<\/span> ', values=' points<\/span> '). reset_index<\/span> ()\n\nposition team F G\n0 to 7.000000 4.0\n1 B 7.333333 9.0\n<\/span><\/span><\/strong><\/pre>\n
 \u0e15\u0e32\u0e23\u0e32\u0e07\u0e2a\u0e23\u0e38\u0e1b\u0e1c\u0e25\u0e25\u0e31\u0e1e\u0e18\u0e4c\u0e08\u0e30\u0e2a\u0e23\u0e38\u0e1b\u0e04\u0e48\u0e32 \u0e04\u0e30\u0e41\u0e19\u0e19<\/strong> \u0e40\u0e09\u0e25\u0e35\u0e48\u0e22\u0e15\u0e32\u0e21 \u0e17\u0e35\u0e21<\/strong> \u0e41\u0e25\u0e30 \u0e15\u0e33\u0e41\u0e2b\u0e19\u0e48\u0e07<\/strong> \u0e41\u0e25\u0e30\u0e44\u0e21\u0e48\u0e21\u0e35 multiIndex \u0e2d\u0e35\u0e01\u0e15\u0e48\u0e2d\u0e44\u0e1b<\/span><\/span><\/p>\n
 \u0e42\u0e1b\u0e23\u0e14\u0e17\u0e23\u0e32\u0e1a\u0e27\u0e48\u0e32\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19 pivot_table()<\/strong> \u0e08\u0e30\u0e04\u0e33\u0e19\u0e27\u0e13\u0e04\u0e48\u0e32\u0e40\u0e09\u0e25\u0e35\u0e48\u0e22\u0e15\u0e32\u0e21\u0e04\u0e48\u0e32\u0e40\u0e23\u0e34\u0e48\u0e21\u0e15\u0e49\u0e19<\/span><\/p>\n
 \u0e2b\u0e32\u0e01\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e04\u0e33\u0e19\u0e27\u0e13\u0e2b\u0e19\u0e48\u0e27\u0e22\u0e27\u0e31\u0e14\u0e2d\u0e37\u0e48\u0e19 \u0e40\u0e0a\u0e48\u0e19 \u0e1c\u0e25\u0e23\u0e27\u0e21 \u0e43\u0e2b\u0e49\u0e43\u0e0a\u0e49\u0e2d\u0e32\u0e23\u0e4c\u0e01\u0e34\u0e27\u0e40\u0e21\u0e19\u0e15\u0e4c aggfunc<\/strong> \u0e14\u0e31\u0e07\u0e19\u0e35\u0e49<\/span><\/p>\n
 #create pivot table to summarize sum of points by team and position\n<\/span>p.d. pivot_table<\/span> (df, index=' team<\/span> ', columns=' position<\/span> ', values=' points<\/span> ',\n               aggfunc=' sum<\/span> '). reset_index<\/span> ()\n\nposition team FG\n0 to 14 8\n1 B 22 9<\/span><\/span><\/strong><\/pre>\n
 \u0e15\u0e32\u0e23\u0e32\u0e07\u0e40\u0e14\u0e37\u0e2d\u0e22\u0e17\u0e35\u0e48\u0e44\u0e14\u0e49\u0e08\u0e30\u0e2a\u0e23\u0e38\u0e1b\u0e1c\u0e25\u0e23\u0e27\u0e21\u0e02\u0e2d\u0e07\u0e04\u0e48\u0e32 \u0e04\u0e30\u0e41\u0e19\u0e19<\/strong> \u0e15\u0e32\u0e21 \u0e17\u0e35\u0e21<\/strong> \u0e41\u0e25\u0e30 \u0e15\u0e33\u0e41\u0e2b\u0e19\u0e48\u0e07<\/strong> \u0e41\u0e25\u0e30\u0e22\u0e31\u0e07\u0e44\u0e21\u0e48\u0e21\u0e35 multiIndex<\/span><\/p>\n
