{"id":973,"date":"2023-07-28T03:22:54","date_gmt":"2023-07-28T03:22:54","guid":{"rendered":"https:\/\/statorials.org\/th\/%e0%b8%aa%e0%b9%81%e0%b8%95%e0%b9%87%e0%b8%81%e0%b8%94%e0%b8%b2%e0%b8%95%e0%b9%89%e0%b8%b2%e0%b9%80%e0%b8%9f%e0%b8%a3%e0%b8%a1%e0%b9%81%e0%b8%9e%e0%b8%99%e0%b8%94%e0%b9%89%e0%b8%b2\/"},"modified":"2023-07-28T03:22:54","modified_gmt":"2023-07-28T03:22:54","slug":"%e0%b8%aa%e0%b9%81%e0%b8%95%e0%b9%87%e0%b8%81%e0%b8%94%e0%b8%b2%e0%b8%95%e0%b9%89%e0%b8%b2%e0%b9%80%e0%b8%9f%e0%b8%a3%e0%b8%a1%e0%b9%81%e0%b8%9e%e0%b8%99%e0%b8%94%e0%b9%89%e0%b8%b2","status":"publish","type":"post","link":"https:\/\/statorials.org\/th\/%e0%b8%aa%e0%b9%81%e0%b8%95%e0%b9%87%e0%b8%81%e0%b8%94%e0%b8%b2%e0%b8%95%e0%b9%89%e0%b8%b2%e0%b9%80%e0%b8%9f%e0%b8%a3%e0%b8%a1%e0%b9%81%e0%b8%9e%e0%b8%99%e0%b8%94%e0%b9%89%e0%b8%b2\/","title":{"rendered":"\u0e27\u0e34\u0e18\u0e35\u0e0b\u0e49\u0e2d\u0e19 pandas dataframes \u0e2b\u0e25\u0e32\u0e22\u0e2d\u0e31\u0e19"},"content":{"rendered":"

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\u0e1a\u0e48\u0e2d\u0e22\u0e04\u0e23\u0e31\u0e49\u0e07\u0e17\u0e35\u0e48\u0e04\u0e38\u0e13\u0e2d\u0e32\u0e08\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e0b\u0e49\u0e2d\u0e19 DataFrames \u0e41\u0e1e\u0e19\u0e14\u0e49\u0e32\u0e2a\u0e2d\u0e07\u0e15\u0e31\u0e27\u0e02\u0e36\u0e49\u0e19\u0e44\u0e1b \u0e42\u0e0a\u0e04\u0e14\u0e35\u0e17\u0e35\u0e48\u0e17\u0e33\u0e44\u0e14\u0e49\u0e07\u0e48\u0e32\u0e22\u0e42\u0e14\u0e22\u0e43\u0e0a\u0e49\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19 pandas concat()<\/a><\/span><\/p>\n

\u0e1a\u0e17\u0e0a\u0e48\u0e27\u0e22\u0e2a\u0e2d\u0e19\u0e19\u0e35\u0e49\u0e41\u0e2a\u0e14\u0e07\u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e27\u0e34\u0e18\u0e35\u0e01\u0e32\u0e23\u0e17\u0e33\u0e40\u0e0a\u0e48\u0e19\u0e19\u0e35\u0e49\u0e2b\u0e25\u0e32\u0e22\u0e1b\u0e23\u0e30\u0e01\u0e32\u0e23<\/span><\/p>\n

\u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e17\u0e35\u0e48 1: \u0e0b\u0e49\u0e2d\u0e19 Pandas DataFrames \u0e2a\u0e2d\u0e07\u0e15\u0e31\u0e27<\/strong><\/span><\/h3>\n

\u0e23\u0e2b\u0e31\u0e2a\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49\u0e41\u0e2a\u0e14\u0e07\u0e27\u0e34\u0e18\u0e35\u0e01\u0e32\u0e23 “\u0e0b\u0e49\u0e2d\u0e19” DataFrame \u0e41\u0e1e\u0e19\u0e14\u0e49\u0e32\u0e2a\u0e2d\u0e07\u0e15\u0e31\u0e27\u0e17\u0e35\u0e48\u0e0b\u0e49\u0e2d\u0e19\u0e01\u0e31\u0e19\u0e41\u0e25\u0e30\u0e2a\u0e23\u0e49\u0e32\u0e07 DataFrame:<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#create