{"id":20128,"date":"2026-01-22T13:23:39","date_gmt":"2026-01-22T05:23:39","guid":{"rendered":"https:\/\/92it.top\/?p=20128"},"modified":"2026-01-22T13:23:39","modified_gmt":"2026-01-22T05:23:39","slug":"%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0_pandas","status":"publish","type":"post","link":"https:\/\/92it.top\/?p=20128","title":{"rendered":"\u673a\u5668\u5b66\u4e60_Pandas"},"content":{"rendered":"\n

\u524d\u8a00\u200b\ud83d\udd16<\/strong><\/p>\n\n\n\n

\n\n\n\n

Pandas \u662f\u9488\u5bf9\u00a0Python\u00a0\u7f16\u7a0b\u8bed\u8a00\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u548c\u6570\u636e\u5206\u6790\u7684\u70ed\u95e8\u8f6f\u4ef6\u5e93\u3002<\/p>\n\n\n\n

Pandas \u662f\u57fa\u4e8e Python \u6784\u5efa\u7684\u7528\u4e8e\u6570\u636e\u64cd\u4f5c\u548c\u6570\u636e\u5206\u6790\u7684\u8f6f\u4ef6\u5e93\u3002pandas \u5e93\u63d0\u4f9b\u5404\u79cd\u6570\u636e\u7ed3\u6784\uff0c\u4e13\u95e8\u7528\u4e8e\u901a\u8fc7\u7b80\u5316\u7684 Python API \u6765\u5904\u7406\u8868\u683c\u6570\u636e\u96c6\u3002pandas \u662f\u5bf9 Python \u7684\u6269\u5c55\uff0c\u53ef\u7528\u4e8e\u5904\u7406\u548c\u64cd\u4f5c\u8868\u683c\u6570\u636e\uff0c\u4ece\u800c\u9ad8\u6548\u5bf9\u6570\u636e\u96c6\u6267\u884c\u52a0\u8f7d\u3001\u5bf9\u9f50\u3001\u5408\u5e76\u548c\u8f6c\u6362\u7b49\u64cd\u4f5c\u3002<\/p>\n\n\n\n

\u4f5c\u4e3a\u4e00\u4e2a\u6570\u636e\u5206\u6790\u5de5\u5177\uff0cpandas \u4e4b\u6240\u4ee5\u53d7\u5230\u6b22\u8fce\uff0c\u4e3b\u8981\u5f97\u76ca\u4e8e\u5b83\u7684\u591a\u529f\u80fd\u6027\u548c\u9ad8\u6548\u7684\u6027\u80fd\u3002\u201cpandas\u201d\u7684\u540d\u79f0\u6e90\u81ea\u672f\u8bed\u201cpanel data\u201d\uff0c\u7528\u4e8e\u63cf\u8ff0\u5305\u542b\u591a\u4e2a\u65f6\u95f4\u6bb5\u89c2\u5bdf\u7ed3\u679c\u7684\u6570\u636e\u96c6\uff0c\u5f3a\u8c03\u5176\u4e13\u6ce8\u4e8e\u5904\u7406\u73b0\u5b9e\u4e16\u754c\u6570\u636e\u96c6\u7684\u591a\u6837\u5316\u6570\u636e\u7ed3\u6784\u3002<\/p>\n\n\n\n

Pandas Python API \u652f\u6301\u8868\u683c\u3001\u77e9\u9635\u548c\u65f6\u95f4\u5e8f\u5217\u7b49\u7ed3\u6784\u5316\u6570\u636e\u683c\u5f0f\uff0c\u63d0\u4f9b\u5404\u79cd\u5de5\u5177\u6765\u5904\u7406\u6742\u4e71\u6216\u539f\u59cb\u6570\u636e\u96c6\uff0c\u5c06\u5176\u6574\u7406\u4e3a\u7b80\u660e\u7684\u7ed3\u6784\u5316\u683c\u5f0f\uff0c\u4ee5\u4fbf\u8fdb\u884c\u5206\u6790\u3002\u4e3a\u63d0\u9ad8\u6027\u80fd\uff0c\u4f1a\u5728\u540e\u7aef\u6e90\u4ee3\u7801\u4e2d\u4f7f\u7528 C \u6216 Cython \u6765\u6267\u884c\u8ba1\u7b97\u5bc6\u96c6\u578b\u64cd\u4f5c\u3002pandas \u5e93\u672c\u8d28\u4e0a\u4e0d\u652f\u6301\u591a\u7ebf\u7a0b\uff0c\u8fd9\u9650\u5236\u4e86\u5176\u5229\u7528\u73b0\u4ee3\u5316\u591a\u6838\u5e73\u53f0\u7684\u80fd\u529b\uff0c\u5e76\u4e14\u65e0\u6cd5\u9ad8\u6548\u5904\u7406\u5927\u91cf\u6570\u636e\u3002\u4f46\u662f\uff0cPython \u751f\u6001\u7cfb\u7edf\u4e2d\u7684\u65b0\u5e93\u548c\u6269\u5c55\u7a0b\u5e8f\u6709\u52a9\u4e8e\u6253\u7834\u8fd9\u4e00\u9650\u5236\u3002<\/p>\n\n\n\n

Pandas \u5e93\u53ef\u4e0e\u66f4\u5e7f\u6cdb\u7684 Python \u6570\u636e\u5206\u6790\u751f\u6001\u7cfb\u7edf\u5185\u7684\u5176\u4ed6\u79d1\u5b66\u5de5\u5177\u76f8\u96c6\u6210\u3002<\/p>\n\n\n\n

\u3000\u3000<\/p>\n\n\n\n

Pandas \u7684\u5de5\u4f5c\u539f\u7406\ud83d\udd16<\/strong><\/p>\n\n\n\n

\n\n\n\n

pandas \u5f00\u6e90\u5e93\u7684\u6838\u5fc3\u662f\u7528\u4e8e\u5904\u7406\u8868\u683c\u548c\u7edf\u8ba1\u6570\u636e\u7684 DataFrame \u6570\u636e\u7ed3\u6784\u3002pandas DataFrame \u4e3a\u4e8c\u7ef4\u6570\u7ec4\u5f0f\u6570\u636e\u8868\uff0c\u5176\u4e2d\u6bcf\u5217\u4ee3\u8868\u7279\u5b9a\u53d8\u91cf\u503c\uff0c\u6bcf\u884c\u5305\u542b\u4e00\u7ec4\u4e0e\u8fd9\u4e9b\u53d8\u91cf\u5bf9\u5e94\u7684\u503c\u3002DataFrame \u4e2d\u5b58\u50a8\u7684\u6570\u636e\u53ef\u5305\u62ec\u6570\u5b57\u3001\u7c7b\u522b\u6216\u6587\u672c\u7b49\u7c7b\u578b\uff0c\u4ee5\u4fbf pandas \u64cd\u4f5c\u548c\u5904\u7406\u591a\u6837\u6027\u6570\u636e\u96c6\u3002<\/p>\n\n\n\n

pandas \u652f\u6301\u5bfc\u5165\u548c\u5bfc\u51fa CSV\u3001SQL \u548c\u7535\u5b50\u8868\u683c\u7b49\u5404\u79cd\u6587\u4ef6\u683c\u5f0f\u7684\u6570\u636e\u96c6\u3002\u8fd9\u4e9b\u64cd\u4f5c\u4e0e\u5176\u6570\u636e\u64cd\u4f5c\u529f\u80fd\u76f8\u7ed3\u5408\uff0c\u6709\u52a9\u4e8e pandas \u6e05\u7406\u3001\u8c03\u6574\u5e76\u5206\u6790\u8868\u683c\u548c\u7edf\u8ba1\u6570\u636e\u3002<\/p>\n\n\n\n

\u4ece\u6839\u672c\u4e0a\u8bf4\uff0cDataFrame \u662f pandas \u7684\u4e3b\u5e72\uff0c\u53ef\u5e2e\u52a9\u7528\u6237\u9ad8\u6548\u7ba1\u7406\u548c\u5206\u6790\u7ed3\u6784\u5316\u6570\u636e\u96c6 \u2014 \u4ece\u5bfc\u5165\u548c\u5bfc\u51fa\u539f\u59cb\u6570\u636e\uff0c\u5230\u6267\u884c\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u7b49\u7684\u9ad8\u7ea7\u6570\u636e\u64cd\u4f5c\u4efb\u52a1\u3002<\/p>\n\n\n

\n
\"\"<\/figure><\/div>\n\n\n

pandas \u652f\u6301\u5bfc\u5165\u548c\u5bfc\u51fa\u5404\u79cd\u4e0d\u540c\u683c\u5f0f\u7684\u8868\u683c\u6570\u636e\uff0c\u5982 CSV\u3001SQL \u548c\u7535\u5b50\u8868\u683c\u6587\u4ef6\u3002<\/p>\n\n\n

\n
\"\"<\/figure><\/div>\n\n\n

pandas \u8fd8\u652f\u6301\u5404\u79cd\u6570\u636e\u5904\u7406\u64cd\u4f5c\u548c\u6570\u636e\u6e05\u6d17\u529f\u80fd\uff0c\u5305\u62ec\u9009\u62e9\u5b50\u96c6\u3001\u521b\u5efa\u884d\u751f\u5217\u3001\u6392\u5e8f\u3001\u8054\u63a5\u3001\u586b\u5145\u3001\u66ff\u6362\u3001\u6c47\u603b\u7edf\u8ba1\u548c\u7ed8\u56fe\u3002<\/p>\n\n\n

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\"\"<\/figure><\/div>\n\n
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\"\"<\/figure><\/div>\n\n\n

\u3000\u3000<\/p>\n\n\n\n

Pandas \u6570\u636e\u7ed3\u6784\uff1aSeries\u00a0\u548c\u00a0DataFrame\ud83d\udd16<\/strong><\/p>\n\n\n\n

\n\n\n\n

Pandas \u4e3b\u8981\u5f15\u5165\u4e86\u4e24\u79cd\u65b0\u7684\u6570\u636e\u7ed3\u6784\uff1aSeries<\/strong> \u548c DataFrame<\/strong>\u3002<\/p>\n\n\n\n

    \n
  • Series<\/strong>\uff1a \u7c7b\u4f3c\u4e8e\u4e00\u7ef4\u6570\u7ec4\u6216\u5217\u8868\uff0c\u662f\u7531\u4e00\u7ec4\u6570\u636e\u4ee5\u53ca\u4e0e\u4e4b\u76f8\u5173\u7684\u6570\u636e\u6807\u7b7e\uff08\u7d22\u5f15\uff09\u6784\u6210\u3002Series \u53ef\u4ee5\u770b\u4f5c\u662f DataFrame \u4e2d\u7684\u4e00\u5217\uff0c\u4e5f\u53ef\u4ee5\u662f\u5355\u72ec\u5b58\u5728\u7684\u4e00\u7ef4\u6570\u636e\u7ed3\u6784\u3002<\/li>\n<\/ul>\n\n\n
    \n
    \"\"<\/figure><\/div>\n\n\n
      \n
    • DataFrame<\/strong>\uff1a \u7c7b\u4f3c\u4e8e\u4e00\u4e2a\u4e8c\u7ef4\u8868\u683c\uff0c\u5b83\u662f Pandas \u4e2d\u6700\u91cd\u8981\u7684\u6570\u636e\u7ed3\u6784\u3002DataFrame \u53ef\u4ee5\u770b\u4f5c\u662f\u7531\u591a\u4e2a Series \u6309\u5217\u6392\u5217\u6784\u6210\u7684\u8868\u683c\uff0c\u5b83\u65e2\u6709\u884c\u7d22\u5f15\u4e5f\u6709\u5217\u7d22\u5f15\uff0c\u56e0\u6b64\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u884c\u5217\u9009\u62e9\u3001\u8fc7\u6ee4\u3001\u5408\u5e76\u7b49\u64cd\u4f5c\u3002<\/li>\n<\/ul>\n\n\n
      \n
      \"\"<\/figure><\/div>\n\n\n

      DataFrame \u53ef\u89c6\u4e3a\u7531\u591a\u4e2a Series \u7ec4\u6210\u7684\u6570\u636e\u7ed3\u6784\uff1a<\/p>\n\n\n

