{"id":2094,"date":"2023-07-23T17:39:42","date_gmt":"2023-07-23T17:39:42","guid":{"rendered":"https:\/\/statorials.org\/th\/%e0%b9%81%e0%b8%9e%e0%b8%99%e0%b8%94%e0%b9%89%e0%b8%b2%e0%b8%aa%e0%b8%a1%e0%b8%b1%e0%b8%84%e0%b8%a3%e0%b8%9a%e0%b8%99%e0%b9%80%e0%b8%a7%e0%b9%87%e0%b8%9a%e0%b9%84%e0%b8%8b%e0%b8%95%e0%b9%8c\/"},"modified":"2023-07-23T17:39:42","modified_gmt":"2023-07-23T17:39:42","slug":"%e0%b9%81%e0%b8%9e%e0%b8%99%e0%b8%94%e0%b9%89%e0%b8%b2%e0%b8%aa%e0%b8%a1%e0%b8%b1%e0%b8%84%e0%b8%a3%e0%b8%9a%e0%b8%99%e0%b9%80%e0%b8%a7%e0%b9%87%e0%b8%9a%e0%b9%84%e0%b8%8b%e0%b8%95%e0%b9%8c","status":"publish","type":"post","link":"https:\/\/statorials.org\/th\/%e0%b9%81%e0%b8%9e%e0%b8%99%e0%b8%94%e0%b9%89%e0%b8%b2%e0%b8%aa%e0%b8%a1%e0%b8%b1%e0%b8%84%e0%b8%a3%e0%b8%9a%e0%b8%99%e0%b9%80%e0%b8%a7%e0%b9%87%e0%b8%9a%e0%b9%84%e0%b8%8b%e0%b8%95%e0%b9%8c\/","title":{"rendered":"\u0e27\u0e34\u0e18\u0e35\u0e43\u0e0a\u0e49 pandas apply()"},"content":{"rendered":"

<\/p>\n

\n

\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19 pandas Apply()<\/a> \u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e43\u0e0a\u0e49\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e19\u0e33\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19\u0e44\u0e1b\u0e43\u0e0a\u0e49\u0e01\u0e31\u0e1a\u0e41\u0e16\u0e27\u0e2b\u0e23\u0e37\u0e2d\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c\u0e02\u0e2d\u0e07 Pandas DataFrame<\/span><\/p>\n

\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19\u0e19\u0e35\u0e49\u0e41\u0e15\u0e01\u0e15\u0e48\u0e32\u0e07\u0e08\u0e32\u0e01\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19\u0e2d\u0e37\u0e48\u0e19\u0e46 \u0e40\u0e0a\u0e48\u0e19 drop()<\/strong> \u0e41\u0e25\u0e30 \u0e41\u0e17\u0e19\u0e17\u0e35\u0e48()<\/strong> \u0e0b\u0e36\u0e48\u0e07\u0e21\u0e35\u0e2d\u0e32\u0e23\u0e4c\u0e01\u0e34\u0e27\u0e40\u0e21\u0e19\u0e15\u0e4c\u0e41\u0e1a\u0e1a\u0e41\u0e17\u0e19\u0e17\u0e35\u0e48:<\/span><\/p>\n

