{"id":478331,"date":"2023-08-09T09:31:12","date_gmt":"2023-08-09T09:31:12","guid":{"rendered":""},"modified":"2023-09-05T11:16:31","modified_gmt":"2023-09-05T11:16:31","slug":"pandas","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/pandas\/","title":{"rendered":"\u718a\u732b"},"content":{"rendered":"<p>Pandas \u662f\u4e00\u4e2a\u6d41\u884c\u7684 Python \u7f16\u7a0b\u8bed\u8a00\u5f00\u6e90\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u5f3a\u5927\u800c\u7075\u6d3b\u7684\u5de5\u5177\u6765\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\uff0c\u4f7f\u5176\u6210\u4e3a\u6570\u636e\u79d1\u5b66\u5bb6\u3001\u5206\u6790\u5e08\u548c\u7814\u7a76\u4eba\u5458\u7684\u5fc5\u5907\u5de5\u5177\u3002 Pandas \u5e7f\u6cdb\u5e94\u7528\u4e8e\u91d1\u878d\u3001\u533b\u7597\u4fdd\u5065\u3001\u8425\u9500\u548c\u5b66\u672f\u754c\u7b49\u5404\u4e2a\u884c\u4e1a\uff0c\u53ef\u9ad8\u6548\u5904\u7406\u6570\u636e\u5e76\u8f7b\u677e\u6267\u884c\u6570\u636e\u5206\u6790\u4efb\u52a1\u3002<\/p>\n<h2>\u5927\u718a\u732b\u7684\u8d77\u6e90\u5386\u53f2\u53ca\u5176\u9996\u6b21\u63d0\u53ca\u3002<\/h2>\n<p>Pandas \u662f\u7531 Wes McKinney \u4e8e 2008 \u5e74\u521b\u5efa\u7684\uff0c\u5f53\u65f6\u4ed6\u5728 AQR Capital Management \u62c5\u4efb\u91d1\u878d\u5206\u6790\u5e08\u3002\u7531\u4e8e\u5bf9\u73b0\u6709\u6570\u636e\u5206\u6790\u5de5\u5177\u7684\u5c40\u9650\u6027\u611f\u5230\u6cae\u4e27\uff0cMcKinney \u7684\u76ee\u6807\u662f\u5efa\u7acb\u4e00\u4e2a\u80fd\u591f\u6709\u6548\u5904\u7406\u5927\u89c4\u6a21\u3001\u73b0\u5b9e\u4e16\u754c\u6570\u636e\u5206\u6790\u4efb\u52a1\u7684\u5e93\u3002\u4ed6\u4e8e 2009 \u5e74 1 \u6708\u53d1\u5e03\u4e86 Pandas \u7684\u7b2c\u4e00\u4e2a\u7248\u672c\uff0c\u6700\u521d\u7684\u7075\u611f\u6765\u81ea\u4e8e R \u7f16\u7a0b\u8bed\u8a00\u7684\u6570\u636e\u6846\u67b6\u548c\u6570\u636e\u64cd\u4f5c\u529f\u80fd\u3002<\/p>\n<h2>\u6709\u5173\u718a\u732b\u7684\u8be6\u7ec6\u4fe1\u606f\u3002\u6269\u5c55\u718a\u732b\u4e3b\u9898\u3002<\/h2>\n<p>Pandas \u6784\u5efa\u5728\u4e24\u79cd\u57fa\u672c\u6570\u636e\u7ed3\u6784\u4e4b\u4e0a\uff1aSeries \u548c DataFrame\u3002\u8fd9\u4e9b\u6570\u636e\u7ed3\u6784\u5141\u8bb8\u7528\u6237\u4ee5\u8868\u683c\u5f62\u5f0f\u5904\u7406\u548c\u64cd\u4f5c\u6570\u636e\u3002 Series \u662f\u4e00\u4e2a\u4e00\u7ef4\u6807\u8bb0\u6570\u7ec4\uff0c\u53ef\u4ee5\u4fdd\u5b58\u4efb\u4f55\u7c7b\u578b\u7684\u6570\u636e\uff0c\u800c DataFrame \u662f\u4e00\u4e2a\u4e8c\u7ef4\u6807\u8bb0\u6570\u636e\u7ed3\u6784\uff0c\u5176\u4e2d\u5305\u542b\u53ef\u80fd\u4e0d\u540c\u6570\u636e\u7c7b\u578b\u7684\u5217\u3002<\/p>\n<p>Pandas \u7684\u4e3b\u8981\u7279\u70b9\u5305\u62ec\uff1a<\/p>\n<ul>\n<li>\u6570\u636e\u5bf9\u9f50\u548c\u5904\u7406\u7f3a\u5931\u6570\u636e\uff1aPandas \u81ea\u52a8\u5bf9\u9f50\u6570\u636e\u5e76\u6709\u6548\u5904\u7406\u7f3a\u5931\u503c\uff0c\u4ece\u800c\u66f4\u8f7b\u677e\u5730\u5904\u7406\u73b0\u5b9e\u4e16\u754c\u7684\u6570\u636e\u3002<\/li>\n<li>\u6570\u636e\u8fc7\u6ee4\u548c\u5207\u7247\uff1aPandas \u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5de5\u5177\u6765\u6839\u636e\u5404\u79cd\u6807\u51c6\u8fc7\u6ee4\u548c\u5207\u7247\u6570\u636e\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u63d0\u53d6\u7279\u5b9a\u7684\u6570\u636e\u5b50\u96c6\u8fdb\u884c\u5206\u6790\u3002<\/li>\n<li>\u6570\u636e\u6e05\u7406\u548c\u8f6c\u6362\uff1a\u5b83\u63d0\u4f9b\u6570\u636e\u6e05\u7406\u548c\u9884\u5904\u7406\u529f\u80fd\uff0c\u4f8b\u5982\u5220\u9664\u91cd\u590d\u9879\u3001\u586b\u5145\u7f3a\u5931\u503c\u4ee5\u53ca\u5728\u4e0d\u540c\u683c\u5f0f\u4e4b\u95f4\u8f6c\u6362\u6570\u636e\u3002<\/li>\n<li>\u5206\u7ec4\u548c\u805a\u5408\uff1aPandas \u652f\u6301\u6839\u636e\u7279\u5b9a\u6807\u51c6\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u5e76\u6267\u884c\u805a\u5408\u64cd\u4f5c\uff0c\u4ece\u800c\u5b9e\u73b0\u5bcc\u6709\u6d1e\u5bdf\u529b\u7684\u6570\u636e\u6c47\u603b\u3002<\/li>\n<li>\u5408\u5e76\u548c\u8fde\u63a5\u6570\u636e\uff1a\u7528\u6237\u53ef\u4ee5\u4f7f\u7528Pandas\u57fa\u4e8e\u516c\u5171\u5217\u7ec4\u5408\u591a\u4e2a\u6570\u636e\u96c6\uff0c\u4ece\u800c\u65b9\u4fbf\u5730\u96c6\u6210\u4e0d\u540c\u7684\u6570\u636e\u6e90\u3002<\/li>\n<li>\u65f6\u95f4\u5e8f\u5217\u529f\u80fd\uff1aPandas \u4e3a\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u63d0\u4f9b\u5f3a\u5927\u7684\u652f\u6301\uff0c\u5305\u62ec\u91cd\u91c7\u6837\u3001\u65f6\u79fb\u548c\u6eda\u52a8\u7a97\u53e3\u8ba1\u7b97\u3002<\/li>\n<\/ul>\n<h2>Pandas \u7684\u5185\u90e8\u7ed3\u6784\u3002Pandas \u7684\u5de5\u4f5c\u539f\u7406\u3002<\/h2>\n<p>Pandas \u6784\u5efa\u5728 NumPy \u4e4b\u4e0a\uff0cNumPy \u662f\u53e6\u4e00\u4e2a\u6d41\u884c\u7684 Python \u6570\u503c\u8ba1\u7b97\u5e93\u3002\u5b83\u4f7f\u7528NumPy\u6570\u7ec4\u4f5c\u4e3a\u5b58\u50a8\u548c\u64cd\u4f5c\u6570\u636e\u7684\u540e\u7aef\uff0c\u63d0\u4f9b\u9ad8\u6548\u3001\u9ad8\u6027\u80fd\u7684\u6570\u636e\u64cd\u4f5c\u3002\u4e3b\u8981\u6570\u636e\u7ed3\u6784 Series \u548c DataFrame \u65e8\u5728\u6709\u6548\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\uff0c\u540c\u65f6\u4fdd\u6301\u6570\u636e\u5206\u6790\u6240\u9700\u7684\u7075\u6d3b\u6027\u3002<\/p>\n<p>\u5728\u5e95\u5c42\uff0cPandas \u4f7f\u7528\u6807\u8bb0\u8f74\uff08\u884c\u548c\u5217\uff09\u6765\u63d0\u4f9b\u4e00\u81f4\u4e14\u6709\u610f\u4e49\u7684\u65b9\u5f0f\u6765\u8bbf\u95ee\u548c\u4fee\u6539\u6570\u636e\u3002\u6b64\u5916\uff0cPandas \u5229\u7528\u5f3a\u5927\u7684\u7d22\u5f15\u548c\u5206\u5c42\u6807\u7b7e\u529f\u80fd\u6765\u4fc3\u8fdb\u6570\u636e\u5bf9\u9f50\u548c\u64cd\u4f5c\u3002<\/p>\n<h2>\u718a\u732b\u7684\u4e3b\u8981\u7279\u5f81\u5206\u6790\u3002<\/h2>\n<p>Pandas \u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u548c\u65b9\u6cd5\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u9ad8\u6548\u5730\u6267\u884c\u5404\u79cd\u6570\u636e\u5206\u6790\u4efb\u52a1\u3002\u4e00\u4e9b\u4e3b\u8981\u529f\u80fd\u53ca\u5176\u4f18\u70b9\u5982\u4e0b\uff1a<\/p>\n<ol>\n<li>\n<p>\u6570\u636e\u5bf9\u9f50\u548c\u5904\u7406\u4e22\u5931\u6570\u636e\uff1a<\/p>\n<ul>\n<li>\u786e\u4fdd\u8de8\u591a\u4e2a\u7cfb\u5217\u548c\u6570\u636e\u5e27\u7684\u4e00\u81f4\u548c\u540c\u6b65\u7684\u6570\u636e\u64cd\u4f5c\u3002<\/li>\n<li>\u7b80\u5316\u5904\u7406\u4e22\u5931\u6216\u4e0d\u5b8c\u6574\u6570\u636e\u7684\u8fc7\u7a0b\uff0c\u51cf\u5c11\u5206\u6790\u8fc7\u7a0b\u4e2d\u7684\u6570\u636e\u4e22\u5931\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u6570\u636e\u8fc7\u6ee4\u548c\u5207\u7247\uff1a<\/p>\n<ul>\n<li>\u4f7f\u7528\u6237\u80fd\u591f\u6839\u636e\u5404\u79cd\u6761\u4ef6\u63d0\u53d6\u7279\u5b9a\u7684\u6570\u636e\u5b50\u96c6\u3002<\/li>\n<li>\u901a\u8fc7\u5173\u6ce8\u76f8\u5173\u6570\u636e\u6bb5\u6765\u4fc3\u8fdb\u6570\u636e\u63a2\u7d22\u548c\u5047\u8bbe\u68c0\u9a8c\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u6570\u636e\u6e05\u7406\u548c\u8f6c\u6362\uff1a<\/p>\n<ul>\n<li>\u901a\u8fc7\u63d0\u4f9b\u5e7f\u6cdb\u7684\u6570\u636e\u6e05\u7406\u529f\u80fd\u7b80\u5316\u6570\u636e\u9884\u5904\u7406\u5de5\u4f5c\u6d41\u7a0b\u3002<\/li>\n<li>\u63d0\u9ad8\u4e0b\u6e38\u5206\u6790\u548c\u5efa\u6a21\u7684\u6570\u636e\u8d28\u91cf\u548c\u51c6\u786e\u6027\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u5206\u7ec4\u548c\u805a\u5408\uff1a<\/p>\n<ul>\n<li>\u5141\u8bb8\u7528\u6237\u6709\u6548\u5730\u6c47\u603b\u6570\u636e\u5e76\u8ba1\u7b97\u805a\u5408\u7edf\u8ba1\u6570\u636e\u3002<\/li>\n<li>\u652f\u6301\u5bcc\u6709\u6d1e\u5bdf\u529b\u7684\u6570\u636e\u6c47\u603b\u548c\u6a21\u5f0f\u53d1\u73b0\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u5408\u5e76\u548c\u8fde\u63a5\u6570\u636e\uff1a<\/p>\n<ul>\n<li>\u7b80\u5316\u57fa\u4e8e\u516c\u5171\u952e\u6216\u5217\u7684\u591a\u4e2a\u6570\u636e\u96c6\u7684\u96c6\u6210\u3002<\/li>\n<li>\u901a\u8fc7\u7ec4\u5408\u6765\u81ea\u4e0d\u540c\u6765\u6e90\u7684\u4fe1\u606f\u6765\u5b9e\u73b0\u5168\u9762\u7684\u6570\u636e\u5206\u6790\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u65f6\u95f4\u5e8f\u5217\u529f\u80fd\uff1a<\/p>\n<ul>\n<li>\u4fc3\u8fdb\u57fa\u4e8e\u65f6\u95f4\u7684\u6570\u636e\u5206\u6790\u3001\u9884\u6d4b\u548c\u8d8b\u52bf\u8bc6\u522b\u3002<\/li>\n<li>\u589e\u5f3a\u6267\u884c\u4e0e\u65f6\u95f4\u76f8\u5173\u7684\u8ba1\u7b97\u548c\u6bd4\u8f83\u7684\u80fd\u529b\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h2>\u5927\u718a\u732b\u7684\u79cd\u7c7b\u53ca\u5176\u7279\u5f81<\/h2>\n<p>Pandas \u63d0\u4f9b\u4e24\u79cd\u4e3b\u8981\u6570\u636e\u7ed3\u6784\uff1a<\/p>\n<ol>\n<li>\n<p>\u7cfb\u5217\uff1a<\/p>\n<ul>\n<li>\u80fd\u591f\u4fdd\u5b58\u4efb\u4f55\u7c7b\u578b\u6570\u636e\uff08\u4f8b\u5982\u6574\u6570\u3001\u5b57\u7b26\u4e32\u3001\u6d6e\u70b9\u6570\uff09\u7684\u4e00\u7ef4\u6807\u8bb0\u6570\u7ec4\u3002<\/li>\n<li>Series \u4e2d\u7684\u6bcf\u4e2a\u5143\u7d20\u90fd\u4e0e\u4e00\u4e2a\u7d22\u5f15\u76f8\u5173\u8054\uff0c\u63d0\u4f9b\u5feb\u901f\u9ad8\u6548\u7684\u6570\u636e\u8bbf\u95ee\u3002<\/li>\n<li>\u975e\u5e38\u9002\u5408\u8868\u793a DataFrame \u4e2d\u7684\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3001\u5e8f\u5217\u6216\u5355\u5217\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u6570\u636e\u6846\uff1a<\/p>\n<ul>\n<li>\u5177\u6709\u884c\u548c\u5217\u7684\u4e8c\u7ef4\u6807\u8bb0\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8e\u7535\u5b50\u8868\u683c\u6216 SQL \u8868\u3002<\/li>\n<li>\u652f\u6301\u6bcf\u5217\u5f02\u6784\u6570\u636e\u7c7b\u578b\uff0c\u5bb9\u7eb3\u590d\u6742\u7684\u6570\u636e\u96c6\u3002<\/li>\n<li>\u63d0\u4f9b\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u3001\u8fc7\u6ee4\u548c\u805a\u5408\u529f\u80fd\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h2>Pandas \u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u95ee\u9898\u4ee5\u53ca\u4e0e\u4f7f\u7528\u76f8\u5173\u7684\u89e3\u51b3\u65b9\u6848\u3002<\/h2>\n<p>Pandas \u7528\u4e8e\u5404\u79cd\u5e94\u7528\u7a0b\u5e8f\u548c\u7528\u4f8b\uff1a<\/p>\n<ol>\n<li>\n<p>\u6570\u636e\u6e05\u7406\u548c\u9884\u5904\u7406\uff1a<\/p>\n<ul>\n<li>Pandas \u7b80\u5316\u4e86\u6e05\u7406\u548c\u8f6c\u6362\u6742\u4e71\u6570\u636e\u96c6\u7684\u8fc7\u7a0b\uff0c\u4f8b\u5982\u5904\u7406\u7f3a\u5931\u503c\u548c\u5f02\u5e38\u503c\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790 (EDA)\uff1a<\/p>\n<ul>\n<li>EDA \u6d89\u53ca\u4f7f\u7528 Pandas \u63a2\u7d22\u548c\u53ef\u89c6\u5316\u6570\u636e\uff0c\u5728\u6df1\u5165\u5206\u6790\u4e4b\u524d\u8bc6\u522b\u6a21\u5f0f\u548c\u5173\u7cfb\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u6570\u636e\u6574\u7406\u548c\u8f6c\u6362\uff1a<\/p>\n<ul>\n<li>Pandas \u80fd\u591f\u91cd\u5851\u548c\u91cd\u65b0\u683c\u5f0f\u5316\u6570\u636e\uff0c\u4e3a\u5efa\u6a21\u548c\u5206\u6790\u505a\u597d\u51c6\u5907\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u6570\u636e\u6c47\u603b\u548c\u62a5\u544a\uff1a<\/p>\n<ul>\n<li>Pandas \u5bf9\u4e8e\u603b\u7ed3\u548c\u805a\u5408\u6570\u636e\u4ee5\u751f\u6210\u62a5\u544a\u548c\u83b7\u5f97\u89c1\u89e3\u975e\u5e38\u6709\u7528\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u65f6\u95f4\u5e8f\u5217\u5206\u6790\uff1a<\/p>\n<ul>\n<li>Pandas\u652f\u6301\u5404\u79cd\u57fa\u4e8e\u65f6\u95f4\u7684\u64cd\u4f5c\uff0c\u9002\u5408\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u548c\u5206\u6790\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\u5e38\u89c1\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848\uff1a<\/p>\n<ol>\n<li>\n<p>\u5904\u7406\u7f3a\u5931\u6570\u636e\uff1a<\/p>\n<ul>\n<li>\u4f7f\u7528\u7c7b\u4f3c\u7684\u51fd\u6570 <code data-no-translation=\"\">dropna()<\/code> \u6216\u8005 <code data-no-translation=\"\">fillna()<\/code> \u5904\u7406\u6570\u636e\u96c6\u4e2d\u7684\u7f3a\u5931\u503c\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u5408\u5e76\u548c\u8fde\u63a5\u6570\u636e\uff1a<\/p>\n<ul>\n<li>\u91c7\u7528 <code data-no-translation=\"\">merge()<\/code> \u6216\u8005 <code data-no-translation=\"\">join()<\/code> \u6839\u636e\u516c\u5171\u952e\u6216\u5217\u7ec4\u5408\u591a\u4e2a\u6570\u636e\u96c6\u7684\u51fd\u6570\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u6570\u636e\u8fc7\u6ee4\u548c\u5207\u7247\uff1a<\/p>\n<ul>\n<li>\u5229\u7528\u5e26\u6709\u5e03\u5c14\u63a9\u7801\u7684\u6761\u4ef6\u7d22\u5f15\u6765\u8fc7\u6ee4\u548c\u63d0\u53d6\u7279\u5b9a\u7684\u6570\u636e\u5b50\u96c6\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u5206\u7ec4\u548c\u805a\u5408\uff1a<\/p>\n<ul>\n<li>\u4f7f\u7528 <code data-no-translation=\"\">groupby()<\/code> \u548c\u805a\u5408\u51fd\u6570\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u5e76\u5bf9\u7ec4\u6267\u884c\u64cd\u4f5c\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h2>\u4e3b\u8981\u7279\u70b9\u53ca\u4e0e\u540c\u7c7b\u672f\u8bed\u7684\u5176\u4ed6\u6bd4\u8f83<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u5f81<\/th>\n<th>\u718a\u732b<\/th>\n<th>\u6570\u503c\u6a21\u62df<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u6570\u636e\u7ed3\u6784<\/td>\n<td>\u7cfb\u5217\u3001\u6570\u636e\u6846<\/td>\n<td>\u591a\u7ef4\u6570\u7ec4\uff08ndarray\uff09<\/td>\n<\/tr>\n<tr>\n<td>\u4e3b\u8981\u7528\u9014<\/td>\n<td>\u6570\u636e\u5904\u7406\u3001\u5206\u6790<\/td>\n<td>\u6570\u503c\u8ba1\u7b97<\/td>\n<\/tr>\n<tr>\n<td>\u4e3b\u8981\u7279\u5f81<\/td>\n<td>\u6570\u636e\u5bf9\u9f50\u3001\u7f3a\u5931\u6570\u636e\u5904\u7406\u3001\u65f6\u95f4\u5e8f\u5217\u652f\u6301<\/td>\n<td>\u6570\u503c\u8fd0\u7b97\u3001\u6570\u5b66\u51fd\u6570<\/td>\n<\/tr>\n<tr>\n<td>\u8868\u73b0<\/td>\n<td>\u5927\u578b\u6570\u636e\u96c6\u7684\u4e2d\u7b49\u901f\u5ea6<\/td>\n<td>\u9ad8\u6027\u80fd\u6570\u503c\u8fd0\u7b97<\/td>\n<\/tr>\n<tr>\n<td>\u7075\u6d3b\u6027<\/td>\n<td>\u652f\u6301\u6df7\u5408\u6570\u636e\u7c7b\u578b\u548c\u5f02\u6784\u6570\u636e\u96c6<\/td>\n<td>\u4e13\u4e3a\u540c\u8d28\u6570\u503c\u6570\u636