two DataFrames<\/span>\ndf1 = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E'],\n                    'points':[12, 5, 13, 17, 27]})\n\ndf2 = pd.DataFrame({'player': ['F', 'G', 'H', 'I', 'J'],\n                    'points':[24, 26, 27, 27, 12]})\n\n#\"stack\" the two DataFrames together<\/span>\ndf3 = pd. concat<\/span> ([df1,df2], ignore_index= True<\/span> )\n\n#view resulting DataFrame<\/span>\ndf3\n\n\tplayer points\n0 to 12\n1 B 5\n2 C 13\n3 D 17\n4 E 27\n5 F 24\n6 G 26\n7:27 a.m.\n8 I 27\n9 D 12\n<\/strong><\/pre>\n
 \u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e17\u0e35\u0e48 2: \u0e0b\u0e49\u0e2d\u0e19 Pandas DataFrames \u0e2a\u0e32\u0e21\u0e15\u0e31\u0e27<\/strong><\/span><\/h3>\n
 \u0e23\u0e2b\u0e31\u0e2a\u0e17\u0e35\u0e48\u0e04\u0e25\u0e49\u0e32\u0e22\u0e01\u0e31\u0e19\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e43\u0e0a\u0e49\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e0b\u0e49\u0e2d\u0e19 DataFrames \u0e41\u0e1e\u0e19\u0e14\u0e49\u0e32\u0e2a\u0e32\u0e21\u0e15\u0e31\u0e27\u0e0b\u0e49\u0e2d\u0e19\u0e17\u0e31\u0e1a\u0e01\u0e31\u0e19\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e2a\u0e23\u0e49\u0e32\u0e07 DataFrame:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#create three DataFrames<\/span>\ndf1 = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E'],\n                    'points':[12, 5, 13, 17, 27]})\n\ndf2 = pd.DataFrame({'player': ['F', 'G', 'H', 'I', 'J'],\n                    'points':[24, 26, 27, 27, 12]})\n\ndf3 = pd.DataFrame({'player': ['K', 'L', 'M', 'N', 'O'],\n                    'points':[9, 5, 5, 13, 17]})\n\n#\"stack\" the two DataFrames together<\/span>\ndf4 = pd. concat<\/span> ([df1,df2, df3], ignore_index= True<\/span> )\n\n#view resulting DataFrame<\/span>\ndf4\n\n        player points\n0 to 12\n1 B 5\n2 C 13\n3 D 17\n4 E 27\n5 F 24\n6 G 26\n7:27 a.m.\n8 I 27\n9 D 12\n10K 9\n11 L 5\n12 M 5\n13 N 13\n14 O 17<\/strong><\/pre>\n
 \u0e04\u0e27\u0e32\u0e21\u0e2a\u0e33\u0e04\u0e31\u0e0d\u0e02\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e25\u0e30\u0e40\u0e27\u0e49\u0e19_\u0e14\u0e31\u0e0a\u0e19\u0e35<\/strong><\/span><\/h3>\n
 \u0e42\u0e1b\u0e23\u0e14\u0e17\u0e23\u0e32\u0e1a\u0e27\u0e48\u0e32\u0e43\u0e19\u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e01\u0e48\u0e2d\u0e19\u0e2b\u0e19\u0e49\u0e32\u0e19\u0e35\u0e49 \u0e40\u0e23\u0e32\u0e43\u0e0a\u0e49 ign_index=True<\/strong><\/span><\/p>\n