      \n
      \"\"<\/figure><\/div>\n\n\n

      \u4e0b\u9762\u8fd9\u5f20\u56fe\u5c55\u793a\u4e86\u4e24\u4e2a Series \u5bf9\u8c61\u76f8\u52a0\u5f97\u5230\u4e00\u4e2a DataFrame \u5bf9\u8c61\uff1a<\/p>\n\n\n

      \n
      \"\"<\/figure><\/div>\n\n\n

      DataFrame \u7531 Index\u3001Key\u3001Value \u7ec4\u6210\uff1a<\/p>\n\n\n

      \n
      \"\"<\/figure><\/div>\n\n\n
      import pandas as pd\n\n# \u521b\u5efa\u4e24\u4e2aSeries\u5bf9\u8c61\nseries_apples = pd.Series([1, 3, 7, 4])\nseries_bananas = pd.Series([2, 6, 3, 5])\n\n# \u5c06\u4e24\u4e2aSeries\u5bf9\u8c61\u76f8\u52a0\uff0c\u5f97\u5230DataFrame\uff0c\u5e76\u6307\u5b9a\u5217\u540d\ndf = pd.DataFrame({ 'Apples': series_apples, 'Bananas': series_bananas })\n\n# \u663e\u793aDataFrame\nprint(df)<\/pre>\n\n\n\n
      \u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n\n\n\n
         Apples  Bananas\n0       1        2\n1       3        6\n2       7        3\n3       4        5<\/pre>\n\n\n\n
      \u3000\u3000<\/p>\n\n\n\n
      Pandas \u7279\u70b9\ud83d\udd16<\/strong><\/p>\n\n\n\n
      \n\n\n\n
      \u9ad8\u6548\u7684\u6570\u636e\u7ed3\u6784\uff1a<\/p>\n\n\n\n
        \n
      • Series<\/strong>\uff1a\u4e00\u7ef4\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8e\u5217\u8868\uff08List\uff09\uff0c\u4f46\u62e5\u6709\u66f4\u5f3a\u7684\u529f\u80fd\uff0c\u652f\u6301\u7d22\u5f15\u3002<\/li>\n\n\n\n
      • DataFrame<\/strong>\uff1a\u4e8c\u7ef4\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8e\u8868\u683c\u6216\u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\u8868\uff0c\u884c\u548c\u5217\u90fd\u5177\u6709\u6807\u7b7e\uff08\u7d22\u5f15\uff09\u3002<\/li>\n<\/ul>\n\n\n\n
        \u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406\uff1a<\/p>\n\n\n\n
          \n
        • Pandas \u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u6765\u5904\u7406\u7f3a\u5931\u503c\u3001\u91cd\u590d\u6570\u636e\u3001\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u3001\u5b57\u7b26\u4e32\u64cd\u4f5c\u7b49\uff0c\u5e2e\u52a9\u7528\u6237\u8f7b\u677e\u6e05\u7406\u548c\u8f6c\u6362\u6570\u636e\u3002<\/li>\n<\/ul>\n\n\n\n
          \u6570\u636e\u64cd\u4f5c\u4e0e\u5206\u6790\uff1a<\/p>\n\n\n\n
            \n
          • \u652f\u6301\u9ad8\u6548\u7684\u6570\u636e\u9009\u62e9\u3001\u7b5b\u9009\u3001\u5207\u7247\uff0c\u6309\u6761\u4ef6\u63d0\u53d6\u6570\u636e\u3001\u5408\u5e76\u3001\u8fde\u63a5\u591a\u4e2a\u6570\u636e\u96c6\u3001\u6570\u636e\u5206\u7ec4\u3001\u6c47\u603b\u7edf\u8ba1\u7b49\u64cd\u4f5c\u3002<\/li>\n\n\n\n
          • \u53ef\u4ee5\u8fdb\u884c\u590d\u6742\u7684\u6570\u636e\u53d8\u6362\uff0c\u5982\u6570\u636e\u900f\u89c6\u8868\u3001\u4ea4\u53c9\u8868\u3001\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u7b49\u3002<\/li>\n<\/ul>\n\n\n\n
            \u6570\u636e\u8bfb\u53d6\u4e0e\u5bfc\u51fa\uff1a<\/p>\n\n\n\n
              \n
            • \u652f\u6301\u4ece\u5404\u79cd\u683c\u5f0f\u7684\u6570\u636e\u6e90\u8bfb\u53d6\u6570\u636e\uff0c\u5982 CSV\u3001Excel\u3001JSON\u3001SQL \u6570\u636e\u5e93\u7b49\u3002<\/li>\n\n\n\n
            • \u4e5f\u53ef\u4ee5\u5c06\u5904\u7406\u540e\u7684\u6570\u636e\u5bfc\u51fa\u4e3a\u4e0d\u540c\u683c\u5f0f\uff0c\u5982 CSV\u3001Excel \u7b49\u3002<\/li>\n<\/ul>\n\n\n\n
              \u6570\u636e\u53ef\u89c6\u5316\uff1a<\/p>\n\n\n\n
                \n
              • \u901a\u8fc7\u4e0e Matplotlib \u548c\u5176\u4ed6\u53ef\u89c6\u5316\u5de5\u5177\u7684\u96c6\u6210\uff0cPandas \u53ef\u4ee5\u5feb\u901f\u751f\u6210\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u5e38\u89c1\u56fe\u8868\u3002<\/li>\n<\/ul>\n\n\n\n
                \u65f6\u95f4\u5e8f\u5217\u5206\u6790\uff1a<\/p>\n\n\n\n
                  \n
                • \u652f\u6301\u5f3a\u5927\u7684\u65f6\u95f4\u5e8f\u5217\u5904\u7406\u529f\u80fd\uff0c\u5305\u62ec\u65e5\u671f\u7684\u89e3\u6790\u3001\u91cd\u91c7\u6837\u3001\u65f6\u533a\u8f6c\u6362\u7b49\u3002<\/li>\n<\/ul>\n\n\n\n
                  \u6027\u80fd\u4e0e\u4f18\u5316\uff1a<\/p>\n\n\n\n
                    \n
                  • Pandas \u4f18\u5316\u4e86\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\uff0c\u63d0\u4f9b\u9ad8\u6548\u7684\u5411\u91cf\u5316\u64cd\u4f5c\uff0c\u907f\u514d\u4e86\u4f7f\u7528 Python \u5faa\u73af\u5904\u7406\u6570\u636e\u7684\u4f4e\u6548\u3002<\/li>\n\n\n\n
                  • \u8fd8\u652f\u6301\u4e00\u4e9b\u5185\u5b58\u4f18\u5316\u6280\u672f\uff0c\u6bd4\u5982\u4f7f\u7528\u00a0category<\/code>\u00a0\u7c7b\u578b\u5904\u7406\u91cd\u590d\u7684\u6570\u636e\u3002<\/li>\n<\/ul>\n\n\n\n
                    \u3000\u3000<\/p>\n\n\n\n
                    Pandas \u5b89\u88c5\ud83d\udd16<\/strong><\/p>\n\n\n\n
                    \n\n\n\n
                    \u5b89\u88c5 pandas \u9700\u8981\u57fa\u7840\u73af\u5883\u662f Python\uff0cPandas \u662f\u4e00\u4e2a\u57fa\u4e8e Python \u7684\u5e93\uff0c\u56e0\u6b64\u4f60\u9700\u8981\u5148\u5b89\u88c5 Python\uff0c\u7136\u540e\u518d\u901a\u8fc7 Python \u7684\u5305\u7ba1\u7406\u5de5\u5177 pip<\/strong> \u5b89\u88c5 Pandas\u3002<\/p>\n\n\n\n
                    \u4f7f\u7528 pip \u5b89\u88c5 pandas:<\/p>\n\n\n\n
                    pip install pandas<\/pre>\n\n\n\n
                    \u5b89\u88c5\u6210\u529f\u540e\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u5bfc\u5165 pandas \u5305\u4f7f\u7528\uff1a<\/p>\n\n\n\n
                    import pandas<\/pre>\n\n\n\n
                    \u5bfc\u5165 pandas \u4e00\u822c\u4f7f\u7528\u522b\u540d pd<\/strong> \u6765\u4ee3\u66ff\uff1a<\/p>\n\n\n\n
                    import pandas as pd<\/pre>\n\n\n\n
                    \u4e00\u4e2a\u7b80\u5355\u7684 pandas \u5b9e\u4f8b\uff1a<\/p>\n\n\n\n
                    import pandas as pd\n\nmydataset = {\n  'sites': [\"Google\", \"Runoob\", \"Wiki\"],\n  'number': [1, 2, 3]\n}\n\nmyvar = pd.DataFrame(mydataset)\n\nprint(myvar)<\/pre>\n\n\n\n
                    \u6267\u884c\u4ee5\u4e0a\u4ee3\u7801\uff0c\u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n\n\n\n
                        sites  number\n0  Google       1\n1  Runoob       2\n2    Wiki       3<\/pre>\n\n\n\n
                    \u3000\u3000<\/p>\n\n\n\n
                    Pandas \u6570\u636e\u7ed3\u6784 – Series\ud83d\udd16<\/strong><\/p>\n\n\n\n
                    \n\n\n\n
                    Series \u662f Pandas \u4e2d\u7684\u4e00\u4e2a\u6838\u5fc3\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8e\u4e00\u4e2a\u4e00\u7ef4\u7684\u6570\u7ec4\uff0c\u5177\u6709\u6570\u636e\u548c\u7d22\u5f15\u3002<\/p>\n\n\n\n
                    Series \u53ef\u4ee5\u5b58\u50a8\u4efb\u4f55\u6570\u636e\u7c7b\u578b\uff08\u6574\u6570\u3001\u6d6e\u70b9\u6570\u3001\u5b57\u7b26\u4e32\u7b49\uff09\uff0c\u5e76\u901a\u8fc7\u6807\u7b7e\uff08\u7d22\u5f15\uff09\u6765\u8bbf\u95ee\u5143\u7d20\u3002<\/p>\n\n\n\n
                    Series \u7684\u6570\u636e\u7ed3\u6784\u662f\u975e\u5e38\u6709\u7528\u7684\uff0c\u56e0\u4e3a\u5b83\u53ef\u4ee5\u5904\u7406\u5404\u79cd\u6570\u636e\u7c7b\u578b\uff0c\u540c\u65f6\u4fdd\u6301\u4e86\u9ad8\u6548\u7684\u6570\u636e\u64cd\u4f5c\u80fd\u529b\uff0c\u6bd4\u5982\u53ef\u4ee5\u901a\u8fc7\u6807\u7b7e\u6765\u5feb\u901f\u8bbf\u95ee\u548c\u64cd\u4f5c\u6570\u636e\u3002<\/p>\n\n\n\n
                    \ud83d\udd39Series \u7279\u70b9\uff1a<\/strong><\/p>\n\n\n\n
                      \n
                    • \u4e00\u7ef4\u6570\u7ec4\uff1a<\/strong>Series \u4e2d\u7684\u6bcf\u4e2a\u5143\u7d20\u90fd\u6709\u4e00\u4e2a\u5bf9\u5e94\u7684\u7d22\u5f15\u503c\u3002<\/li>\n\n\n\n
                    • \u7d22\u5f15\uff1a<\/strong>\u00a0\u6bcf\u4e2a\u6570\u636e\u5143\u7d20\u90fd\u53ef\u4ee5\u901a\u8fc7\u6807\u7b7e\uff08\u7d22\u5f15\uff09\u6765\u8bbf\u95ee\uff0c\u9ed8\u8ba4\u60c5\u51b5\u4e0b\u7d22\u5f15\u662f\u4ece 0 \u5f00\u59cb\u7684\u6574\u6570\uff0c\u4f46\u4f60\u4e5f\u53ef\u4ee5\u81ea\u5b9a\u4e49\u7d22\u5f15\u3002<\/li>\n\n\n\n
                    • \u6570\u636e\u7c7b\u578b\uff1a<\/strong>\u00a0Series<\/code>\u00a0\u53ef\u4ee5\u5bb9\u7eb3\u4e0d\u540c\u6570\u636e\u7c7b\u578b\u7684\u5143\u7d20\uff0c\u5305\u62ec\u6574\u6570\u3001\u6d6e\u70b9\u6570\u3001\u5b57\u7b26\u4e32\u3001Python \u5bf9\u8c61\u7b49\u3002<\/li>\n\n\n\n