e\u800c\u8bbe\u8ba1<\/td>\n<\/tr>\n<tr>\n<td>\u5e94\u7528<\/td>\n<td>\u4e00\u822c\u6570\u636e\u5206\u6790<\/td>\n<td>\u79d1\u5b66\u8ba1\u7b97\u3001\u6570\u5b66\u4efb\u52a1<\/td>\n<\/tr>\n<tr>\n<td>\u7528\u6cd5<\/td>\n<td>\u6570\u636e\u6e05\u6d17\u3001EDA\u3001\u6570\u636e\u8f6c\u6362<\/td>\n<td>\u6570\u5b66\u8ba1\u7b97\u3001\u7ebf\u6027\u4ee3\u6570<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u4e0e\u718a\u732b\u76f8\u5173\u7684\u672a\u6765\u524d\u666f\u548c\u6280\u672f\u3002<\/h2>\n<p>\u968f\u7740\u6280\u672f\u548c\u6570\u636e\u79d1\u5b66\u7684\u4e0d\u65ad\u53d1\u5c55\uff0cPandas \u7684\u672a\u6765\u770b\u8d77\u6765\u5145\u6ee1\u5e0c\u671b\u3002\u4e00\u4e9b\u6f5c\u5728\u7684\u53d1\u5c55\u548c\u8d8b\u52bf\u5305\u62ec\uff1a<\/p>\n<ol>\n<li>\n<p>\u6027\u80fd\u6539\u8fdb\uff1a<\/p>\n<ul>\n<li>\u8fdb\u4e00\u6b65\u4f18\u5316\u548c\u5e76\u884c\u5316\uff0c\u4ee5\u6709\u6548\u5904\u7406\u66f4\u5927\u7684\u6570\u636e\u96c6\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u4e0e\u4eba\u5de5\u667a\u80fd\u548c\u673a\u5668\u5b66\u4e60\u96c6\u6210\uff1a<\/p>\n<ul>\n<li>\u4e0e\u673a\u5668\u5b66\u4e60\u5e93\u65e0\u7f1d\u96c6\u6210\uff0c\u4ee5\u7b80\u5316\u6570\u636e\u9884\u5904\u7406\u548c\u5efa\u6a21\u6d41\u7a0b\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u589e\u5f3a\u7684\u53ef\u89c6\u5316\u529f\u80fd\uff1a<\/p>\n<ul>\n<li>\u4e0e\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\u96c6\u6210\u4ee5\u5b9e\u73b0\u4ea4\u4e92\u5f0f\u6570\u636e\u63a2\u7d22\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u57fa\u4e8e\u4e91\u7684\u89e3\u51b3\u65b9\u6848\uff1a<\/p>\n<ul>\n<li>\u4e0e\u4e91\u5e73\u53f0\u96c6\u6210\uff0c\u5b9e\u73b0\u53ef\u6269\u5c55\u7684\u6570\u636e\u5206\u6790\u548c\u534f\u4f5c\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h2>\u5982\u4f55\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u5c06\u5176\u4e0e Pandas \u5173\u8054\u3002<\/h2>\n<p>\u4ee3\u7406\u670d\u52a1\u5668\u548c Pandas \u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5173\u8054\uff0c\u7279\u522b\u662f\u5728\u5904\u7406\u7f51\u7edc\u6293\u53d6\u548c\u6570\u636e\u63d0\u53d6\u4efb\u52a1\u65f6\u3002\u4ee3\u7406\u670d\u52a1\u5668\u5145\u5f53\u5ba2\u6237\u7aef\uff08\u7f51\u7edc\u6293\u53d6\u5de5\u5177\uff09\u548c\u6258\u7ba1\u88ab\u6293\u53d6\u7f51\u7ad9\u7684\u670d\u52a1\u5668\u4e4b\u95f4\u7684\u4e2d\u4ecb\u3002\u901a\u8fc7\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\uff0c\u7f51\u7edc\u6293\u53d6\u5de5\u5177\u53ef\u4ee5\u5c06\u8bf7\u6c42\u5206\u53d1\u5230\u591a\u4e2a IP \u5730\u5740\uff0c\u4ece\u800c\u964d\u4f4e\u88ab\u65bd\u52a0\u8bbf\u95ee\u9650\u5236\u7684\u7f51\u7ad9\u963b\u6b62\u7684\u98ce\u9669\u3002<\/p>\n<p>\u5728 Pandas \u7684\u80cc\u666f\u4e0b\uff0c\u7f51\u7edc\u722c\u866b\u53ef\u4ee5\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u540c\u65f6\u4ece\u591a\u4e2a\u6765\u6e90\u83b7\u53d6\u6570\u636e\uff0c\u4ece\u800c\u63d0\u9ad8\u6570\u636e\u6536\u96c6\u7684\u6548\u7387\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u5b9e\u65bd\u4ee3\u7406\u8f6e\u6362\uff0c\u4ee5\u9632\u6b62\u7f51\u7ad9\u57fa\u4e8e IP \u7684\u5c01\u9501\u548c\u8bbf\u95ee\u9650\u5236\u3002<\/p>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<p>\u6709\u5173 Pandas \u7684\u66f4\u591a\u4fe1\u606f\uff0c\u60a8\u53ef\u4ee5\u53c2\u8003\u4ee5\u4e0b\u8d44\u6e90\uff1a<\/p>\n<ul>\n<li><a href=\"https:\/\/pandas.pydata.org\/docs\/\" target=\"_new\" rel=\"noopener nofollow\">Pandas \u5b98\u65b9\u6587\u6863<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/pandas-dev\/pandas\" target=\"_new\" rel=\"noopener nofollow\">Pandas GitHub \u5b58\u50a8\u5e93<\/a><\/li>\n<li><a href=\"https:\/\/pandas.pydata.org\/pandas-docs\/stable\/getting_started\/index.html\" target=\"_new\" rel=\"noopener nofollow\">Pandas \u6559\u7a0b\u548c\u6307\u5357<\/a><\/li>\n<li><a href=\"https:\/\/stackoverflow.com\/questions\/tagged\/pandas\" target=\"_new\" rel=\"noopener nofollow\">Stack Overflow \u4e0a\u7684 Pandas<\/a> \uff08\u7528\u4e8e\u793e\u533a\u95ee\u7b54\uff09<\/li>\n<li><a href=\"https:\/\/www.datacamp.com\/community\/tutorials\/pandas-tutorial-dataframe-python\" target=\"_new\" rel=\"noopener nofollow\">DataCamp Pandas \u6559\u7a0b<\/a><\/li>\n<\/ul>\n<p>\u603b\u4e4b\uff0cPandas \u56e0\u5176\u76f4\u89c2\u7684\u6570\u636e\u64cd\u4f5c\u80fd\u529b\u548c\u5e7f\u6cdb\u7684\u529f\u80fd\u800c\u6210\u4e3a\u6570\u636e\u5206\u6790\u5e08\u548c\u79d1\u5b66\u5bb6\u4e0d\u53ef\u6216\u7f3a\u7684\u5de5\u5177\u3002\u5b83\u7684\u4e0d\u65ad\u53d1\u5c55\u4ee5\u53ca\u4e0e\u5c16\u7aef\u6280\u672f\u7684\u878d\u5408\u786e\u4fdd\u4e86\u5176\u5728\u672a\u6765\u6570\u636e\u5206\u6790\u548c\u6570\u636e\u9a71\u52a8\u51b3\u7b56\u4e2d\u7684\u76f8\u5173\u6027\u548c\u91cd\u8981\u6027\u3002\u65e0\u8bba\u60a8\u662f\u4e00\u4f4d\u6709\u62b1\u8d1f\u7684\u6570\u636e\u79d1\u5b66\u5bb6\u8fd8\u662f\u7ecf\u9a8c\u4e30\u5bcc\u7684\u7814\u7a76\u4eba\u5458\uff0cPandas \u90fd\u662f\u4e00\u7b14\u5b9d\u8d35\u7684\u8d44\u4ea7\uff0c\u53ef\u4ee5\u5e2e\u52a9\u60a8\u91ca\u653e\u6570\u636e\u4e2d\u9690\u85cf\u7684\u6f5c\u529b\u3002<\/p>","protected":false},"featured_media":469107,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478331","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Pandas: A Comprehensive Guide<\/mark>","faq_items":[{"question":"What is Pandas and why is it popular for data analysis?","answer":"<p>Pandas is an open-source Python library that provides powerful tools for data manipulation and analysis. It is popular because of its ease of use, flexibility, and efficient handling of structured data. With Pandas, data scientists and analysts can perform various data tasks, such as cleaning, filtering, grouping, and aggregation, with just a few lines of code.