 \u0e0b\u0e36\u0e48\u0e07\u0e08\u0e30\u0e1a\u0e2d\u0e01\u0e43\u0e2b\u0e49\u0e41\u0e1e\u0e19\u0e14\u0e49\u0e32\u0e25\u0e30\u0e40\u0e27\u0e49\u0e19\u0e2b\u0e21\u0e32\u0e22\u0e40\u0e25\u0e02\u0e14\u0e31\u0e0a\u0e19\u0e35\u0e43\u0e19\u0e41\u0e15\u0e48\u0e25\u0e30 DataFrame \u0e41\u0e25\u0e30\u0e2a\u0e23\u0e49\u0e32\u0e07\u0e14\u0e31\u0e0a\u0e19\u0e35\u0e43\u0e2b\u0e21\u0e48\u0e15\u0e31\u0e49\u0e07\u0e41\u0e15\u0e48 0 \u0e16\u0e36\u0e07 n-1 \u0e2a\u0e33\u0e2b\u0e23\u0e31\u0e1a DataFrame \u0e43\u0e2b\u0e21\u0e48<\/span><\/p>\n
 \u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e40\u0e0a\u0e48\u0e19 \u0e1e\u0e34\u0e08\u0e32\u0e23\u0e13\u0e32\u0e27\u0e48\u0e32\u0e08\u0e30\u0e40\u0e01\u0e34\u0e14\u0e2d\u0e30\u0e44\u0e23\u0e02\u0e36\u0e49\u0e19\u0e40\u0e21\u0e37\u0e48\u0e2d\u0e40\u0e23\u0e32\u0e44\u0e21\u0e48\u0e43\u0e0a\u0e49 Continue_index=True<\/strong> \u0e40\u0e21\u0e37\u0e48\u0e2d\u0e0b\u0e49\u0e2d\u0e19 DataFrames \u0e2a\u0e2d\u0e07\u0e2d\u0e31\u0e19\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#create two DataFrames with indices<\/span>\ndf1 = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E'],\n                    'points':[12, 5, 13, 17, 27]},\n                    index=[0, 1, 2, 3, 4])\n\ndf2 = pd.DataFrame({'player': ['F', 'G', 'H', 'I', 'J'],\n                    'points':[24, 26, 27, 27, 12]},\n                    index=[2, 4, 5, 6, 9])\n\n#stack the two DataFrames together<\/span>\ndf3 = pd. concat<\/span> ([df1,df2])\n\n#view resulting DataFrame<\/span>\ndf3\n\n        player points\n0 to 12\n1 B 5\n2 C 13\n3 D 17\n4 E 27\n2 F 24\n4G 26\n5:27 a.m.\n6 I 27\n9 D 12\n<\/strong><\/pre>\n
 DataFrame \u0e17\u0e35\u0e48\u0e40\u0e1b\u0e47\u0e19\u0e1c\u0e25\u0e25\u0e31\u0e1e\u0e18\u0e4c\u0e08\u0e30\u0e04\u0e07\u0e04\u0e48\u0e32\u0e14\u0e31\u0e0a\u0e19\u0e35\u0e14\u0e31\u0e49\u0e07\u0e40\u0e14\u0e34\u0e21\u0e44\u0e27\u0e49\u0e08\u0e32\u0e01 DataFrames \u0e17\u0e31\u0e49\u0e07\u0e2a\u0e2d\u0e07<\/span><\/p>\n
 \u0e14\u0e31\u0e07\u0e19\u0e31\u0e49\u0e19\u0e42\u0e14\u0e22\u0e17\u0e31\u0e48\u0e27\u0e44\u0e1b\u0e04\u0e38\u0e13\u0e04\u0e27\u0e23\u0e43\u0e0a\u0e49 ign_index=True<\/strong> \u0e40\u0e21\u0e37\u0e48\u0e2d\u0e0b\u0e49\u0e2d\u0e19 DataFrames \u0e2a\u0e2d\u0e07\u0e2d\u0e31\u0e19\u0e40\u0e02\u0e49\u0e32\u0e14\u0e49\u0e27\u0e22\u0e01\u0e31\u0e19 \u0e40\u0e27\u0e49\u0e19\u0e41\u0e15\u0e48\u0e04\u0e38\u0e13\u0e08\u0e30\u0e21\u0e35\u0e40\u0e2b\u0e15\u0e38\u0e1c\u0e25\u0e40\u0e09\u0e1e\u0e32\u0e30\u0e17\u0e35\u0e48\u0e08\u0e30\u0e40\u0e01\u0e47\u0e1a\u0e04\u0e48\u0e32\u0e14\u0e31\u0e0a\u0e19\u0e35\u0e14\u0e31\u0e49\u0e07\u0e40\u0e14\u0e34\u0e21\u0e44\u0e27\u0e49<\/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><\/h3>\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\u0e07\u0e32\u0e19\u0e17\u0e31\u0e48\u0e27\u0e44\u0e1b\u0e2d\u0e37\u0e48\u0e19 \u0e46 \u0e43\u0e19 Pandas:<\/span><\/p>\n