 df. drop<\/span> ([' column1<\/span> '], inplace= True<\/span> )\n\ndf. rename<\/span> ({' old_column<\/span> ': ' new_column<\/span> '}, inplace= True<\/span> )\n<\/strong><\/pre>\n
 \u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19 Apply()<\/strong> \u0e44\u0e21\u0e48\u0e21\u0e35\u0e2d\u0e32\u0e23\u0e4c\u0e01\u0e34\u0e27\u0e40\u0e21\u0e19\u0e15\u0e4c inplace \u0e14\u0e31\u0e07\u0e19\u0e31\u0e49\u0e19\u0e40\u0e23\u0e32\u0e15\u0e49\u0e2d\u0e07\u0e43\u0e0a\u0e49\u0e44\u0e27\u0e22\u0e32\u0e01\u0e23\u0e13\u0e4c\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e41\u0e1b\u0e25\u0e07 DataFrame \u0e41\u0e1a\u0e1a inplace:<\/span><\/p>\n
 df = df. apply<\/span> ( lambda<\/span> x: x* 2<\/span> )<\/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\u0e01\u0e32\u0e23\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\u0e01\u0e31\u0e1a Pandas DataFrame \u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\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>df\n\n        points assists rebounds\n0 25 5 11\n1 12 7 8\n2 15 7 10\n3 14 9 6\n4 19 12 6\n5 23 9 5\n6 25 9 9\n7 29 4 12\n<\/strong><\/pre>\n
 \u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e17\u0e35\u0e48 1: \u0e43\u0e0a\u0e49 Apply() \u0e41\u0e17\u0e19\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c<\/strong><\/span><\/h3>\n
 \u0e23\u0e2b\u0e31\u0e2a\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49\u0e2a\u0e32\u0e18\u0e34\u0e15\u0e27\u0e34\u0e18\u0e35\u0e01\u0e32\u0e23\u0e43\u0e0a\u0e49 Apply()<\/strong> \u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e41\u0e1b\u0e25\u0e07\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c\u0e01\u0e23\u0e2d\u0e1a\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e43\u0e19\u0e2a\u0e16\u0e32\u0e19\u0e17\u0e35\u0e48:<\/span><\/p>\n
 #multiply all values in 'points' column by 2 in place\n<\/span>df. loc<\/span> [:, ' points<\/span> '] = df. points<\/span> . apply<\/span> ( lambda<\/span> x: x* 2<\/span> )\n\n#view updated DataFrame<\/span>\ndf\n\npoints assists rebounds\n0 50 5 11\n1 24 7 8\n2 30 7 10\n3 28 9 6\n4 38 12 6\n5 46 9 5\n6 50 9 9\n7 58 4 12\n<\/strong><\/pre>\n
 \u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e17\u0e35\u0e48 2: \u0e43\u0e0a\u0e49 Apply() \u0e41\u0e17\u0e19\u0e2b\u0e25\u0e32\u0e22\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c<\/strong><\/span><\/h3>\n
 \u0e23\u0e2b\u0e31\u0e2a\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49\u0e2a\u0e32\u0e18\u0e34\u0e15\u0e27\u0e34\u0e18\u0e35\u0e01\u0e32\u0e23\u0e43\u0e0a\u0e49 Apply()<\/strong> \u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e41\u0e1b\u0e25\u0e07\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c\u0e01\u0e23\u0e2d\u0e1a\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e2b\u0e25\u0e32\u0e22\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c\u0e43\u0e19\u0e2a\u0e16\u0e32\u0e19\u0e17\u0e35\u0e48:<\/span><\/p>\n
 multiply all values in 'points' and 'rebounds' column by 2 in place<\/span>\ndf[[' points<\/span> ', ' rebounds<\/span> ']] = df[[' points<\/span> ', ' rebounds<\/span> ']]. apply<\/span> ( lambda<\/span> x: x* 2<\/span> )\n\n#view updated DataFrame\n<\/span>df\n\n\tpoints assists rebounds\n0 50 5 22\n1 24 7 16\n2 30 7 20\n3 28 9 12\n4 38 12 12\n5 46 9 10\n6 50 9 18\n7 58 4 24\n<\/strong><\/pre>\n
 \u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e17\u0e35\u0e48 3: \u0e43\u0e0a\u0e49 Apply() \u0e41\u0e17\u0e19\u0e17\u0e38\u0e01\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c<\/strong><\/span><\/h3>\n