                    • \u5927\u5c0f\u4e0d\u53d8\u6027\uff1a<\/strong>Series \u7684\u5927\u5c0f\u5728\u521b\u5efa\u540e\u662f\u4e0d\u53d8\u7684\uff0c\u4f46\u53ef\u4ee5\u901a\u8fc7\u67d0\u4e9b\u64cd\u4f5c\uff08\u5982 append \u6216 delete\uff09\u6765\u6539\u53d8\u3002<\/li>\n\n\n\n
                    • \u64cd\u4f5c\uff1a<\/strong>Series \u652f\u6301\u5404\u79cd\u64cd\u4f5c\uff0c\u5982\u6570\u5b66\u8fd0\u7b97\u3001\u7edf\u8ba1\u5206\u6790\u3001\u5b57\u7b26\u4e32\u5904\u7406\u7b49\u3002<\/li>\n\n\n\n
                    • \u7f3a\u5931\u6570\u636e\uff1a<\/strong>Series \u53ef\u4ee5\u5305\u542b\u7f3a\u5931\u6570\u636e\uff0cPandas \u4f7f\u7528NaN\uff08Not a Number\uff09\u6765\u8868\u793a\u7f3a\u5931\u6216\u65e0\u503c\u3002<\/li>\n\n\n\n
                    • \u81ea\u52a8\u5bf9\u9f50\uff1a<\/strong>\u5f53\u5bf9\u591a\u4e2a Series \u8fdb\u884c\u8fd0\u7b97\u65f6\uff0cPandas \u4f1a\u81ea\u52a8\u6839\u636e\u7d22\u5f15\u5bf9\u9f50\u6570\u636e\uff0c\u8fd9\u4f7f\u5f97\u6570\u636e\u5904\u7406\u66f4\u52a0\u9ad8\u6548\u3002<\/li>\n<\/ul>\n\n\n\n
                      \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 Pandas \u5e93\u6765\u521b\u5efa\u4e00\u4e2a Series \u5bf9\u8c61\uff0c\u5e76\u4e14\u53ef\u4ee5\u4e3a\u5176\u6307\u5b9a\u7d22\u5f15\uff08Index\uff09\u3001\u540d\u79f0\uff08Name\uff09\u4ee5\u53ca\u503c\uff08Values\uff09\uff1a<\/p>\n\n\n
                      \n
                      \"\"<\/figure><\/div>\n\n\n
                      import pandas as pd\n\n# \u521b\u5efa\u4e00\u4e2aSeries\u5bf9\u8c61\uff0c\u6307\u5b9a\u540d\u79f0\u4e3a'A'\uff0c\u503c\u5206\u522b\u4e3a1, 2, 3, 4\n# \u9ed8\u8ba4\u7d22\u5f15\u4e3a0, 1, 2, 3\nseries = pd.Series([1, 2, 3, 4], name='A')\n\n# \u663e\u793aSeries\u5bf9\u8c61\nprint(series)\n\n# \u5982\u679c\u4f60\u60f3\u8981\u663e\u5f0f\u5730\u8bbe\u7f6e\u7d22\u5f15\uff0c\u53ef\u4ee5\u8fd9\u6837\u505a\uff1a\ncustom_index = [1, 2, 3, 4]  # \u81ea\u5b9a\u4e49\u7d22\u5f15\nseries_with_index = pd.Series([1, 2, 3, 4], index=custom_index, name='A')\n\n# \u663e\u793a\u5e26\u6709\u81ea\u5b9a\u4e49\u7d22\u5f15\u7684Series\u5bf9\u8c61\nprint(series_with_index)<\/pre>\n\n\n\n
                      \u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n\n\n\n
                      0    1\n1    2\n2    3\n3    4\nName: A, dtype: int64\n1    1\n2    2\n3    3\n4    4\nName: A, dtype: int64<\/pre>\n\n\n\n
                      Series \u662f Pandas \u4e2d\u7684\u4e00\u79cd\u57fa\u672c\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8e\u4e00\u7ef4\u6570\u7ec4\u6216\u5217\u8868\uff0c\u4f46\u5177\u6709\u6807\u7b7e\uff08\u7d22\u5f15\uff09\uff0c\u4f7f\u5f97\u6570\u636e\u5728\u5904\u7406\u548c\u5206\u6790\u65f6\u66f4\u5177\u7075\u6d3b\u6027\u3002<\/p>\n\n\n\n
                      \u4ee5\u4e0b\u662f\u5173\u4e8e Pandas \u4e2d\u7684 Series \u7684\u8be6\u7ec6\u4ecb\u7ecd\u3002<\/p>\n\n\n\n
                      \u3000\u3000<\/p>\n\n\n\n
                      \ud83d\udd39\u521b\u5efa Series<\/strong><\/p>\n\n\n\n
                      \u53ef\u4ee5\u4f7f\u7528 pd.Series() \u6784\u9020\u51fd\u6570\u521b\u5efa\u4e00\u4e2a Series \u5bf9\u8c61\uff0c\u4f20\u9012\u4e00\u4e2a\u6570\u636e\u6570\u7ec4\uff08\u53ef\u4ee5\u662f\u5217\u8868\u3001NumPy \u6570\u7ec4\u7b49\uff09\u548c\u4e00\u4e2a\u53ef\u9009\u7684\u7d22\u5f15\u6570\u7ec4\u3002<\/p>\n\n\n\n
                      pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)<\/pre>\n\n\n\n
                      \u53c2\u6570\u8bf4\u660e\uff1a<\/p>\n\n\n\n
                        \n
                      • data<\/code>\uff1aSeries \u7684\u6570\u636e\u90e8\u5206\uff0c\u53ef\u4ee5\u662f\u5217\u8868\u3001\u6570\u7ec4\u3001\u5b57\u5178\u3001\u6807\u91cf\u503c\u7b49\u3002\u5982\u679c\u4e0d\u63d0\u4f9b\u6b64\u53c2\u6570\uff0c\u5219\u521b\u5efa\u4e00\u4e2a\u7a7a\u7684 Series\u3002<\/li>\n\n\n\n
                      • index<\/code>\uff1aSeries \u7684\u7d22\u5f15\u90e8\u5206\uff0c\u7528\u4e8e\u5bf9\u6570\u636e\u8fdb\u884c\u6807\u8bb0\u3002\u53ef\u4ee5\u662f\u5217\u8868\u3001\u6570\u7ec4\u3001\u7d22\u5f15\u5bf9\u8c61\u7b49\u3002\u5982\u679c\u4e0d\u63d0\u4f9b\u6b64\u53c2\u6570\uff0c\u5219\u521b\u5efa\u4e00\u4e2a\u9ed8\u8ba4\u7684\u6574\u6570\u7d22\u5f15\u3002<\/li>\n\n\n\n
                      • dtype<\/code>\uff1a\u6307\u5b9a Series \u7684\u6570\u636e\u7c7b\u578b\u3002\u53ef\u4ee5\u662f NumPy \u7684\u6570\u636e\u7c7b\u578b\uff0c\u4f8b\u5982\u00a0np.int64<\/code>\u3001np.float64<\/code>\u00a0\u7b49\u3002\u5982\u679c\u4e0d\u63d0\u4f9b\u6b64\u53c2\u6570\uff0c\u5219\u6839\u636e\u6570\u636e\u81ea\u52a8\u63a8\u65ad\u6570\u636e\u7c7b\u578b\u3002<\/li>\n\n\n\n
                      • name<\/code>\uff1aSeries \u7684\u540d\u79f0\uff0c\u7528\u4e8e\u6807\u8bc6 Series \u5bf9\u8c61\u3002\u5982\u679c\u63d0\u4f9b\u4e86\u6b64\u53c2\u6570\uff0c\u5219\u521b\u5efa\u7684 Series \u5bf9\u8c61\u5c06\u5177\u6709\u6307\u5b9a\u7684\u540d\u79f0\u3002<\/li>\n\n\n\n
                      • copy<\/code>\uff1a\u662f\u5426\u590d\u5236\u6570\u636e\u3002\u9ed8\u8ba4\u4e3a False\uff0c\u8868\u793a\u4e0d\u590d\u5236\u6570\u636e\u3002\u5982\u679c\u8bbe\u7f6e\u4e3a True\uff0c\u5219\u590d\u5236\u8f93\u5165\u7684\u6570\u636e\u3002<\/li>\n\n\n\n
                      • fastpath<\/code>\uff1a\u662f\u5426\u542f\u7528\u5feb\u901f\u8def\u5f84\u3002\u9ed8\u8ba4\u4e3a False\u3002\u542f\u7528\u5feb\u901f\u8def\u5f84\u53ef\u80fd\u4f1a\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\u63d0\u9ad8\u6027\u80fd\u3002<\/li>\n<\/ul>\n\n\n\n
                        \u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684 Series \u5b9e\u4f8b\uff1a<\/p>\n\n\n\n
                        import pandas as pd\n\na = [1, 2, 3]\n\nmyvar = pd.Series(a)\n\nprint(myvar)<\/pre>\n\n\n\n
                        import pandas as pd\n\na = [1, 2, 3]\n\nmyvar = pd.Series(a)\n\nprint(myvar)<\/pre>\n\n\n\n
                        \u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n\n\n
                        \n
                        \"\"<\/figure><\/div>\n\n\n
                        \u4ece\u4e0a\u56fe\u53ef\u77e5\uff0c\u5982\u679c\u6ca1\u6709\u6307\u5b9a\u7d22\u5f15\uff0c\u7d22\u5f15\u503c\u5c31\u4ece 0 \u5f00\u59cb\uff0c\u6211\u4eec\u53ef\u4ee5\u6839\u636e\u7d22\u5f15\u503c\u8bfb\u53d6\u6570\u636e\uff1a<\/p>\n\n\n\n
                        import pandas as pd\n\na = [1, 2, 3]\n\nmyvar = pd.Series(a)\n\nprint(myvar[1])<\/pre>\n\n\n\n
                        \u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n\n\n\n
                        2<\/pre>\n\n\n\n
                        \u6211\u4eec\u53ef\u4ee5\u6307\u5b9a\u7d22\u5f15\u503c\uff0c\u5982\u4e0b\u5b9e\u4f8b\uff1a<\/p>\n\n\n\n
                        import pandas as pd\n\na = [\"Google\", \"Runoob\", \"Wiki\"]\n\nmyvar = pd.Series(a, index = [\"x\", \"y\", \"z\"])\n\nprint(myvar)<\/pre>\n\n\n\n
                        \u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n\n\n
                        \n
                        \"\"<\/figure><\/div>\n\n\n
                        \u6839\u636e\u7d22\u5f15\u503c\u8bfb\u53d6\u6570\u636e:<\/p>\n\n\n\n
                        import pandas as pd\n\na = [\"Google\", \"Runoob\", \"Wiki\"]\n\nmyvar = pd.Series(a, index = [\"x\", \"y\", \"z\"])\n\nprint(myvar[\"y\"])<\/pre>\n\n\n\n
                        \u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n\n\n\n
                        Runoob<\/pre>\n\n\n\n
                        \u6211\u4eec\u4e5f\u53ef\u4ee5\u4f7f\u7528 key\/value \u5bf9\u8c61\uff0c\u7c7b\u4f3c\u5b57\u5178\u6765\u521b\u5efa Series\uff1a<\/p>\n\n\n\n
                        import pandas as pd\n\nsites = {1: \"Google\", 2: \"Runoob\", 3: \"Wiki\"}\n\nmyvar = pd.Series(sites)\n\nprint(myvar)<\/pre>\n\n\n\n
                        \u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n\n\n
                        \n
                        \"\"<\/figure><\/div>\n\n\n
                        \u4ece\u4e0a\u56fe\u53ef\u77e5\uff0c\u5b57\u5178\u7684 key \u53d8\u6210\u4e86\u7d22\u5f15\u503c\u3002<\/p>\n\n\n\n
                        \u5982\u679c\u6211\u4eec\u53ea\u9700\u8981\u5b57\u5178\u4e2d\u7684\u4e00\u90e8\u5206\u6570\u636e\uff0c\u53ea\u9700\u8981\u6307\u5b9a\u9700\u8981\u6570\u636e\u7684\u7d22\u5f15\u5373\u53ef\uff0c\u5982\u4e0b\u5b9e\u4f8b\uff1a<\/p>\n\n\n\n
