{"id":1733,"date":"2023-07-25T05:11:30","date_gmt":"2023-07-25T05:11:30","guid":{"rendered":"https:\/\/statorials.org\/cn\/pandas-%e6%8c%89%e7%b4%a2%e5%bc%95%e5%88%a0%e9%99%a4%e5%88%97\/"},"modified":"2023-07-25T05:11:30","modified_gmt":"2023-07-25T05:11:30","slug":"pandas-%e6%8c%89%e7%b4%a2%e5%bc%95%e5%88%a0%e9%99%a4%e5%88%97","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/pandas-%e6%8c%89%e7%b4%a2%e5%bc%95%e5%88%a0%e9%99%a4%e5%88%97\/","title":{"rendered":"\u5982\u4f55\u5728 pandas \u4e2d\u6309\u7d22\u5f15\u5220\u9664\u5217"},"content":{"rendered":"

<\/p>\n

\n

\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6309\u7d22\u5f15\u53f7\u4ece pandas DataFrame \u4e2d\u5220\u9664\u5217\uff1a<\/span><\/p>\n

 #drop first column from DataFrame<\/span>\ndf. drop<\/span> ( df.columns<\/span> [0], axis= 1<\/span> , inplace= True<\/span> )\n<\/strong><\/pre>\n
\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6309\u7d22\u5f15\u53f7\u4ece pandas DataFrame \u4e2d\u5220\u9664\u591a\u5217\uff1a<\/span><\/p>\n
 #drop first, second, and fourth column from DataFrame\n<\/span>cols = [0, 1, 3]\ndf. drop<\/span> (df. columns<\/span> [cols], axis= 1<\/span> , inplace= True<\/span> )<\/strong><\/pre>\n
\u5982\u679c\u60a8\u7684 DataFrame \u5177\u6709\u91cd\u590d\u7684\u5217\u540d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6309\u7d22\u5f15\u53f7\u5220\u9664\u5217\uff1a<\/span><\/p>\n
 #define list of columns\n<\/span>cols = [x for<\/span> x in<\/span> range( df.shape<\/span> [1])]\n\n#drop second column\n<\/span>collars. remove<\/span> (1)\n\n#view resulting DataFrame\n<\/span>df. iloc<\/span> [:, cols]<\/strong><\/pre>\n
\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u6309\u7d22\u5f15\u5220\u9664\u5217\u3002<\/span><\/p>\n
\u793a\u4f8b1\uff1a\u901a\u8fc7\u7d22\u5f15\u5220\u9664\u5217<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u4ece pandas DataFrame \u4e2d\u5220\u9664\u7b2c\u4e00\u5217\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd<\/span>\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],\n                   ' first<\/span> ': ['Dirk', 'Kobe', 'Tim', 'Lebron'],\n                   ' last<\/span> ': ['Nowitzki', 'Bryant', 'Duncan', 'James'],\n                   ' points<\/span> ': [26, 31, 22, 29]})<\/span>\n\n#drop first column from DataFrame<\/span>\ndf. drop<\/span> ( df.columns<\/span> [0], axis= 1<\/span> , inplace= True<\/span> )\n\n#view resulting dataFrame<\/span>\ndf\n\n        first last points\n0 Dirk Nowitzki 26\n1 Kobe Bryant 31\n2 Tim Duncan 22\n3 LeBron James 29\n<\/strong><\/pre>\n
\u793a\u4f8b 2\uff1a\u6309\u7d22\u5f15\u5220\u9664\u591a\u5217<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u6309\u7d22\u5f15\u5220\u9664 pandas DataFrame \u4e2d\u7684\u591a\u5217\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd<\/span>\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],\n                   ' first<\/span> ': ['Dirk', 'Kobe', 'Tim', 'Lebron'],\n                   ' last<\/span> ': ['Nowitzki', 'Bryant', 'Duncan', 'James'],\n                   ' points<\/span> ': [26, 31, 22, 29]})<\/span>\n\n#drop first, second and fourth columns from DataFrame\n<\/span>cols = [0, 1, 3] \n<\/span>df. drop<\/span> (df. columns<\/span> [cols], axis= 1<\/span> , inplace= True<\/span> )\n\n#view resulting dataFrame<\/span>\ndf\n\n        last\n0 Nowitzki\n1 Bryant\n2 Duncan\n3 James\n<\/strong><\/pre>\n
\u793a\u4f8b 3\uff1a\u901a\u8fc7\u7d22\u5f15\u5220\u9664\u5177\u6709\u91cd\u590d\u9879\u7684\u5217<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5f53\u5b58\u5728\u91cd\u590d\u7684\u5217\u540d\u65f6\u5982\u4f55\u901a\u8fc7 pandas DataFrame \u4e2d\u7684\u7d22\u5f15\u53f7\u5220\u9664\u5217\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd<\/span>\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],\n                   ' last<\/span> ': ['Nowitzki', 'Bryant', 'Duncan', 'James'],\n                   ' last<\/span> ': ['Nowitzki', 'Bryant', 'Duncan', 'James'],\n                   ' points<\/span> ': [26, 31, 22, 29]},\n                   columns=[' team<\/span> ', ' last<\/span> ', ' last<\/span> ', ' points<\/span> '])\n\n<\/span>#define list of columns range\ncols = [x for<\/span> x in<\/span> range( df.shape<\/span> [1])]\n<\/span>\n#remove second column in DataFrame\ncollars. remove<\/span> (1)\n<\/span>\n#view resulting DataFrame\ndf. iloc<\/span> [:, cols]\n\n\tteam last points\n0 Mavs Nowitzki 26\n1 Lakers Bryant 31\n2 Spurs Duncan 22\n3 Cavs James 29\n<\/span><\/span><\/strong><\/pre>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u5982\u4f55\u5728 Pandas \u4e2d\u5408\u5e76\u4e24\u5217<\/a>
Pandas\uff1a\u5982\u4f55\u6309\u540d\u79f0\u5bf9\u5217\u8fdb\u884c\u6392\u5e8f<\/a>
Pandas\uff1a\u5982\u4f55\u627e\u5230\u4e24\u5217\u4e4b\u95f4\u7684\u5dee\u5f02<\/a>
Pandas\uff1a\u5982\u4f55\u6839\u636e\u6761\u4ef6\u6dfb\u52a0\u5217<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6309\u7d22\u5f15\u53f7\u4ece pandas DataFrame \u4e2d\u5220\u9664\u5217\uff1a #drop first colum […]<\/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":[],"class_list":["post-1733","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u5728 Pandas \u4e2d\u6309\u7d22\u5f15\u5220\u9664\u5217 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u51e0\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u901a\u8fc7\u7d22\u5f15\u53f7\u4ece pandas DataFrame \u4e2d\u5220\u9664\u4e00\u5217\u6216\u591a\u5217\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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