 \u0e23\u0e2b\u0e31\u0e2a\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49\u0e41\u0e2a\u0e14\u0e07\u0e27\u0e34\u0e18\u0e35\u0e43\u0e0a\u0e49 Apply()<\/strong> \u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e41\u0e1b\u0e25\u0e07\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c\u0e17\u0e31\u0e49\u0e07\u0e2b\u0e21\u0e14\u0e43\u0e19\u0e01\u0e23\u0e2d\u0e1a\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25:<\/span><\/p>\n
 #multiply values in all columns by 2\n<\/span>df = df. apply<\/span> ( lambda<\/span> x: x* 2<\/span> )\n\n#view updated DataFrame<\/span>\ndf\n\n\tpoints assists rebounds\n0 50 10 22\n1 24 14 16\n2 30 14 20\n3 28 18 12\n4 38 24 12\n5 46 18 10\n6 50 18 18\n7 58 8 24\n<\/strong><\/pre>\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\u0e01\u0e32\u0e23\u0e43\u0e0a\u0e49\u0e07\u0e32\u0e19\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19\u0e17\u0e31\u0e48\u0e27\u0e44\u0e1b\u0e2d\u0e37\u0e48\u0e19 \u0e46 \u0e43\u0e19\u0e41\u0e1e\u0e19\u0e14\u0e49\u0e32:<\/span><\/p>\n
 \u0e27\u0e34\u0e18\u0e35\u0e04\u0e33\u0e19\u0e27\u0e13\u0e1c\u0e25\u0e23\u0e27\u0e21\u0e02\u0e2d\u0e07\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c\u0e43\u0e19 Pandas<\/a>
 \u0e27\u0e34\u0e18\u0e35\u0e01\u0e32\u0e23\u0e04\u0e33\u0e19\u0e27\u0e13\u0e04\u0e48\u0e32\u0e40\u0e09\u0e25\u0e35\u0e48\u0e22\u0e02\u0e2d\u0e07\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c\u0e43\u0e19 Pandas<\/a>
 \u0e27\u0e34\u0e18\u0e35\u0e04\u0e49\u0e19\u0e2b\u0e32\u0e04\u0e48\u0e32\u0e2a\u0e39\u0e07\u0e2a\u0e38\u0e14\u0e02\u0e2d\u0e07\u0e04\u0e2d\u0e25\u0e31\u0e21\u0e19\u0e4c\u0e43\u0e19 Pandas<\/p>\n","protected":false},"excerpt":{"rendered":"
\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19 pandas Apply() \u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e43\u0e0a\u0e49\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e19\u0e33\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19\u0e44\u0e1b\u0e43\u0e0a\u0e49\u0e01\u0e31 […]<\/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-2094","post","type-post","status-publish","format-standard","hentry","category-3"],"yoast_head":"\n\u0e27\u0e34\u0e18\u0e35\u0e43\u0e0a\u0e49 Pandas Apply() \u0e43\u0e19\u0e2a\u0e16\u0e32\u0e19\u0e17\u0e35\u0e48 \u2013 Statorials<\/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\u0e43\u0e0a\u0e49\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19 pandas Apply() \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\/\u0e41\u0e1e\u0e19\u0e14\u0e49\u0e32\u0e2a\u0e21\u0e31\u0e04\u0e23\u0e1a\u0e19\u0e40\u0e27\u0e47\u0e1a\u0e44\u0e0b\u0e15\u0e4c\/\" \/>\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\u0e43\u0e0a\u0e49 Pandas Apply() \u0e43\u0e19\u0e2a\u0e16\u0e32\u0e19\u0e17\u0e35\u0e48 \u2013 Statorials\" \/>\n<meta property=\"og: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\u0e43\u0e0a\u0e49\u0e1f\u0e31\u0e07\u0e01\u0e4c\u0e0a\u0e31\u0e19 pandas Apply() \u0e23\u0e27\u0e21\u0e16\u0e36\u0e07\u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e15\u0e48\u0e32\u0e07\u0e46\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/th\/\u0e41\u0e1e\u0e19\u0e14\u0e49\u0e32\u0e2a\u0e21\u0e31\u0e04\u0e23\u0e1a\u0e19\u0e40\u0e27\u0e47\u0e1a\u0e44\u0e0b\u0e15\u0e4c\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-23T17:39:42+00:00\" \/>\n<meta name=\"author\" content=\"\u0e14\u0e23.\u0e40\u0e1a\u0e19\u0e08\u0e32\u0e21\u0e34\u0e19 \u0e41\u0e2d\u0e19\u0e40\u0e14\u0e2d\u0e23\u0e4c\u0e2a\u0e31\u0e19\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u0e14\u0e23.\u0e40\u0e1a\u0e19\u0e08\u0e32\u0e21\u0e34\u0e19 \u0e41\u0e2d\u0e19\u0e40\u0e14\u0e2d\u0e23\u0e4c\u0e2a\u0e31\u0e19\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" 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