                        import pandas as pd\n\nsites = {1: \"Google\", 2: \"Runoob\", 3: \"Wiki\"}\n\nmyvar = pd.Series(sites, index = [1, 2])\n\nprint(myvar)<\/pre>\n\n\n\n
                        \u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n\n\n
                        \n
                        \"\"<\/figure><\/div>\n\n\n
                        \u8bbe\u7f6e Series \u540d\u79f0\u53c2\u6570\uff1a<\/p>\n\n\n\n
                        import pandas as pd\n\nsites = {1: \"Google\", 2: \"Runoob\", 3: \"Wiki\"}\n\nmyvar = pd.Series(sites, index = [1, 2], name=\"RUNOOB-Series-TEST\" )\n\nprint(myvar)<\/pre>\n\n\n
                        \n
                        \"\"<\/figure><\/div>\n\n\n
                        \u3000\u3000<\/p>\n\n\n\n
                        \ud83d\udd39Series \u65b9\u6cd5<\/strong><\/p>\n\n\n\n
                        \u4e0b\u9762\u662f Series \u4e2d\u4e00\u4e9b\u5e38\u7528\u7684\u65b9\u6cd5<\/p>\n\n\n\n
                        \u65b9\u6cd5\u540d\u79f0<\/strong><\/th>\u529f\u80fd\u63cf\u8ff0<\/strong><\/th><\/tr><\/thead>
                        index<\/code><\/td>\u83b7\u53d6 Series \u7684\u7d22\u5f15<\/td><\/tr>
                        values<\/code><\/td>\u83b7\u53d6 Series \u7684\u6570\u636e\u90e8\u5206\uff08\u8fd4\u56de NumPy \u6570\u7ec4\uff09<\/td><\/tr>
                        head(n)<\/code><\/td>\u8fd4\u56de Series \u7684\u524d n \u884c\uff08\u9ed8\u8ba4\u4e3a 5\uff09<\/td><\/tr>
                        tail(n)<\/code><\/td>\u8fd4\u56de Series \u7684\u540e n \u884c\uff08\u9ed8\u8ba4\u4e3a 5\uff09<\/td><\/tr>
                        dtype<\/code><\/td>\u8fd4\u56de Series \u4e2d\u6570\u636e\u7684\u7c7b\u578b<\/td><\/tr>
                        shape<\/code><\/td>\u8fd4\u56de Series \u7684\u5f62\u72b6\uff08\u884c\u6570\uff09<\/td><\/tr>
                        describe()<\/code><\/td>\u8fd4\u56de Series \u7684\u7edf\u8ba1\u63cf\u8ff0\uff08\u5982\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u6700\u5c0f\u503c\u7b49\uff09<\/td><\/tr>
                        isnull()<\/code><\/td>\u8fd4\u56de\u4e00\u4e2a\u5e03\u5c14 Series\uff0c\u8868\u793a\u6bcf\u4e2a\u5143\u7d20\u662f\u5426\u4e3a NaN<\/td><\/tr>
                        notnull()<\/code><\/td>\u8fd4\u56de\u4e00\u4e2a\u5e03\u5c14 Series\uff0c\u8868\u793a\u6bcf\u4e2a\u5143\u7d20\u662f\u5426\u4e0d\u662f NaN<\/td><\/tr>
                        unique()<\/code><\/td>\u8fd4\u56de Series \u4e2d\u7684\u552f\u4e00\u503c\uff08\u53bb\u91cd\uff09<\/td><\/tr>
                        value_counts()<\/code><\/td>\u8fd4\u56de Series \u4e2d\u6bcf\u4e2a\u552f\u4e00\u503c\u7684\u51fa\u73b0\u6b21\u6570<\/td><\/tr>
                        map(func)<\/code><\/td>\u5c06\u6307\u5b9a\u51fd\u6570\u5e94\u7528\u4e8e Series \u4e2d\u7684\u6bcf\u4e2a\u5143\u7d20<\/td><\/tr>
                        apply(func)<\/code><\/td>\u5c06\u6307\u5b9a\u51fd\u6570\u5e94\u7528\u4e8e Series \u4e2d\u7684\u6bcf\u4e2a\u5143\u7d20\uff0c\u5e38\u7528\u4e8e\u81ea\u5b9a\u4e49\u64cd\u4f5c<\/td><\/tr>
                        astype(dtype)<\/code><\/td>\u5c06 Series \u8f6c\u6362\u4e3a\u6307\u5b9a\u7684\u7c7b\u578b<\/td><\/tr>
                        sort_values()<\/code><\/td>\u5bf9 Series \u4e2d\u7684\u5143\u7d20\u8fdb\u884c\u6392\u5e8f\uff08\u6309\u503c\u6392\u5e8f\uff09<\/td><\/tr>
                        sort_index()<\/code><\/td>\u5bf9 Series \u7684\u7d22\u5f15\u8fdb\u884c\u6392\u5e8f<\/td><\/tr>
                        dropna()<\/code><\/td>\u5220\u9664 Series \u4e2d\u7684\u7f3a\u5931\u503c\uff08NaN\uff09<\/td><\/tr>
                        fillna(value)<\/code><\/td>\u586b\u5145 Series \u4e2d\u7684\u7f3a\u5931\u503c\uff08NaN\uff09<\/td><\/tr>
                        replace(to_replace, value)<\/code><\/td>\u66ff\u6362 Series \u4e2d\u6307\u5b9a\u7684\u503c<\/td><\/tr>
                        cumsum()<\/code><\/td>\u8fd4\u56de Series \u7684\u7d2f\u8ba1\u6c42\u548c<\/td><\/tr>
                        cumprod()<\/code><\/td>\u8fd4\u56de Series \u7684\u7d2f\u8ba1\u4e58\u79ef<\/td><\/tr>
                        shift(periods)<\/code><\/td>\u5c06 Series \u4e2d\u7684\u5143\u7d20\u6309\u6307\u5b9a\u7684\u6b65\u6570\u8fdb\u884c\u4f4d\u79fb<\/td><\/tr>
                        rank()<\/code><\/td>\u8fd4\u56de Series \u4e2d\u5143\u7d20\u7684\u6392\u540d<\/td><\/tr>
                        corr(other)<\/code><\/td>\u8ba1\u7b97 Series \u4e0e\u53e6\u4e00\u4e2a Series \u7684\u76f8\u5173\u6027\uff08\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570\uff09<\/td><\/tr>
                        cov(other)<\/code><\/td>\u8ba1\u7b97 Series \u4e0e\u53e6\u4e00\u4e2a Series \u7684\u534f\u65b9\u5dee<\/td><\/tr>
                        to_list()<\/code><\/td>\u5c06 Series \u8f6c\u6362\u4e3a Python \u5217\u8868<\/td><\/tr>
                        to_frame()<\/code><\/td>\u5c06 Series \u8f6c\u6362\u4e3a DataFrame<\/td><\/tr>
                        iloc[]<\/code><\/td>\u901a\u8fc7\u4f4d\u7f6e\u7d22\u5f15\u6765\u9009\u62e9\u6570\u636e<\/td><\/tr>
                        loc[]<\/code><\/td>\u901a\u8fc7\u6807\u7b7e\u7d22\u5f15\u6765\u9009\u62e9\u6570\u636e<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                        \u3000\u3000<\/p>\n\n\n\n
                        import pandas as pd\n\n# \u521b\u5efa Series\ndata = [1, 2, 3, 4, 5, 6]\nindex = ['a', 'b', 'c', 'd', 'e', 'f']\ns = pd.Series(data, index=index)\n\n# \u67e5\u770b\u57fa\u672c\u4fe1\u606f\nprint(\"\u7d22\u5f15\uff1a\", s.index)\nprint(\"\u6570\u636e\uff1a\", s.values)\nprint(\"\u6570\u636e\u7c7b\u578b\uff1a\", s.dtype)\nprint(\"\u524d\u4e24\u884c\u6570\u636e\uff1a\", s.head(2))\n\n# \u4f7f\u7528 map \u51fd\u6570\u5c06\u6bcf\u4e2a\u5143\u7d20\u52a0\u500d\ns_doubled = s.map(lambda x: x * 2)\nprint(\"\u5143\u7d20\u52a0\u500d\u540e\uff1a\", s_doubled)\n\n# \u8ba1\u7b97\u7d2f\u8ba1\u548c\ncumsum_s = s.cumsum()\nprint(\"\u7d2f\u8ba1\u6c42\u548c\uff1a\", cumsum_s)\n\n# \u67e5\u627e\u7f3a\u5931\u503c\uff08\u8fd9\u91cc\u6ca1\u6709\u7f3a\u5931\u503c\uff0c\u6240\u4ee5\u8fd4\u56de\u7684\u5168\u662f False\uff09\nprint(\"\u7f3a\u5931\u503c\u5224\u65ad\uff1a\", s.isnull())\n\n# \u6392\u5e8f\nsorted_s = s.sort_values()\nprint(\"\u6392\u5e8f\u540e\u7684 Series\uff1a\", sorted_s)<\/pre>\n\n\n\n
                        \u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n\n\n\n
                        \u7d22\u5f15\uff1a Index(['a', 'b', 'c', 'd', 'e', 'f'], dtype='object')\n\u6570\u636e\uff1a [1 2 3 4 5 6]\n\u6570\u636e\u7c7b\u578b\uff1a int64\n\u524d\u4e24\u884c\u6570\u636e\uff1a a    1\nb    2\ndtype: int64\n\u5143\u7d20\u52a0\u500d\u540e\uff1a a     2\nb     4\nc     6\nd     8\ne    10\nf    12\ndtype: int64\n\u7d2f\u8ba1\u6c42\u548c\uff1a a     1\nb     3\nc     6\nd    10\ne    15\nf    21\ndtype: int64\n\u7f3a\u5931\u503c\u5224\u65ad\uff1a a    False\nb    False\nc    False\nd    False\ne    False\nf    False\ndtype: bool\n\u6392\u5e8f\u540e\u7684 Series\uff1a a    1\nb    2\nc    3\nd    4\ne    5\nf    6\ndtype: int64<\/pre>\n\n\n\n
                        \u3000\u3000<\/p>\n\n\n\n
                        \ud83d\udd39\u66f4\u591a Series \u8bf4\u660e<\/strong><\/p>\n\n\n\n
                        \u4f7f\u7528\u5217\u8868\u3001\u5b57\u5178\u6216\u6570\u7ec4\u521b\u5efa\u4e00\u4e2a\u9ed8\u8ba4\u7d22\u5f15\u7684 Series\u3002<\/p>\n\n\n\n
                        # \u4f7f\u7528\u5217\u8868\u521b\u5efa Series\ns = pd.Series([1, 2, 3, 4])\n\n# \u4f7f\u7528 NumPy \u6570\u7ec4\u521b\u5efa Series\ns = pd.Series(np.array([1, 2, 3, 4]))\n\n# \u4f7f\u7528\u5b57\u5178\u521b\u5efa Series\ns = pd.Series({'a': 1, 'b': 2, 'c': 3, 'd': 4})\n<\/pre>\n\n\n\n
                        \u57fa\u672c\u64cd\u4f5c\uff1a<\/p>\n\n\n\n
