{"id":1264,"date":"2023-07-27T02:24:49","date_gmt":"2023-07-27T02:24:49","guid":{"rendered":"https:\/\/statorials.org\/cn\/pandas-%e8%af%bb%e5%8f%96%e6%96%87%e6%9c%ac%e6%96%87%e4%bb%b6\/"},"modified":"2023-07-27T02:24:49","modified_gmt":"2023-07-27T02:24:49","slug":"pandas-%e8%af%bb%e5%8f%96%e6%96%87%e6%9c%ac%e6%96%87%e4%bb%b6","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/pandas-%e8%af%bb%e5%8f%96%e6%96%87%e6%9c%ac%e6%96%87%e4%bb%b6\/","title":{"rendered":"\u5982\u4f55\u7528 pandas \u8bfb\u53d6\u6587\u672c\u6587\u4ef6\uff08\u5305\u62ec\u793a\u4f8b\uff09"},"content":{"rendered":"

<\/p>\n

\n

\u8981\u5728 Python \u4e2d\u4f7f\u7528 pandas \u8bfb\u53d6\u6587\u672c\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\uff1a<\/span><\/p>\n

 df = pd. read_csv<\/span> (\" data.txt<\/span> \", sep=\"\")\n<\/strong><\/pre>\n
\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u8be5\u529f\u80fd\u5b9e\u9645\u4f7f\u7528\u7684\u51e0\u4e2a\u793a\u4f8b\u3002<\/span><\/p>\n
\u8bfb\u53d6\u5e26\u6807\u9898\u7684\u6587\u672c\u6587\u4ef6<\/strong><\/span><\/h3>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b\u540d\u4e3adata.txt \u7684<\/strong>\u6587\u672c\u6587\u4ef6\uff0c\u5176\u4e2d\u5305\u542b\u6807\u9898\uff1a<\/span> <\/p>\n
\"\u5728<\/p>\n
\u8981\u5c06\u8fd9\u4e2a\u6587\u4ef6\u8bfb\u5165 pandas DataFrame\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd<\/span>\n\n#read text file into pandas DataFrame<\/span>\ndf = pd. read_csv<\/span> (\" data.txt<\/span> \", sep=\"\")\n\n#display DataFrame<\/span>\nprint<\/span> (df)\n\n   column1 column2\n0 1 4\n1 3 4\n2 2 5\n3 7 9\n4 9 1\n5 6 3\n6 4 4\n7 5 2\n8 4 8\n9 6 8\n<\/strong><\/pre>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6253\u5370 DataFrame \u7c7b\u5e76\u67e5\u627e\u884c\u6570\u548c\u5217\u6570\uff1a<\/span><\/p>\n
 #display class of DataFrame\n<\/span>print<\/span> (type(df))\n\n<class 'pandas.core.frame.DataFrame'>\n\n#display number of rows and columns in DataFrame\ndf. shape<\/span><\/span>\n\n(10, 2)\n<\/span><\/span><\/strong><\/pre>\n
\u6211\u4eec\u53ef\u4ee5\u770b\u5230df<\/strong>\u662f\u4e00\u4e2a\u6709 10 \u884c 2 \u5217\u7684 pandas DataFrame\u3002<\/span><\/p>\n
\u8bfb\u53d6\u6ca1\u6709\u6807\u9898\u7684\u6587\u672c\u6587\u4ef6<\/strong><\/span><\/h3>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b\u540d\u4e3adata.txt \u7684<\/strong>\u6587\u672c\u6587\u4ef6\uff0c\u6ca1\u6709\u6807\u9898\uff1a<\/span> <\/p>\n
\"Pandas<\/p>\n
\u8981\u5c06\u8fd9\u4e2a\u6587\u4ef6\u8bfb\u5165 pandas DataFrame\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\uff1a<\/span><\/p>\n
 #read text file into pandas DataFrame<\/span>\ndf = pd. read_csv<\/span> (\" data.txt<\/span> \", sep=\"\", header= None<\/span> )\n\n#display DataFrame<\/span>\nprint<\/span> (df)\n\n   0 1\n0 1 4\n1 3 4\n2 2 5\n3 7 9\n4 9 1\n5 6 3\n6 4 4\n7 5 2\n8 4 8\n9 6 8\n<\/strong><\/pre>\n
\u7531\u4e8e\u6587\u672c\u6587\u4ef6\u6ca1\u6709\u6807\u9898\uff0cpandas \u53ea\u662f\u5c06\u5217\u547d\u540d\u4e3a0<\/strong>\u548c1<\/strong> \u3002<\/span><\/p>\n
\u8bfb\u53d6\u6ca1\u6709\u6807\u9898\u7684\u6587\u672c\u6587\u4ef6\u5e76\u6307\u5b9a\u5217\u540d\u79f0<\/strong><\/span><\/h3>\n
\u5982\u679c\u9700\u8981\uff0c\u6211\u4eec\u53ef\u4ee5\u5728\u5bfc\u5165\u6587\u672c\u6587\u4ef6\u65f6\u4f7f\u7528\u540d\u79f0<\/strong>\u53c2\u6570\u6307\u5b9a\u5217\u540d\u79f0\uff1a<\/span><\/p>\n
 #read text file into pandas DataFrame and specify column names<\/span>\ndf = pd. read_csv<\/span> (\" data.txt<\/span> \", sep=\"\", header= None, names=[\"<\/span> A \", \"<\/span> B \"]<\/span><\/span> )\n\n#display DataFrame\n<\/span>print<\/span> (df)\n\n   AB\n0 1 4\n1 3 4\n2 2 5\n3 7 9\n4 9 1\n5 6 3\n6 4 4\n7 5 2\n8 4 8\n9 6 8\n<\/strong><\/pre>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u5982\u4f55\u4f7f\u7528 Pandas \u8bfb\u53d6 CSV \u6587\u4ef6<\/a>
\u5982\u4f55\u4f7f\u7528 Pandas \u8bfb\u53d6 Excel \u6587\u4ef6<\/a>
\u5982\u4f55\u4f7f\u7528 Pandas \u8bfb\u53d6 JSON \u6587\u4ef6<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u8981\u5728 Python \u4e2d\u4f7f\u7528 pandas \u8bfb\u53d6\u6587\u672c\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\uff1a df = pd. read_c […]<\/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-1264","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u7528 Pandas \u8bfb\u53d6\u6587\u672c\u6587\u4ef6\uff08\u5305\u62ec\u793a\u4f8b\uff09<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u51e0\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Python \u4e2d\u4f7f\u7528 pandas \u8bfb\u53d6\u6587\u672c\u6587\u4ef6\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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