{"id":4110,"date":"2023-07-13T13:10:59","date_gmt":"2023-07-13T13:10:59","guid":{"rendered":"https:\/\/statorials.org\/cn\/pandas-%e6%8f%90%e5%8f%96%e5%ad%97%e7%ac%a6%e4%b8%b2%e6%95%b0%e9%87%8f\/"},"modified":"2023-07-13T13:10:59","modified_gmt":"2023-07-13T13:10:59","slug":"pandas-%e6%8f%90%e5%8f%96%e5%ad%97%e7%ac%a6%e4%b8%b2%e6%95%b0%e9%87%8f","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/pandas-%e6%8f%90%e5%8f%96%e5%ad%97%e7%ac%a6%e4%b8%b2%e6%95%b0%e9%87%8f\/","title":{"rendered":"\u5982\u4f55\u4ece pandas \u4e2d\u7684\u5b57\u7b26\u4e32\u4e2d\u63d0\u53d6\u6570\u5b57"},"content":{"rendered":"

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\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\u4ece pandas \u4e2d\u7684\u5b57\u7b26\u4e32\u4e2d\u63d0\u53d6\u6570\u5b57\uff1a<\/span><\/p>\n

 df[' my_column<\/span> ']. str<\/span> . extract<\/span> (' (\\d+)<\/span> ')\n<\/strong><\/pre>\n
\u8fd9\u79cd\u7279\u6b8a\u7684\u8bed\u6cd5\u4f1a\u5c06\u6bcf\u4e2a\u5b57\u7b26\u4e32\u4e2d\u7684\u6570\u5b57\u63d0\u53d6\u5230 pandas DataFrame \u4e2d\u540d\u4e3amy_column<\/strong>\u7684\u5217\u4e2d\u3002<\/span><\/p>\n
\u6ce8\u610f<\/strong>\uff1a\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u65f6\uff0c \\d<\/strong>\u8868\u793a\u201c\u4efb\u4f55\u6570\u5b57\u201d\uff0c +<\/strong>\u8868\u793a\u201c\u4e00\u4e2a\u6216\u591a\u4e2a\u201d\u3002<\/span><\/p>\n
\u4e0b\u9762\u7684\u4f8b\u5b50\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u9645\u4e2d\u4f7f\u7528\u8fd9\u4e2a\u529f\u80fd\u3002<\/span><\/p>\n
\u793a\u4f8b\uff1a\u4ece Pandas \u4e2d\u7684\u5b57\u7b26\u4e32\u4e2d\u63d0\u53d6\u6570\u5b57<\/strong><\/span><\/h2>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b pandas DataFrame\uff0c\u5176\u4e2d\u5305\u542b\u6709\u5173\u5404\u79cd\u4ea7\u54c1\u9500\u552e\u7684\u4fe1\u606f\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' product<\/span> ': ['A33', 'B34', 'A22', 'A50', 'C200', 'D7', 'A9', 'A13'],\n                   ' sales<\/span> ': [18, 22, 19, 14, 14, 11, 20, 28]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  product sales\n0 A33 18\n1 B34 22\n2 A22 19\n3 A50 14\n4 C200 14\n5 D7 11\n6 A9 20\n7 A13 28<\/span><\/span><\/strong><\/pre>\n
\u5047\u8bbe\u6211\u4eec\u8981\u4ece\u4ea7\u54c1<\/strong>\u5217\u4e2d\u63d0\u53d6\u6bcf\u4e2a\u5b57\u7b26\u4e32\u7684\u7f16\u53f7\u3002<\/span><\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6765\u505a\u5230\u8fd9\u4e00\u70b9\uff1a<\/span><\/p>\n
 #extract numbers from strings in 'product' column\n<\/span>df[' product<\/span> ']. str<\/span> . extract<\/span> (' (\\d+)<\/span> ')\n\n\t0\n0 33\n1 34\n2 22\n3 50\n4,200\n5 7\n6 9\n7 13\n<\/span><\/span><\/strong><\/pre>\n
\u7ed3\u679c\u662f\u4e00\u4e2a\u4ec5\u5305\u542bProduct<\/strong>\u5217\u6bcf\u884c\u4e2d\u7684\u6570\u5b57\u7684 DataFrame\u3002<\/span><\/p>\n
\u4f8b\u5982\uff1a<\/span><\/p>\n