                        # \u6307\u5b9a\u7d22\u5f15\u521b\u5efa Series\ns = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n\n# \u83b7\u53d6\u503c\nvalue = s[2]  # \u83b7\u53d6\u7d22\u5f15\u4e3a2\u7684\u503c\nprint(s['a'])  # \u8fd4\u56de\u7d22\u5f15\u6807\u7b7e 'a' \u5bf9\u5e94\u7684\u5143\u7d20\n\n# \u83b7\u53d6\u591a\u4e2a\u503c\nsubset = s[1:4]  # \u83b7\u53d6\u7d22\u5f15\u4e3a1\u52303\u7684\u503c\n\n# \u4f7f\u7528\u81ea\u5b9a\u4e49\u7d22\u5f15\nvalue = s['b']  # \u83b7\u53d6\u7d22\u5f15\u4e3a'b'\u7684\u503c\n\n# \u7d22\u5f15\u548c\u503c\u7684\u5bf9\u5e94\u5173\u7cfb\nfor index, value in s.items():\n    print(f\"Index: {index}, Value: {value}\")\n\n\n# \u4f7f\u7528\u5207\u7247\u8bed\u6cd5\u6765\u8bbf\u95ee Series \u7684\u4e00\u90e8\u5206\nprint(s['a':'c'])  # \u8fd4\u56de\u7d22\u5f15\u6807\u7b7e 'a' \u5230 'c' \u4e4b\u95f4\u7684\u5143\u7d20\nprint(s[:3])  # \u8fd4\u56de\u524d\u4e09\u4e2a\u5143\u7d20\n\n# \u4e3a\u7279\u5b9a\u7684\u7d22\u5f15\u6807\u7b7e\u8d4b\u503c\ns['a'] = 10  # \u5c06\u7d22\u5f15\u6807\u7b7e 'a' \u5bf9\u5e94\u7684\u5143\u7d20\u4fee\u6539\u4e3a 10\n\n# \u901a\u8fc7\u8d4b\u503c\u7ed9\u65b0\u7684\u7d22\u5f15\u6807\u7b7e\u6765\u6dfb\u52a0\u5143\u7d20\ns['e'] = 5  # \u5728 Series \u4e2d\u6dfb\u52a0\u4e00\u4e2a\u65b0\u7684\u5143\u7d20\uff0c\u7d22\u5f15\u6807\u7b7e\u4e3a 'e'\n\n# \u4f7f\u7528 del \u5220\u9664\u6307\u5b9a\u7d22\u5f15\u6807\u7b7e\u7684\u5143\u7d20\u3002\ndel s['a']  # \u5220\u9664\u7d22\u5f15\u6807\u7b7e 'a' \u5bf9\u5e94\u7684\u5143\u7d20\n\n# \u4f7f\u7528 drop \u65b9\u6cd5\u5220\u9664\u4e00\u4e2a\u6216\u591a\u4e2a\u7d22\u5f15\u6807\u7b7e\uff0c\u5e76\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684 Series\u3002\ns_dropped = s.drop(['b'])  # \u8fd4\u56de\u4e00\u4e2a\u5220\u9664\u4e86\u7d22\u5f15\u6807\u7b7e 'b' \u7684\u65b0 Series\n<\/pre>\n\n\n\n
                        \u57fa\u672c\u8fd0\u7b97\uff1a<\/p>\n\n\n\n
                        # \u7b97\u672f\u8fd0\u7b97\nresult = series * 2  # \u6240\u6709\u5143\u7d20\u4e58\u4ee52\n\n# \u8fc7\u6ee4\nfiltered_series = series[series > 2]  # \u9009\u62e9\u5927\u4e8e2\u7684\u5143\u7d20\n\n# \u6570\u5b66\u51fd\u6570\nimport numpy as np\nresult = np.sqrt(series)  # \u5bf9\u6bcf\u4e2a\u5143\u7d20\u53d6\u5e73\u65b9\u6839<\/pre>\n\n\n\n
                        \u8ba1\u7b97\u7edf\u8ba1\u6570\u636e\uff1a\u4f7f\u7528 Series \u7684\u65b9\u6cd5\u6765\u8ba1\u7b97\u63cf\u8ff0\u6027\u7edf\u8ba1\u3002<\/p>\n\n\n\n
                        print(s.sum())  # \u8f93\u51fa Series \u7684\u603b\u548c\nprint(s.mean())  # \u8f93\u51fa Series \u7684\u5e73\u5747\u503c\nprint(s.max())  # \u8f93\u51fa Series \u7684\u6700\u5927\u503c\nprint(s.min())  # \u8f93\u51fa Series \u7684\u6700\u5c0f\u503c\nprint(s.std())  # \u8f93\u51fa Series \u7684\u6807\u51c6\u5dee<\/pre>\n\n\n\n
                        \u5c5e\u6027\u548c\u65b9\u6cd5\uff1a<\/p>\n\n\n\n
                        # \u83b7\u53d6\u7d22\u5f15\nindex = s.index\n\n# \u83b7\u53d6\u503c\u6570\u7ec4\nvalues = s.values\n\n# \u83b7\u53d6\u63cf\u8ff0\u7edf\u8ba1\u4fe1\u606f\nstats = s.describe()\n\n# \u83b7\u53d6\u6700\u5927\u503c\u548c\u6700\u5c0f\u503c\u7684\u7d22\u5f15\nmax_index = s.idxmax()\nmin_index = s.idxmin()\n\n# \u5176\u4ed6\u5c5e\u6027\u548c\u65b9\u6cd5\nprint(s.dtype)   # \u6570\u636e\u7c7b\u578b\nprint(s.shape)   # \u5f62\u72b6\nprint(s.size)    # \u5143\u7d20\u4e2a\u6570\nprint(s.head())  # \u524d\u51e0\u4e2a\u5143\u7d20\uff0c\u9ed8\u8ba4\u662f\u524d 5 \u4e2a\nprint(s.tail())  # \u540e\u51e0\u4e2a\u5143\u7d20\uff0c\u9ed8\u8ba4\u662f\u540e 5 \u4e2a\nprint(s.sum())   # \u6c42\u548c\nprint(s.mean())  # \u5e73\u5747\u503c\nprint(s.std())   # \u6807\u51c6\u5dee\nprint(s.min())   # \u6700\u5c0f\u503c\nprint(s.max())   # \u6700\u5927\u503c<\/pre>\n\n\n\n
                        \u4f7f\u7528\u5e03\u5c14\u8868\u8fbe\u5f0f\uff1a\u6839\u636e\u6761\u4ef6\u8fc7\u6ee4 Series\u3002<\/p>\n\n\n\n
                        print(s > 2)  # \u8fd4\u56de\u4e00\u4e2a\u5e03\u5c14 Series\uff0c\u5176\u4e2d\u7684\u5143\u7d20\u503c\u5927\u4e8e 2<\/pre>\n\n\n\n
                        \u67e5\u770b\u6570\u636e\u7c7b\u578b\uff1a\u4f7f\u7528 dtype \u5c5e\u6027\u67e5\u770b Series \u7684\u6570\u636e\u7c7b\u578b\u3002<\/p>\n\n\n\n
                        print(s.dtype)  # \u8f93\u51fa Series \u7684\u6570\u636e\u7c7b\u578b<\/pre>\n\n\n\n
                        \u8f6c\u6362\u6570\u636e\u7c7b\u578b\uff1a\u4f7f\u7528 astype \u65b9\u6cd5\u5c06 Series \u8f6c\u6362\u4e3a\u53e6\u4e00\u79cd\u6570\u636e\u7c7b\u578b\u3002<\/p>\n\n\n\n
                        s = s.astype('float64')  # \u5c06 Series \u4e2d\u7684\u6240\u6709\u5143\u7d20\u8f6c\u6362\u4e3a float64 \u7c7b\u578b<\/pre>\n\n\n\n
                        \u6ce8\u610f\u4e8b\u9879\uff1a<\/p>\n\n\n\n
                          \n
                        • Series<\/code>\u00a0\u4e2d\u7684\u6570\u636e\u662f\u6709\u5e8f\u7684\u3002<\/li>\n\n\n\n
                        • \u53ef\u4ee5\u5c06\u00a0Series<\/code>\u00a0\u89c6\u4e3a\u5e26\u6709\u7d22\u5f15\u7684\u4e00\u7ef4\u6570\u7ec4\u3002<\/li>\n\n\n\n
                        • \u7d22\u5f15\u53ef\u4ee5\u662f\u552f\u4e00\u7684\uff0c\u4f46\u4e0d\u662f\u5fc5\u987b\u7684\u3002<\/li>\n\n\n\n
                        • \u6570\u636e\u53ef\u4ee5\u662f\u6807\u91cf\u3001\u5217\u8868\u3001NumPy \u6570\u7ec4\u7b49\u3002<\/li>\n<\/ul>\n\n\n\n
                          \u3000\u3000<\/p>\n\n\n\n
                          Pandas \u6570\u636e\u7ed3\u6784 – DataFrame\ud83d\udd16<\/strong><\/p>\n\n\n\n
                          \n\n\n\n
                          DataFrame \u662f Pandas \u4e2d\u7684\u53e6\u4e00\u4e2a\u6838\u5fc3\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8e\u4e00\u4e2a\u4e8c\u7ef4\u7684\u8868\u683c\u6216\u6570\u636e\u5e93\u4e2d\u7684\u6570\u636e\u8868\u3002<\/p>\n\n\n\n
                          DataFrame \u662f\u4e00\u4e2a\u8868\u683c\u578b\u7684\u6570\u636e\u7ed3\u6784\uff0c\u5b83\u542b\u6709\u4e00\u7ec4\u6709\u5e8f\u7684\u5217\uff0c\u6bcf\u5217\u53ef\u4ee5\u662f\u4e0d\u540c\u7684\u503c\u7c7b\u578b\uff08\u6570\u503c\u3001\u5b57\u7b26\u4e32\u3001\u5e03\u5c14\u578b\u503c\uff09\u3002<\/p>\n\n\n\n
                          DataFrame \u65e2\u6709\u884c\u7d22\u5f15\u4e5f\u6709\u5217\u7d22\u5f15\uff0c\u5b83\u53ef\u4ee5\u88ab\u770b\u505a\u7531 Series \u7ec4\u6210\u7684\u5b57\u5178\uff08\u5171\u540c\u7528\u4e00\u4e2a\u7d22\u5f15\uff09\u3002<\/p>\n\n\n\n
                          DataFrame \u63d0\u4f9b\u4e86\u5404\u79cd\u529f\u80fd\u6765\u8fdb\u884c\u6570\u636e\u8bbf\u95ee\u3001\u7b5b\u9009\u3001\u5206\u5272\u3001\u5408\u5e76\u3001\u91cd\u5851\u3001\u805a\u5408\u4ee5\u53ca\u8f6c\u6362\u7b49\u64cd\u4f5c\u3002<\/p>\n\n\n\n
                          DataFrame \u662f\u4e00\u4e2a\u975e\u5e38\u7075\u6d3b\u4e14\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\uff0c\u5e7f\u6cdb\u7528\u4e8e\u6570\u636e\u5206\u6790\u3001\u6e05\u6d17\u3001\u8f6c\u6362\u3001\u53ef\u89c6\u5316\u7b49\u4efb\u52a1\u3002<\/p>\n\n\n\n
                          DataFrame \u7279\u70b9\uff1a<\/strong><\/p>\n\n\n\n
                            \n
                          • \u4e8c\u7ef4\u7ed3\u6784\uff1a<\/strong>\u00a0DataFrame<\/code>\u00a0\u662f\u4e00\u4e2a\u4e8c\u7ef4\u8868\u683c\uff0c\u53ef\u4ee5\u88ab\u770b\u4f5c\u662f\u4e00\u4e2a Excel \u7535\u5b50\u8868\u683c\u6216 SQL \u8868\uff0c\u5177\u6709\u884c\u548c\u5217\u3002\u53ef\u4ee5\u5c06\u5176\u89c6\u4e3a\u591a\u4e2a\u00a0Series<\/code>\u00a0\u5bf9\u8c61\u7ec4\u6210\u7684\u5b57\u5178\u3002<\/li>\n\n\n\n
                          • \u5217\u7684\u6570\u636e\u7c7b\u578b\uff1a<\/strong>\u00a0\u4e0d\u540c\u7684\u5217\u53ef\u4ee5\u5305\u542b\u4e0d\u540c\u7684\u6570\u636e\u7c7b\u578b\uff0c\u4f8b\u5982\u6574\u6570\u3001\u6d6e\u70b9\u6570\u3001\u5b57\u7b26\u4e32\u6216 Python \u5bf9\u8c61\u7b49\u3002<\/li>\n\n\n\n
                          • \u7d22\u5f15<\/strong>\uff1aDataFrame<\/code>\u00a0\u53ef\u4ee5\u62e5\u6709\u884c\u7d22\u5f15\u548c\u5217\u7d22\u5f15\uff0c\u7c7b\u4f3c\u4e8e Excel \u4e2d\u7684\u884c\u53f7\u548c\u5217\u6807\u3002<\/li>\n\n\n\n
                          • \u5927\u5c0f\u53ef\u53d8<\/strong>\uff1a\u53ef\u4ee5\u6dfb\u52a0\u548c\u5220\u9664\u5217\uff0c\u7c7b\u4f3c\u4e8e Python \u4e2d\u7684\u5b57\u5178\u3002<\/li>\n\n\n\n
                          • \u81ea\u52a8\u5bf9\u9f50<\/strong>\uff1a\u5728\u8fdb\u884c\u7b97\u672f\u8fd0\u7b97\u6216\u6570\u636e\u5bf9\u9f50\u64cd\u4f5c\u65f6\uff0cDataFrame<\/code>\u00a0\u4f1a\u81ea\u52a8\u5bf9\u9f50\u7d22\u5f15\u3002<\/li>\n\n\n\n
                          • \u5904\u7406\u7f3a\u5931\u6570\u636e<\/strong>\uff1aDataFrame<\/code>\u00a0\u53ef\u4ee5\u5305\u542b\u7f3a\u5931\u6570\u636e\uff0cPandas \u4f7f\u7528\u00a0NaN<\/code>\uff08Not a Number\uff09\u6765\u8868\u793a\u3002<\/li>\n\n\n\n
                          • \u6570\u636e\u64cd\u4f5c<\/strong>\uff1a\u652f\u6301\u6570\u636e\u5207\u7247\u3001\u7d22\u5f15\u3001\u5b50\u96c6\u5206\u5272\u7b49\u64cd\u4f5c\u3002<\/li>\n\n\n\n