<\/p>"},{"question":"Who created Pandas and when was it first released?","answer":"<p>Pandas was created by Wes McKinney, a financial analyst at AQR Capital Management, in 2008. The first version of Pandas was released in January 2009.<\/p>"},{"question":"What are the key data structures in Pandas?","answer":"<p>Pandas offers two primary data structures: Series and DataFrame. Series is a one-dimensional labeled array, and DataFrame is a two-dimensional labeled data structure with rows and columns, similar to a spreadsheet.<\/p>"},{"question":"How does Pandas handle missing data?","answer":"<p>Pandas provides efficient tools to handle missing data. Users can use functions like <code>dropna()<\/code> or <code>fillna()<\/code> to remove or fill missing values in the dataset, ensuring data integrity during analysis.<\/p>"},{"question":"What are the key features of Pandas?","answer":"<p>Pandas offers several essential features, including data alignment, missing data handling, data filtering and slicing, data cleaning and transformation, grouping and aggregation, merging and joining data, and time series functionality.<\/p>"},{"question":"How can Pandas be used for web scraping?","answer":"<p>Proxy servers can be associated with Pandas for web scraping tasks. By using proxy servers, web scrapers can distribute their requests across multiple IP addresses, reducing the risk of being blocked by websites that impose access restrictions.<\/p>"},{"question":"What are the future perspectives of Pandas?","answer":"<p>In the future, Pandas is expected to witness performance improvements, better integration with AI and ML libraries, enhanced visualization capabilities, and potential integration with cloud platforms for scalable data analysis.<\/p>"},{"question":"Where can I find more information about Pandas?","answer":"<p>For more information about Pandas, you can refer to the official Pandas documentation, GitHub repository, tutorials, and guides available on the Pandas website. Additionally, you can explore the Pandas-related discussions on Stack Overflow and DataCamp's Pandas tutorial for in-depth learning.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/478331","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/478331\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/469107"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=478331"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}