{"id":3754,"date":"2023-07-15T18:16:37","date_gmt":"2023-07-15T18:16:37","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e6%af%8f%e5%a4%a9%e4%b8%80%e7%be%a4%e7%86%8a%e7%8c%ab\/"},"modified":"2023-07-15T18:16:37","modified_gmt":"2023-07-15T18:16:37","slug":"%e6%af%8f%e5%a4%a9%e4%b8%80%e7%be%a4%e7%86%8a%e7%8c%ab","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e6%af%8f%e5%a4%a9%e4%b8%80%e7%be%a4%e7%86%8a%e7%8c%ab\/","title":{"rendered":"\u5982\u4f55\u5728 pandas dataframe \u4e2d\u6309\u5929\u5206\u7ec4\uff08\u4e3e\u4f8b\uff09"},"content":{"rendered":"

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\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\u5728 pandas DataFrame \u4e2d\u6309\u5929\u5bf9\u884c\u8fdb\u884c\u5206\u7ec4\uff1a<\/span><\/p>\n

 df. groupby<\/span> (df. your_date_column<\/span> . dt<\/span> . day<\/span> )[' values_column<\/span> ']. sum<\/span> ()\n<\/strong><\/pre>\n
\u8fd9\u4e2a\u7279\u5b9a\u7684\u516c\u5f0f\u6309\u65e5\u671f\u5bf9your_date_column<\/strong>\u4e2d\u7684\u884c\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u8ba1\u7b97 DataFrame \u4e2dvalues_column<\/strong>\u7684\u503c\u7684\u603b\u548c\u3002<\/span><\/p>\n
\u8bf7\u6ce8\u610f\uff0c dt.day()<\/strong>\u51fd\u6570\u4ece pandas \u7684\u65e5\u671f\u5217\u4e2d\u63d0\u53d6\u65e5\u671f\u3002<\/span><\/p>\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\u5982\u4f55\u5728 pandas \u4e2d\u6309\u5929\u5206\u7ec4<\/strong><\/span><\/h2>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b pandas DataFrame\uff0c\u5b83\u663e\u793a\u4e86\u4e00\u5bb6\u516c\u53f8\u5728\u4e0d\u540c\u65e5\u671f\u7684\u9500\u552e\u989d\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' date<\/span> ': pd. date_range<\/span> (start=' 1\/1\/2020<\/span> ', freq=' 8h<\/span> ', periods= 10<\/span> ),\n                   ' sales<\/span> ': [6, 8, 9, 11, 13, 8, 8, 15, 22, 9],\n                   ' returns<\/span> ': [0, 3, 2, 2, 1, 3, 2, 4, 1, 5]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n                 date sales returns\n0 2020-01-01 00:00:00 6 0\n1 2020-01-01 08:00:00 8 3\n2 2020-01-01 16:00:00 9 2\n3 2020-01-02 00:00:00 11 2\n4 2020-01-02 08:00:00 13 1\n5 2020-01-02 16:00:00 8 3\n6 2020-01-03 00:00:00 8 2\n7 2020-01-03 08:00:00 15 4\n8 2020-01-03 16:00:00 22 1\n9 2020-01-04 00:00:00 9 5<\/strong><\/span><\/pre>\n
\u76f8\u5173\uff1a<\/strong>\u5982\u4f55\u5728 Pandas \u4e2d\u521b\u5efa\u65e5\u671f\u8303\u56f4<\/a><\/span><\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6765\u8ba1\u7b97\u6309\u5929\u5206\u7ec4\u7684\u9500\u552e\u989d\u603b\u548c\uff1a<\/span><\/p>\n
 #calculate sum of sales grouped by day\ndf. groupby<\/span> (df. date<\/span> . dt<\/span> . day<\/span> )[' sales<\/span> ']. sum<\/span> ()\n\ndate\n1 23\n2 32\n3 45\n4 9\nName: sales, dtype: int64<\/span><\/span><\/strong><\/pre>\n
\u4ee5\u4e0b\u662f\u5982\u4f55\u89e3\u91ca\u7ed3\u679c\uff1a<\/span><\/p>\n