 \u0e27\u0e34\u0e18\u0e35\u0e40\u0e1e\u0e34\u0e48\u0e21\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c\u0e27\u0e48\u0e32\u0e07\u0e43\u0e2b\u0e49\u0e01\u0e31\u0e1a Pandas DataFrame<\/a>
 \u0e27\u0e34\u0e18\u0e35\u0e41\u0e17\u0e23\u0e01\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c\u0e25\u0e07\u0e43\u0e19 Pandas DataFrame<\/a>
 \u0e27\u0e34\u0e18\u0e35\u0e2a\u0e48\u0e07\u0e2d\u0e2d\u0e01 Pandas DataFrame \u0e44\u0e1b\u0e22\u0e31\u0e07 Excel<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0e1a\u0e48\u0e2d\u0e22\u0e04\u0e23\u0e31\u0e49\u0e07\u0e17\u0e35\u0e48\u0e04\u0e38\u0e13\u0e2d\u0e32\u0e08\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e0b\u0e49\u0e2d\u0e19 DataFrames \u0e41\u0e1e\u0e19\u0e14\u0e49\u0e32\u0e2a\u0e2d\u0e07\u0e15\u0e31\u0e27\u0e02\u0e36 […]<\/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-973","post","type-post","status-publish","format-standard","hentry","category-3"],"yoast_head":"\n\u0e27\u0e34\u0e18\u0e35\u0e0b\u0e49\u0e2d\u0e19 DataFrames \u0e02\u0e2d\u0e07 Pandas \u0e2b\u0e25\u0e32\u0e22\u0e2d\u0e31\u0e19 \u2013 \u0e2a\u0e16\u0e34\u0e15\u0e34<\/title>\n<meta name=\"description\" content=\"\u0e04\u0e33\u0e2d\u0e18\u0e34\u0e1a\u0e32\u0e22\u0e07\u0e48\u0e32\u0e22\u0e46 \u0e40\u0e01\u0e35\u0e48\u0e22\u0e27\u0e01\u0e31\u0e1a\u0e27\u0e34\u0e18\u0e35\u0e0b\u0e49\u0e2d\u0e19 DataFrames \u0e02\u0e2d\u0e07\u0e41\u0e1e\u0e19\u0e14\u0e49\u0e32\u0e15\u0e31\u0e49\u0e07\u0e41\u0e15\u0e48\u0e2a\u0e2d\u0e07\u0e15\u0e31\u0e27\u0e02\u0e36\u0e49\u0e19\u0e44\u0e1b \u0e23\u0e27\u0e21\u0e16\u0e36\u0e07\u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e15\u0e48\u0e32\u0e07\u0e46\" \/>\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\/th\/\u0e2a\u0e41\u0e15\u0e47\u0e01\u0e14\u0e32\u0e15\u0e49\u0e32\u0e40\u0e1f\u0e23\u0e21\u0e41\u0e1e\u0e19\u0e14\u0e49\u0e32\/\" \/>\n<meta property=\"og:locale\" content=\"th_TH\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u0e27\u0e34\u0e18\u0e35\u0e0b\u0e49\u0e2d\u0e19 DataFrames \u0e02\u0e2d\u0e07 Pandas \u0e2b\u0e25\u0e32\u0e22\u0e2d\u0e31\u0e19 \u2013 \u0e2a\u0e16\u0e34\u0e15\u0e34\" \/>\n<meta property=\"og:description\" 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