                          • \u65f6\u95f4\u5e8f\u5217\u652f\u6301<\/strong>\uff1aDataFrame<\/code>\u00a0\u5bf9\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u6709\u7279\u522b\u7684\u652f\u6301\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u8fdb\u884c\u65f6\u95f4\u6570\u636e\u7684\u5207\u7247\u3001\u7d22\u5f15\u548c\u64cd\u4f5c\u3002<\/li>\n\n\n\n
                          • \u4e30\u5bcc\u7684\u6570\u636e\u8bbf\u95ee\u529f\u80fd<\/strong>\uff1a\u901a\u8fc7\u00a0.loc<\/code>\u3001.iloc<\/code>\u00a0\u548c\u00a0.query()<\/code>\u00a0\u65b9\u6cd5\uff0c\u53ef\u4ee5\u7075\u6d3b\u5730\u8bbf\u95ee\u548c\u7b5b\u9009\u6570\u636e\u3002<\/li>\n\n\n\n
                          • \u7075\u6d3b\u7684\u6570\u636e\u5904\u7406\u529f\u80fd<\/strong>\uff1a\u5305\u62ec\u6570\u636e\u5408\u5e76\u3001\u91cd\u5851\u3001\u900f\u89c6\u3001\u5206\u7ec4\u548c\u805a\u5408\u7b49\u3002<\/li>\n\n\n\n
                          • \u6570\u636e\u53ef\u89c6\u5316<\/strong>\uff1a\u867d\u7136\u00a0DataFrame<\/code>\u00a0\u672c\u8eab\u4e0d\u662f\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u4f46\u5b83\u53ef\u4ee5\u4e0e Matplotlib \u6216 Seaborn \u7b49\u53ef\u89c6\u5316\u5e93\u7ed3\u5408\u4f7f\u7528\uff0c\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002<\/li>\n\n\n\n
                          • \u9ad8\u6548\u7684\u6570\u636e\u8f93\u5165\u8f93\u51fa<\/strong>\uff1a\u53ef\u4ee5\u65b9\u4fbf\u5730\u8bfb\u53d6\u548c\u5199\u5165\u6570\u636e\uff0c\u652f\u6301\u591a\u79cd\u683c\u5f0f\uff0c\u5982 CSV\u3001Excel\u3001SQL \u6570\u636e\u5e93\u548c HDF5 \u683c\u5f0f\u3002<\/li>\n\n\n\n
                          • \u63cf\u8ff0\u6027\u7edf\u8ba1<\/strong>\uff1a\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u65b9\u6cd5\u6765\u8ba1\u7b97\u63cf\u8ff0\u6027\u7edf\u8ba1\u6570\u636e\uff0c\u5982\u00a0.describe()<\/code>\u3001.mean()<\/code>\u3001.sum()<\/code>\u00a0\u7b49\u3002<\/li>\n\n\n\n
                          • \u7075\u6d3b\u7684\u6570\u636e\u5bf9\u9f50\u548c\u96c6\u6210<\/strong>\uff1a\u53ef\u4ee5\u8f7b\u677e\u5730\u4e0e\u5176\u4ed6\u00a0DataFrame<\/code>\u00a0\u6216\u00a0Series<\/code>\u00a0\u5bf9\u8c61\u8fdb\u884c\u5408\u5e76\u3001\u8fde\u63a5\u6216\u66f4\u65b0\u64cd\u4f5c\u3002<\/li>\n\n\n\n
                          • \u8f6c\u6362\u529f\u80fd<\/strong>\uff1a\u53ef\u4ee5\u5bf9\u6570\u636e\u96c6\u4e2d\u7684\u503c\u8fdb\u884c\u8f6c\u6362\uff0c\u4f8b\u5982\u4f7f\u7528\u00a0.apply()<\/code>\u00a0\u65b9\u6cd5\u5e94\u7528\u81ea\u5b9a\u4e49\u51fd\u6570\u3002<\/li>\n\n\n\n
                          • \u6eda\u52a8\u7a97\u53e3\u548c\u65f6\u95f4\u5e8f\u5217\u5206\u6790<\/strong>\uff1a\u652f\u6301\u5bf9\u6570\u636e\u96c6\u8fdb\u884c\u6eda\u52a8\u7a97\u53e3\u7edf\u8ba1\u548c\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3002<\/li>\n<\/ul>\n\n\n
                            \n
                            \"\"<\/figure><\/div>\n\n
                            \n
                            \"\"<\/figure><\/div>\n\n\n
                            DataFrame \u6784\u9020\u65b9\u6cd5\u5982\u4e0b\uff1a<\/p>\n\n\n\n
                            pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)<\/pre>\n\n\n\n
                            \u53c2\u6570\u8bf4\u660e\uff1a<\/p>\n\n\n\n
                              \n
                            • data<\/code>\uff1aDataFrame \u7684\u6570\u636e\u90e8\u5206\uff0c\u53ef\u4ee5\u662f\u5b57\u5178\u3001\u4e8c\u7ef4\u6570\u7ec4\u3001Series\u3001DataFrame \u6216\u5176\u4ed6\u53ef\u8f6c\u6362\u4e3a DataFrame \u7684\u5bf9\u8c61\u3002\u5982\u679c\u4e0d\u63d0\u4f9b\u6b64\u53c2\u6570\uff0c\u5219\u521b\u5efa\u4e00\u4e2a\u7a7a\u7684 DataFrame\u3002<\/li>\n\n\n\n
                            • index<\/code>\uff1aDataFrame \u7684\u884c\u7d22\u5f15\uff0c\u7528\u4e8e\u6807\u8bc6\u6bcf\u884c\u6570\u636e\u3002\u53ef\u4ee5\u662f\u5217\u8868\u3001\u6570\u7ec4\u3001\u7d22\u5f15\u5bf9\u8c61\u7b49\u3002\u5982\u679c\u4e0d\u63d0\u4f9b\u6b64\u53c2\u6570\uff0c\u5219\u521b\u5efa\u4e00\u4e2a\u9ed8\u8ba4\u7684\u6574\u6570\u7d22\u5f15\u3002<\/li>\n\n\n\n
                            • columns<\/code>\uff1aDataFrame \u7684\u5217\u7d22\u5f15\uff0c\u7528\u4e8e\u6807\u8bc6\u6bcf\u5217\u6570\u636e\u3002\u53ef\u4ee5\u662f\u5217\u8868\u3001\u6570\u7ec4\u3001\u7d22\u5f15\u5bf9\u8c61\u7b49\u3002\u5982\u679c\u4e0d\u63d0\u4f9b\u6b64\u53c2\u6570\uff0c\u5219\u521b\u5efa\u4e00\u4e2a\u9ed8\u8ba4\u7684\u6574\u6570\u7d22\u5f15\u3002<\/li>\n\n\n\n
                            • dtype<\/code>\uff1a\u6307\u5b9a DataFrame \u7684\u6570\u636e\u7c7b\u578b\u3002\u53ef\u4ee5\u662f NumPy \u7684\u6570\u636e\u7c7b\u578b\uff0c\u4f8b\u5982\u00a0np.int64<\/code>\u3001np.float64<\/code>\u00a0\u7b49\u3002\u5982\u679c\u4e0d\u63d0\u4f9b\u6b64\u53c2\u6570\uff0c\u5219\u6839\u636e\u6570\u636e\u81ea\u52a8\u63a8\u65ad\u6570\u636e\u7c7b\u578b\u3002<\/li>\n\n\n\n
                            • copy<\/code>\uff1a\u662f\u5426\u590d\u5236\u6570\u636e\u3002\u9ed8\u8ba4\u4e3a False\uff0c\u8868\u793a\u4e0d\u590d\u5236\u6570\u636e\u3002\u5982\u679c\u8bbe\u7f6e\u4e3a True\uff0c\u5219\u590d\u5236\u8f93\u5165\u7684\u6570\u636e\u3002<\/li>\n<\/ul>\n\n\n\n
                              Pandas DataFrame \u662f\u4e00\u4e2a\u4e8c\u7ef4\u7684\u6570\u7ec4\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8c\u7ef4\u6570\u7ec4\u3002<\/p>\n\n\n\n
                              import pandas as pd\n\ndata = [['Google', 10], ['Runoob', 12], ['Wiki', 13]]\n\n# \u521b\u5efaDataFrame\ndf = pd.DataFrame(data, columns=['Site', 'Age'])\n\n# \u4f7f\u7528astype\u65b9\u6cd5\u8bbe\u7f6e\u6bcf\u5217\u7684\u6570\u636e\u7c7b\u578b\ndf['Site'] = df['Site'].astype(str)\ndf['Age'] = df['Age'].astype(float)\n\nprint(df)<\/pre>\n\n\n\n
                              \u4e5f\u53ef\u4ee5\u4f7f\u7528\u5b57\u5178\u6765\u521b\u5efa\uff1a<\/p>\n\n\n\n
                              import pandas as pd\n\ndata = {'Site':['Google', 'Runoob', 'Wiki'], 'Age':[10, 12, 13]}\n\ndf = pd.DataFrame(data)\n\nprint (df)<\/pre>\n\n\n\n
                              \u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n\n\n
                              \n
                              \"\"<\/figure><\/div>\n\n\n
                              \u4ee5\u4e0b\u5b9e\u4f8b\u4f7f\u7528 ndarrays \u521b\u5efa\uff0cndarray \u7684\u957f\u5ea6\u5fc5\u987b\u76f8\u540c\uff0c \u5982\u679c\u4f20\u9012\u4e86 index\uff0c\u5219\u7d22\u5f15\u7684\u957f\u5ea6\u5e94\u7b49\u4e8e\u6570\u7ec4\u7684\u957f\u5ea6\u3002\u5982\u679c\u6ca1\u6709\u4f20\u9012\u7d22\u5f15\uff0c\u5219\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c\u7d22\u5f15\u5c06\u662frange(n)\uff0c\u5176\u4e2dn\u662f\u6570\u7ec4\u957f\u5ea6\u3002<\/p>\n\n\n\n
                              \u5b9e\u4f8b – \u4f7f\u7528 ndarrays \u521b\u5efa<\/p>\n\n\n\n
                              import numpy as np\nimport pandas as pd\n\n# \u521b\u5efa\u4e00\u4e2a\u5305\u542b\u7f51\u7ad9\u548c\u5e74\u9f84\u7684\u4e8c\u7ef4ndarray\nndarray_data = np.array([\n    ['Google', 10],\n    ['Runoob', 12],\n    ['Wiki', 13]\n])\n\n# \u4f7f\u7528DataFrame\u6784\u9020\u51fd\u6570\u521b\u5efa\u6570\u636e\u5e27\ndf = pd.DataFrame(ndarray_data, columns=['Site', 'Age'])\n\n# \u6253\u5370\u6570\u636e\u5e27\nprint(df)<\/pre>\n\n\n\n
                              \u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n\n\n
                              \n
                              \"\"<\/figure><\/div>\n\n\n
                              \u4ece\u4ee5\u4e0a\u8f93\u51fa\u7ed3\u679c\u53ef\u4ee5\u77e5\u9053\uff0c DataFrame \u6570\u636e\u7c7b\u578b\u4e00\u4e2a\u8868\u683c\uff0c\u5305\u542b rows\uff08\u884c\uff09 \u548c columns\uff08\u5217\uff09\uff1a<\/p>\n\n\n
                              \n
                              \"\"<\/figure><\/div>\n\n\n
                              \u8fd8\u53ef\u4ee5\u4f7f\u7528\u5b57\u5178\uff08key\/value\uff09\uff0c\u5176\u4e2d\u5b57\u5178\u7684 key \u4e3a\u5217\u540d:<\/p>\n\n\n\n
                              \u5b9e\u4f8b – \u4f7f\u7528\u5b57\u5178\u521b\u5efa<\/p>\n\n\n\n
                              import pandas as pd\n\ndata = [{'a': 1, 'b': 2},{'a': 5, 'b': 10, 'c': 20}]\n\ndf = pd.DataFrame(data)\n\nprint (df)<\/pre>\n\n\n\n
