{"id":2384,"date":"2023-07-22T12:34:04","date_gmt":"2023-07-22T12:34:04","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e7%86%8a%e7%8c%ab%e7%a7%bb%e4%bd%8d%e5%88%97\/"},"modified":"2023-07-22T12:34:04","modified_gmt":"2023-07-22T12:34:04","slug":"%e7%86%8a%e7%8c%ab%e7%a7%bb%e4%bd%8d%e5%88%97","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e7%86%8a%e7%8c%ab%e7%a7%bb%e4%bd%8d%e5%88%97\/","title":{"rendered":"\u5982\u4f55\u504f\u79fb pandas \u4e2d\u7684\u5217\uff1a\u793a\u4f8b"},"content":{"rendered":"

<\/p>\n

\n

\u60a8\u53ef\u4ee5\u4f7f\u7528shift()<\/strong>\u51fd\u6570\u5c06 pandas DataFrame \u4e2d\u7684\u5217\u4e0a\u79fb\u6216\u4e0b\u79fb\uff1a<\/span><\/p>\n

 #shift values down by 1<\/span>\ndf[' column1<\/span> '] = df[' column1<\/span> ']. shift<\/span> (1)\n\n#shift values up by 1<\/span>\ndf[' column1<\/span> '] = df[' column1<\/span> ']. shift<\/span> (-1)\n<\/strong><\/pre>\n
\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u901a\u8fc7\u4ee5\u4e0b pandas DataFrame \u4f7f\u7528\u6b64\u51fd\u6570\uff1a<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' product<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F'],\n                   ' sales<\/span> ': [4, 7, 8, 12, 15, 19]})\n\n#view DataFrame\n<\/span>df\n\n        product sales\n0 to 4\n1 B 7\n2 C 8\n3 D 12\n4 E 15\n5 F 19<\/strong><\/pre>\n
\u793a\u4f8b 1\uff1a\u5411\u4e0a\u6216\u5411\u4e0b\u79fb\u52a8\u5217<\/strong><\/span><\/h3>\n
\u4e0b\u9762\u7684\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5c06\u201cproduct\u201d\u5217\u4e2d\u7684\u6240\u6709\u503c\u4e0b\u79fb 1\uff1a<\/span><\/p>\n
 #shift all 'product' values down by 1\n<\/span>df[' product<\/span> '] = df[' product<\/span> ']. shift<\/span> (1)\n\n#view updated DataFrame\n<\/span>df\n\n\tproduct sales\n0 NaN 4\n1 to 7\n2 B 8\n3 C 12\n4 D 15\n5 E 19\n<\/span><\/span><\/strong><\/pre>\n
\u8bf7\u6ce8\u610f\uff0c\u201cproduct\u201d\u5217\u4e2d\u7684\u6bcf\u4e2a\u503c\u5747\u5df2\u79fb\u4f4d 1\uff0c\u5e76\u4e14\u8be5\u5217\u4e2d\u7684\u7b2c\u4e00\u4e2a\u503c\u5df2\u66f4\u6539\u4e3a NaN\u3002<\/span><\/p>\n
\u53e6\u8bf7\u6ce8\u610f\uff0c\u4ea7\u54c1\u5217\u4e2d\u7684\u6700\u540e\u4e00\u4e2a\u503c\uff08\u201cF\u201d\uff09\u5df2\u4ece DataFrame \u4e2d\u5b8c\u5168\u5220\u9664\u3002<\/span><\/p>\n
\u4e3a\u4e86\u4fdd\u7559DataFrame\u4e2d\u7684\u503c\u201cF\u201d\uff0c\u6211\u4eec\u9996\u5148\u9700\u8981\u5728DataFrame\u7684\u5e95\u90e8\u6dfb\u52a0\u4e00\u4e2a\u7a7a\u884c\uff0c\u7136\u540e\u8fdb\u884c\u504f\u79fb\uff1a<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\n\n#add empty row to bottom of DataFrame\n<\/span>df. loc<\/span> [len(df. index<\/span> )] = [np. nah<\/span> , np. no<\/span> ]\n\n#shift all 'product' values down by 1\n<\/span>df[' product<\/span> '] = df[' product<\/span> ']. shift<\/span> (1)\n\n#view updated DataFrame\n<\/span>df\n\n\tproduct sales\n0 NaN 4.0\n1 to 7.0\n2B 8.0\n3C 12.0\n4 D 15.0\n5 E 19.0\n6 F NaN\n<\/span><\/span><\/strong><\/pre>\n
\u8bf7\u6ce8\u610f\uff0c\u201cF\u201d\u503c\u4fdd\u7559\u4e3a\u201c\u4ea7\u54c1\u201d\u5217\u4e2d\u7684\u6700\u540e\u4e00\u4e2a\u503c\u3002<\/span><\/p>\n
\u793a\u4f8b 2\uff1a\u5411\u4e0a\u6216\u5411\u4e0b\u79fb\u52a8\u591a\u5217<\/strong><\/span><\/h3>\n
\u4e0b\u9762\u7684\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5c06\u201cproduct\u201d\u548c\u201csales\u201d\u5217\u4e2d\u7684\u6240\u6709\u503c\u5411\u4e0a\u79fb\u52a8 2\uff1a<\/span><\/p>\n
 #shift all 'product' and 'sales' values up by 2\n<\/span>df[[' product<\/span> ', ' sales<\/span> ']] = df[[' product<\/span> ', ' sales<\/span> ']]. shift<\/span> (-2)\n\n#view updated DataFrame\n<\/span>df\n\n        product sales\n0 C 8.0\n1 D 12.0\n2 E 15.0\n3 F 19.0\n4 NaN NaN\n5 NaN NaN\n<\/span><\/span><\/strong><\/pre>\n
\u8bf7\u6ce8\u610f\uff0c\u201c\u4ea7\u54c1\u201d\u548c\u201c\u9500\u552e\u201d\u5217\u4e2d\u7684\u6bcf\u4e2a\u503c\u90fd\u5df2\u4e0a\u79fb 2\uff0c\u5e76\u4e14\u6bcf\u5217\u4e2d\u5e95\u90e8\u7684\u4e24\u4e2a\u503c\u5df2\u66f4\u6539\u4e3a NaN\u3002<\/span><\/p>\n
\u6ce8\u610f<\/strong>\uff1a\u60a8\u53ef\u4ee5\u5728\u6b64\u5904<\/a>\u627e\u5230shift()<\/strong>\u51fd\u6570\u7684\u5b8c\u6574\u6587\u6863\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 pandas \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u64cd\u4f5c\uff1a<\/span><\/p>\n
\u5982\u4f55\u5411 Pandas DataFrame \u6dfb\u52a0\u884c<\/a>
\u5982\u4f55\u5411 Pandas DataFrame \u6dfb\u52a0\u5217
\u5982\u4f55\u5411 Pandas DataFrame \u6dfb\u52a0\u6807\u9898\u884c<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u60a8\u53ef\u4ee5\u4f7f\u7528shift()\u51fd\u6570\u5c06 pandas DataFrame \u4e2d\u7684\u5217\u4e0a\u79fb\u6216\u4e0b\u79fb\uff1a #shift value […]<\/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-2384","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u5982\u4f55\u504f\u79fb Pandas \u4e2d\u7684\u5217\uff08\u5e26\u6709\u793a\u4f8b\uff09 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u51e0\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u504f\u79fb pandas DataFrame \u4e2d\u7684\u5217\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/cn\/\u718a\u732b\u79fb\u4f4d\u5217\/\" \/>\n<meta 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