 \u0e2b\u0e21\u0e32\u0e22\u0e40\u0e2b\u0e15\u0e38<\/strong> : \u0e04\u0e38\u0e13\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e14\u0e39\u0e40\u0e2d\u0e01\u0e2a\u0e32\u0e23\u0e09\u0e1a\u0e31\u0e1a\u0e40\u0e15\u0e47\u0e21\u0e02\u0e2d\u0e07\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19 pandas pivot_table()<\/strong> \u0e44\u0e14\u0e49\u0e17\u0e35\u0e48\u0e19\u0e35\u0e48<\/a><\/span><\/p>\n
 \u0e41\u0e2b\u0e25\u0e48\u0e07\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e40\u0e1e\u0e34\u0e48\u0e21\u0e40\u0e15\u0e34\u0e21<\/strong><\/span><\/h2>\n
 \u0e1a\u0e17\u0e0a\u0e48\u0e27\u0e22\u0e2a\u0e2d\u0e19\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49\u0e08\u0e30\u0e2d\u0e18\u0e34\u0e1a\u0e32\u0e22\u0e27\u0e34\u0e18\u0e35\u0e14\u0e33\u0e40\u0e19\u0e34\u0e19\u0e01\u0e32\u0e23\u0e17\u0e31\u0e48\u0e27\u0e44\u0e1b\u0e2d\u0e37\u0e48\u0e19 \u0e46 \u0e43\u0e19\u0e41\u0e1e\u0e19\u0e14\u0e49\u0e32:<\/span><\/p>\n
 Pandas: \u0e27\u0e34\u0e18\u0e35\u0e40\u0e1e\u0e34\u0e48\u0e21\u0e15\u0e31\u0e27\u0e01\u0e23\u0e2d\u0e07\u0e25\u0e07\u0e43\u0e19\u0e15\u0e32\u0e23\u0e32\u0e07\u0e2a\u0e32\u0e23\u0e30\u0e2a\u0e33\u0e04\u0e31\u0e0d<\/a>
 Pandas: \u0e27\u0e34\u0e18\u0e35\u0e08\u0e31\u0e14\u0e40\u0e23\u0e35\u0e22\u0e07\u0e15\u0e32\u0e23\u0e32\u0e07\u0e40\u0e14\u0e37\u0e2d\u0e22\u0e15\u0e32\u0e21\u0e04\u0e48\u0e32\u0e43\u0e19\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c<\/a>
 Pandas: \u0e27\u0e34\u0e18\u0e35\u0e40\u0e1e\u0e34\u0e48\u0e21\u0e1c\u0e25\u0e23\u0e27\u0e21\u0e22\u0e48\u0e2d\u0e22\u0e25\u0e07\u0e43\u0e19\u0e15\u0e32\u0e23\u0e32\u0e07\u0e2a\u0e23\u0e38\u0e1b<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0e2b\u0e32\u0e01\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e25\u0e1a multiIndex \u0e2d\u0e2d\u0e01\u0e08\u0e32\u0e01\u0e15\u0e32\u0e23\u0e32\u0e07 Pivot \u0e02\u0e2d\u0e07 pandas \u0e04\u0e38 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-3780","post","type-post","status-publish","format-standard","hentry","category-3"],"yoast_head":"\nPandas: \u0e27\u0e34\u0e18\u0e35\u0e25\u0e1a MultiIndex \u0e43\u0e19\u0e15\u0e32\u0e23\u0e32\u0e07 Pivot - \u0e2a\u0e16\u0e34\u0e15\u0e34<\/title>\n<meta name=\"description\" content=\"\u0e1a\u0e17\u0e0a\u0e48\u0e27\u0e22\u0e2a\u0e2d\u0e19\u0e19\u0e35\u0e49\u0e08\u0e30\u0e2d\u0e18\u0e34\u0e1a\u0e32\u0e22\u0e27\u0e34\u0e18\u0e35\u0e25\u0e1a multiIndex \u0e2d\u0e2d\u0e01\u0e08\u0e32\u0e01\u0e15\u0e32\u0e23\u0e32\u0e07 Pivot \u0e02\u0e2d\u0e07 pandas 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