                              \u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n\n\n\n
                                 a   b     c\n0  1   2   NaN\n1  5  10  20.0<\/pre>\n\n\n\n
                              \u6ca1\u6709\u5bf9\u5e94\u7684\u90e8\u5206\u6570\u636e\u4e3a NaN<\/strong>\u3002<\/p>\n\n\n\n
                              Pandas \u53ef\u4ee5\u4f7f\u7528 loc<\/strong> \u5c5e\u6027\u8fd4\u56de\u6307\u5b9a\u884c\u7684\u6570\u636e\uff0c\u5982\u679c\u6ca1\u6709\u8bbe\u7f6e\u7d22\u5f15\uff0c\u7b2c\u4e00\u884c\u7d22\u5f15\u4e3a 0<\/strong>\uff0c\u7b2c\u4e8c\u884c\u7d22\u5f15\u4e3a 1<\/strong>\uff0c\u4ee5\u6b64\u7c7b\u63a8\uff1a<\/p>\n\n\n\n
                              import pandas as pd\n\ndata = {\n  \"calories\": [420, 380, 390],\n  \"duration\": [50, 40, 45]\n}\n\n# \u6570\u636e\u8f7d\u5165\u5230 DataFrame \u5bf9\u8c61\ndf = pd.DataFrame(data)\n\n# \u8fd4\u56de\u7b2c\u4e00\u884c\nprint(df.loc[0])\n# \u8fd4\u56de\u7b2c\u4e8c\u884c\nprint(df.loc[1])<\/pre>\n\n\n\n
                              \u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n\n\n\n
                              calories    420\nduration     50\nName: 0, dtype: int64\ncalories    380\nduration     40\nName: 1, dtype: int64\n<\/pre>\n\n\n\n
                              \u6ce8\u610f\uff1a<\/strong>\u8fd4\u56de\u7ed3\u679c\u5176\u5b9e\u5c31\u662f\u4e00\u4e2a Pandas Series \u6570\u636e\u3002<\/p>\n\n\n\n
                              \u4e5f\u53ef\u4ee5\u8fd4\u56de\u591a\u884c\u6570\u636e\uff0c\u4f7f\u7528 [[ … ]]<\/strong> \u683c\u5f0f\uff0c…<\/strong> \u4e3a\u5404\u884c\u7684\u7d22\u5f15\uff0c\u4ee5\u9017\u53f7\u9694\u5f00\uff1a<\/p>\n\n\n\n
                              import pandas as pd\n\ndata = {\n  \"calories\": [420, 380, 390],\n  \"duration\": [50, 40, 45]\n}\n\n# \u6570\u636e\u8f7d\u5165\u5230 DataFrame \u5bf9\u8c61\ndf = pd.DataFrame(data)\n\n# \u8fd4\u56de\u7b2c\u4e00\u884c\u548c\u7b2c\u4e8c\u884c\nprint(df.loc[[0, 1]])<\/pre>\n\n\n\n
                              \u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n\n\n\n
                                 calories  duration\n0       420        50\n1       380        40<\/pre>\n\n\n\n
                              \u6ce8\u610f\uff1a<\/strong>\u8fd4\u56de\u7ed3\u679c\u5176\u5b9e\u5c31\u662f\u4e00\u4e2a Pandas DataFrame \u6570\u636e\u3002<\/p>\n\n\n\n
                              \u6211\u4eec\u53ef\u4ee5\u6307\u5b9a\u7d22\u5f15\u503c\uff0c\u5982\u4e0b\u5b9e\u4f8b\uff1a<\/p>\n\n\n\n
                              import pandas as pd\n\ndata = {\n  \"calories\": [420, 380, 390],\n  \"duration\": [50, 40, 45]\n}\n\ndf = pd.DataFrame(data, index = [\"day1\", \"day2\", \"day3\"])\n\nprint(df)<\/pre>\n\n\n\n
                              \u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n\n\n\n
                                    calories  duration\nday1       420        50\nday2       380        40\nday3       390        45<\/pre>\n\n\n\n
                              Pandas \u53ef\u4ee5\u4f7f\u7528 loc<\/strong> \u5c5e\u6027\u8fd4\u56de\u6307\u5b9a\u7d22\u5f15\b\u5bf9\u5e94\u5230\u67d0\u4e00\u884c\uff1a<\/p>\n\n\n\n
                              import pandas as pd\n\ndata = {\n  \"calories\": [420, 380, 390],\n  \"duration\": [50, 40, 45]\n}\n\ndf = pd.DataFrame(data, index = [\"day1\", \"day2\", \"day3\"])\n\n# \u6307\u5b9a\u7d22\u5f15\nprint(df.loc[\"day2\"])<\/pre>\n\n\n\n
                              \u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n\n\n\n
                              calories    380\nduration     40\nName: day2, dtype: int64<\/pre>\n\n\n\n
                              \u3000\u3000<\/p>\n\n\n\n
                              \ud83d\udd39DataFrame \u65b9\u6cd5<\/strong><\/p>\n\n\n\n
                              DataFrame \u7684\u5e38\u7528\u64cd\u4f5c\u548c\u65b9\u6cd5\u5982\u4e0b\u8868\u6240\u793a\uff1a<\/p>\n\n\n\n
                              \u65b9\u6cd5\u540d\u79f0<\/strong><\/th>\u529f\u80fd\u63cf\u8ff0<\/strong><\/th><\/tr><\/thead>
                              head(n)<\/code><\/td>\u8fd4\u56de DataFrame \u7684\u524d n \u884c\u6570\u636e\uff08\u9ed8\u8ba4\u524d 5 \u884c\uff09<\/td><\/tr>
                              tail(n)<\/code><\/td>\u8fd4\u56de DataFrame \u7684\u540e n \u884c\u6570\u636e\uff08\u9ed8\u8ba4\u540e 5 \u884c\uff09<\/td><\/tr>
                              info()<\/code><\/td>\u663e\u793a DataFrame \u7684\u7b80\u8981\u4fe1\u606f\uff0c\u5305\u62ec\u5217\u540d\u3001\u6570\u636e\u7c7b\u578b\u3001\u975e\u7a7a\u503c\u6570\u91cf\u7b49<\/td><\/tr>
                              describe()<\/code><\/td>\u8fd4\u56de DataFrame \u6570\u503c\u5217\u7684\u7edf\u8ba1\u4fe1\u606f\uff0c\u5982\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u6700\u5c0f\u503c\u7b49<\/td><\/tr>
                              shape<\/code><\/td>\u8fd4\u56de DataFrame \u7684\u884c\u6570\u548c\u5217\u6570\uff08\u884c\u6570, \u5217\u6570\uff09<\/td><\/tr>
                              columns<\/code><\/td>\u8fd4\u56de DataFrame \u7684\u6240\u6709\u5217\u540d<\/td><\/tr>
                              index<\/code><\/td>\u8fd4\u56de DataFrame \u7684\u884c\u7d22\u5f15<\/td><\/tr>
                              dtypes<\/code><\/td>\u8fd4\u56de\u6bcf\u4e00\u5217\u7684\u6570\u503c\u6570\u636e\u7c7b\u578b<\/td><\/tr>
                              sort_values(by)<\/code><\/td>\u6309\u7167\u6307\u5b9a\u5217\u6392\u5e8f<\/td><\/tr>
                              sort_index()<\/code><\/td>\u6309\u884c\u7d22\u5f15\u6392\u5e8f<\/td><\/tr>
                              dropna()<\/code><\/td>\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\uff08NaN\uff09\u7684\u884c\u6216\u5217<\/td><\/tr>
                              fillna(value)<\/code><\/td>\u7528\u6307\u5b9a\u7684\u503c\u586b\u5145\u7f3a\u5931\u503c<\/td><\/tr>
                              isnull()<\/code><\/td>\u5224\u65ad\u7f3a\u5931\u503c\uff0c\u8fd4\u56de\u4e00\u4e2a\u5e03\u5c14\u503c DataFrame<\/td><\/tr>
                              notnull()<\/code><\/td>\u5224\u65ad\u975e\u7f3a\u5931\u503c\uff0c\u8fd4\u56de\u4e00\u4e2a\u5e03\u5c14\u503c DataFrame<\/td><\/tr>
                              loc[]<\/code><\/td>\u6309\u6807\u7b7e\u7d22\u5f15\u9009\u62e9\u6570\u636e<\/td><\/tr>
                              iloc[]<\/code><\/td>\u6309\u4f4d\u7f6e\u7d22\u5f15\u9009\u62e9\u6570\u636e<\/td><\/tr>
                              at[]<\/code><\/td>\u8bbf\u95ee DataFrame \u4e2d\u5355\u4e2a\u5143\u7d20\uff08\u6bd4 loc[]<\/code> \u66f4\u9ad8\u6548\uff09<\/td><\/tr>
                              iat[]<\/code><\/td>\u8bbf\u95ee DataFrame \u4e2d\u5355\u4e2a\u5143\u7d20\uff08\u6bd4 iloc[]<\/code> \u66f4\u9ad8\u6548\uff09<\/td><\/tr>
                              apply(func)<\/code><\/td>\u5bf9 DataFrame \u6216 Series \u5e94\u7528\u4e00\u4e2a\u51fd\u6570<\/td><\/tr>
                              applymap(func)<\/code><\/td>\u5bf9 DataFrame \u7684\u6bcf\u4e2a\u5143\u7d20\u5e94\u7528\u51fd\u6570\uff08\u4ec5\u5bf9 DataFrame\uff09<\/td><\/tr>
                              groupby(by)<\/code><\/td>\u5206\u7ec4\u64cd\u4f5c\uff0c\u7528\u4e8e\u6309\u67d0\u4e00\u5217\u5206\u7ec4\u8fdb\u884c\u6c47\u603b\u7edf\u8ba1<\/td><\/tr>
                              pivot_table()<\/code><\/td>\u521b\u5efa\u900f\u89c6\u8868<\/td><\/tr>
                              merge()<\/code><\/td>\u5408\u5e76\u591a\u4e2a DataFrame\uff08\u7c7b\u4f3c SQL \u7684 JOIN \u64cd\u4f5c\uff09<\/td><\/tr>
                              concat()<\/code><\/td>\u6309\u884c\u6216\u6309\u5217\u8fde\u63a5\u591a\u4e2a DataFrame<\/td><\/tr>
                              to_csv()<\/code><\/td>\u5c06 DataFrame \u5bfc\u51fa\u4e3a CSV \u6587\u4ef6<\/td><\/tr>
                              to_excel()<\/code><\/td>\u5c06 DataFrame \u5bfc\u51fa\u4e3a Excel \u6587\u4ef6<\/td><\/tr>
                              to_json()<\/code><\/td>\u5c06 DataFrame \u5bfc\u51fa\u4e3a JSON \u683c\u5f0f<\/td><\/tr>
                              to_sql()<\/code><\/td>\u5c06 DataFrame \u5bfc\u51fa\u4e3a SQL \u6570\u636e\u5e93<\/td><\/tr>
                              query()<\/code><\/td>\u4f7f\u7528 SQL \u98ce\u683c\u7684\u8bed\u6cd5\u67e5\u8be2 DataFrame<\/td><\/tr>
