{"id":4210,"date":"2023-07-12T20:21:46","date_gmt":"2023-07-12T20:21:46","guid":{"rendered":"https:\/\/statorials.org\/cn\/pandas-%e5%b5%8c%e5%a5%97%e6%95%b0%e6%8d%ae%e6%a1%86\/"},"modified":"2023-07-12T20:21:46","modified_gmt":"2023-07-12T20:21:46","slug":"pandas-%e5%b5%8c%e5%a5%97%e6%95%b0%e6%8d%ae%e6%a1%86","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/pandas-%e5%b5%8c%e5%a5%97%e6%95%b0%e6%8d%ae%e6%a1%86\/","title":{"rendered":"\u5982\u4f55\u5728 pandas \u4e2d\u521b\u5efa\u5d4c\u5957 dataframe\uff08\u5e26\u6709\u793a\u4f8b\uff09"},"content":{"rendered":"

<\/p>\n

\n

\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u5c06\u591a\u4e2a pandas DataFrame \u5d4c\u5957\u5728\u53e6\u4e00\u4e2a DataFrame \u4e2d\uff1a<\/span><\/p>\n

 df_all = pd. DataFrame<\/span> ({' idx<\/span> ':[1,2,3],' dfs<\/span> ':[df1,df2,df3]})\n<\/strong><\/pre>\n
\u6b64\u7279\u5b9a\u793a\u4f8b\u5c06\u4e09\u4e2a DataFrame\uff08 df1<\/strong> \u3001 df2<\/strong> \u3001 df3<\/strong> \uff09\u5d4c\u5957\u5230\u4e00\u4e2a\u540d\u4e3adf_all<\/strong>\u7684\u66f4\u5927 DataFrame \u4e2d\u3002<\/span><\/p>\n
\u7136\u540e\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6765\u8bbf\u95ee\u7279\u5b9a\u7684\u5d4c\u5957 DataFrame \u4e4b\u4e00\uff1a<\/span><\/p>\n
 #display first nested DataFrame\n<\/span>print<\/span> (df_all[' dfs<\/span> ']. iloc<\/span> [0])<\/strong><\/pre>\n
\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u6b64\u8bed\u6cd5\u3002<\/span><\/p>\n
\u793a\u4f8b\uff1a\u5728 Pandas \u4e2d\u521b\u5efa\u5d4c\u5957 DataFrame<\/strong><\/span><\/h2>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4e09\u4e2a panda DataFrame\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#create first DataFrame\n<\/span>df1 = pd. DataFrame<\/span> ({' item<\/span> ': ['A', 'B', 'C', 'D', 'E'],\n                    ' sales<\/span> ': [18, 22, 19, 14, 30]})\n\nprint<\/span> (df1)\n\n  item sales\n0 to 18\n1 B 22\n2 C 19\n3 D 14\n4 E 30\n\n#create second DataFrame\n<\/span>df2 = pd. DataFrame<\/span> ({' item<\/span> ': ['F', 'G', 'H', 'I', 'J'],\n                    ' sales<\/span> ': [10, 12, 13, 13, 19]})\n\nprint<\/span> (df2)\n\n  item sales\n0 F 10\n1 G 12\n2:13 a.m.\n3 I 13\n4 Day 19\n\n#create third DataFrame\n<\/span>df3 = pd. DataFrame<\/span> ({' item<\/span> ': ['K', 'L', 'M', 'N', 'O'],\n                    ' sales<\/span> ': [41, 22, 28, 25, 18]})\n\nprint<\/span> (df3)\n\n  item sales\n0 K 41\n1 L 22\n2 M 28\n3 N 25\n4 O 18\n<\/strong><\/pre>\n
\u73b0\u5728\u5047\u8bbe\u6211\u4eec\u8981\u521b\u5efa\u4e00\u4e2a\u5927\u7684 DataFrame \u6765\u4fdd\u5b58\u8fd9\u4e09\u4e2a DataFrame\u3002<\/span><\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6765\u505a\u5230\u8fd9\u4e00\u70b9\uff1a<\/span><\/p>\n
 df_all = pd. DataFrame<\/span> ({' idx<\/span> ':[1,2,3],' dfs<\/span> ':[df1,df2,df3]})\n<\/strong><\/pre>\n
\u7136\u540e\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 pandas iloc<\/strong>\u51fd\u6570\u6765\u8bbf\u95ee\u7279\u5b9a\u7684\u5d4c\u5957 DataFrame\u3002<\/span><\/p>\n
\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6765\u8bbf\u95ee\u7b2c\u4e00\u4e2a\u5d4c\u5957\u7684 DataFrame\uff1a<\/span><\/p>\n
 #display first nested DataFrame\n<\/span>print<\/span> (df_all[' dfs<\/span> ']. iloc<\/span> [0])\n\n  item sales\n0 to 18\n1 B 22\n2 C 19\n3 D 14\n4 E 30\n<\/strong><\/pre>\n
\u6216\u8005\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6765\u8bbf\u95ee\u7b2c\u4e8c\u4e2a\u5d4c\u5957\u7684 DataFrame\uff1a<\/span><\/p>\n
 #display second nested DataFrame\n<\/span>print<\/span> (df_all[' dfs<\/span> ']. iloc<\/span> [1])\n\n  item sales\n0 F 10\n1 G 12\n2:13 a.m.\n3 I 13\n4 Day 19<\/strong><\/pre>\n
\u7b49\u7b49\u3002<\/span><\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 pandas \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u529f\u80fd\uff1a<\/span><\/p>\n
\u5982\u4f55\u5728 Pandas \u4e2d\u5c06\u7d22\u5f15\u8f6c\u6362\u4e3a\u5217<\/a>
\u5982\u4f55\u91cd\u547d\u540d Pandas \u4e2d\u7684\u7d22\u5f15<\/a>
\u5982\u4f55\u5728 Pandas \u4e2d\u5c06\u5217\u8bbe\u7f6e\u4e3a\u7d22\u5f15<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u5c06\u591a\u4e2a pandas DataFrame \u5d4c\u5957\u5728\u53e6\u4e00\u4e2a DataFrame \u4e2d\uff1a df_al […]<\/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-4210","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u5728 Pandas \u4e2d\u521b\u5efa\u5d4c\u5957 DataFrame\uff08\u5e26\u6709\u793a\u4f8b\uff09- Statory<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 pandas \u4e2d\u521b\u5efa\u5d4c\u5957 DataFrame \u4ee5\u53ca\u5982\u4f55\u8bbf\u95ee\u5b83\u5305\u542b\u7684 DataFrame\uff0c\u5305\u62ec\u4e00\u4e2a\u793a\u4f8b\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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