                              duplicated()<\/code><\/td>\u8fd4\u56de\u5e03\u5c14\u503c DataFrame\uff0c\u6307\u793a\u6bcf\u884c\u662f\u5426\u662f\u91cd\u590d\u7684<\/td><\/tr>
                              drop_duplicates()<\/code><\/td>\u5220\u9664\u91cd\u590d\u7684\u884c<\/td><\/tr>
                              set_index()<\/code><\/td>\u8bbe\u7f6e DataFrame \u7684\u7d22\u5f15<\/td><\/tr>
                              reset_index()<\/code><\/td>\u91cd\u7f6e DataFrame \u7684\u7d22\u5f15<\/td><\/tr>
                              transpose()<\/code><\/td>\u8f6c\u7f6e DataFrame\uff08\u884c\u5217\u4ea4\u6362\uff09<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                              \u3000\u3000<\/p>\n\n\n\n
                              \u5b9e\u4f8b<\/p>\n\n\n\n
                              import pandas as pd\n\n# \u521b\u5efa DataFrame\ndata = {\n    'Name': ['Alice', 'Bob', 'Charlie', 'David'],\n    'Age': [25, 30, 35, 40],\n    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']\n}\ndf = pd.DataFrame(data)\n\n# \u67e5\u770b\u524d\u4e24\u884c\u6570\u636e\nprint(df.head(2))\n\n# \u67e5\u770b DataFrame \u7684\u57fa\u672c\u4fe1\u606f\nprint(df.info())\n\n# \u83b7\u53d6\u63cf\u8ff0\u7edf\u8ba1\u4fe1\u606f\nprint(df.describe())\n\n# \u6309\u5e74\u9f84\u6392\u5e8f\ndf_sorted = df.sort_values(by='Age', ascending=False)\nprint(df_sorted)\n\n# \u9009\u62e9\u6307\u5b9a\u5217\nprint(df[['Name', 'Age']])\n\n# \u6309\u7d22\u5f15\u9009\u62e9\u884c\nprint(df.iloc[1:3])  # \u9009\u62e9\u7b2c\u4e8c\u5230\u7b2c\u4e09\u884c\uff08\u6309\u4f4d\u7f6e\uff09\n\n# \u6309\u6807\u7b7e\u9009\u62e9\u884c\nprint(df.loc[1:2])  # \u9009\u62e9\u7b2c\u4e8c\u5230\u7b2c\u4e09\u884c\uff08\u6309\u6807\u7b7e\uff09\n\n# \u8ba1\u7b97\u5206\u7ec4\u7edf\u8ba1\uff08\u6309\u57ce\u5e02\u5206\u7ec4\uff0c\u8ba1\u7b97\u5e73\u5747\u5e74\u9f84\uff09\nprint(df.groupby('City')['Age'].mean())\n\n# \u5904\u7406\u7f3a\u5931\u503c\uff08\u586b\u5145\u7f3a\u5931\u503c\uff09\ndf['Age'] = df['Age'].fillna(30)\n\n# \u5bfc\u51fa\u4e3a CSV \u6587\u4ef6\ndf.to_csv('output.csv', index=False)<\/pre>\n\n\n\n
                              \u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n\n\n\n
                              # \u67e5\u770b\u524d\u4e24\u884c\u6570\u636e\n     Name  Age         City\n0   Alice   25     New York\n1     Bob   30  Los Angeles\n\n# \u67e5\u770b DataFrame \u7684\u57fa\u672c\u4fe1\u606f\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 4 entries, 0 to 3\nData columns (total 3 columns):\n #   Column  Non-Null Count  Dtype  \n---  ------  --------------  -----  \n 0   Name    4 non-null      object \n 1   Age     4 non-null      int64  \n 2   City    4 non-null      object \ndtypes: int64(1), object(2)\nmemory usage: 148.0+ bytes\n\n# \u83b7\u53d6\u63cf\u8ff0\u7edf\u8ba1\u4fe1\u606f\n             Age\ncount   4.000000\nmean   32.500000\nstd     6.454972\nmin    25.000000\n25%    27.500000\n50%    32.500000\n75%    37.500000\nmax    40.000000\n\n# \u6309\u5e74\u9f84\u6392\u5e8f\n      Name  Age         City\n3    David   40     Houston\n2  Charlie   35      Chicago\n1      Bob   30  Los Angeles\n0    Alice   25     New York\n\n# \u6309\u6807\u7b7e\u9009\u62e9\u884c\n      Name  Age         City\n1     Bob   30  Los Angeles\n2  Charlie   35      Chicago\n\n# \u8ba1\u7b97\u5206\u7ec4\u7edf\u8ba1\uff08\u6309\u57ce\u5e02\u5206\u7ec4\uff0c\u8ba1\u7b97\u5e73\u5747\u5e74\u9f84\uff09\nCity\nChicago        35.0\nHouston        40.0\nLos Angeles    30.0\nNew York       25.0\nName: Age, dtype: float64\n<\/pre>\n\n\n\n
                              \u3000\u3000<\/p>\n\n\n\n
                              \ud83d\udd39\u66f4\u591a DataFrame \u8bf4\u660e<\/strong><\/p>\n\n\n\n
                              \u521b\u5efa DataFrame<\/strong><\/p>\n\n\n\n
                                \n
                              • \u4ece\u5b57\u5178\u521b\u5efa\uff1a<\/strong>\u5b57\u5178\u7684\u952e\u6210\u4e3a\u5217\u540d\uff0c\u503c\u6210\u4e3a\u5217\u6570\u636e\u3002<\/li>\n<\/ul>\n\n\n\n
                                import pandas as pd\n\n# \u901a\u8fc7\u5b57\u5178\u521b\u5efa DataFrame\ndf = pd.DataFrame({'Column1': [1, 2, 3], 'Column2': [4, 5, 6]})<\/pre>\n\n\n\n
                                  \n
                                • \u4ece\u5217\u8868\u7684\u5217\u8868\u521b\u5efa\uff1a<\/strong>\u5916\u5c42\u5217\u8868\u4ee3\u8868\u884c\uff0c\u5185\u5c42\u5217\u8868\u4ee3\u8868\u5217\u3002# \u901a\u8fc7\u5217\u8868\u7684\u5217\u8868\u521b\u5efa DataFrame<\/li>\n<\/ul>\n\n\n\n
                                  df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]],\n                  columns=['Column1', 'Column2', 'Column3'])<\/pre>\n\n\n\n
                                    \n
                                  • \u4ece NumPy \u6570\u7ec4\u521b\u5efa\uff1a<\/strong>\u63d0\u4f9b\u4e00\u4e2a\u4e8c\u7ef4 NumPy \u6570\u7ec4\u3002<\/li>\n<\/ul>\n\n\n\n
                                    import numpy as np\n\n# \u901a\u8fc7 NumPy \u6570\u7ec4\u521b\u5efa DataFrame\ndf = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))<\/pre>\n\n\n\n
                                      \n
                                    • \u4ece Series \u521b\u5efa DataFrame\uff1a<\/strong>\u901a\u8fc7\u00a0pd.Series()<\/strong>\u00a0\u521b\u5efa\u3002<\/li>\n<\/ul>\n\n\n\n
                                      # \u4ece Series \u521b\u5efa DataFrame\ns1 = pd.Series(['Alice', 'Bob', 'Charlie'])\ns2 = pd.Series([25, 30, 35])\ns3 = pd.Series(['New York', 'Los Angeles', 'Chicago'])\ndf = pd.DataFrame({'Name': s1, 'Age': s2, 'City': s3})<\/pre>\n\n\n\n
                                      \u3000\u3000<\/p>\n\n\n\n
                                      \ud83d\udd39DataFrame \u7684\u5c5e\u6027\u548c\u65b9\u6cd5<\/strong><\/p>\n\n\n\n
                                      DataFrame \u5bf9\u8c61\u6709\u8bb8\u591a\u5c5e\u6027\u548c\u65b9\u6cd5\uff0c\u7528\u4e8e\u6570\u636e\u64cd\u4f5c\u3001\u7d22\u5f15\u548c\u5904\u7406\uff0c\u4f8b\u5982\uff1ashape\u3001columns\u3001index\u3001head()\u3001tail()\u3001info()\u3001describe()\u3001mean()\u3001sum() \u7b49\u3002<\/p>\n\n\n\n
                                      # DataFrame \u7684\u5c5e\u6027\u548c\u65b9\u6cd5\nprint(df.shape)     # \u5f62\u72b6\nprint(df.columns)   # \u5217\u540d\nprint(df.index)     # \u7d22\u5f15\nprint(df.head())    # \u524d\u51e0\u884c\u6570\u636e\uff0c\u9ed8\u8ba4\u662f\u524d 5 \u884c\nprint(df.tail())    # \u540e\u51e0\u884c\u6570\u636e\uff0c\u9ed8\u8ba4\u662f\u540e 5 \u884c\nprint(df.info())    # \u6570\u636e\u4fe1\u606f\nprint(df.describe())# \u63cf\u8ff0\u7edf\u8ba1\u4fe1\u606f\nprint(df.mean())    # \u6c42\u5e73\u5747\u503c\nprint(df.sum())     # \u6c42\u548c<\/pre>\n\n\n\n
                                      \u3000\u3000<\/p>\n\n\n\n
                                      \ud83d\udd39\u8bbf\u95ee DataFrame \u5143\u7d20<\/strong><\/p>\n\n\n\n
                                      \u8bbf\u95ee\u5217\uff1a<\/strong>\u4f7f\u7528\u5217\u540d\u4f5c\u4e3a\u5c5e\u6027\u6216\u901a\u8fc7 .loc[]<\/strong>\u3001.iloc[]<\/strong> \u8bbf\u95ee\uff0c\u4e5f\u53ef\u4ee5\u4f7f\u7528\u6807\u7b7e\u6216\u4f4d\u7f6e\u7d22\u5f15\u3002\u3002<\/p>\n\n\n\n
                                      # \u901a\u8fc7\u5217\u540d\u8bbf\u95ee\nprint(df['Column1'])\n\n# \u901a\u8fc7\u5c5e\u6027\u8bbf\u95ee\nprint(df.Name)     \n   \n# \u901a\u8fc7 .loc[] \u8bbf\u95ee\nprint(df.loc[:, 'Column1'])\n\n# \u901a\u8fc7 .iloc[] \u8bbf\u95ee\nprint(df.iloc[:, 0])  # \u5047\u8bbe 'Column1' \u662f\u7b2c\u4e00\u5217\n\n# \u8bbf\u95ee\u5355\u4e2a\u5143\u7d20\nprint(df['Name'][0])<\/pre>\n\n\n\n
                                      \u8bbf\u95ee\u884c\uff1a<\/strong>\u4f7f\u7528\u884c\u7684\u6807\u7b7e\u548c .loc[]<\/strong> \u8bbf\u95ee\u3002<\/p>\n\n\n\n
                                      # \u901a\u8fc7\u884c\u6807\u7b7e\u8bbf\u95ee\nprint(df.loc[0, 'Column1'])<\/pre>\n\n\n\n
                                      \u3000\u3000<\/p>\n\n\n\n
                                      \ud83d\udd39\u4fee\u6539 DataFrame<\/strong><\/p>\n\n\n\n
                                      \u4fee\u6539\u5217\u6570\u636e\uff1a<\/strong>\u76f4\u63a5\u5bf9\u5217\u8fdb\u884c\u8d4b\u503c\u3002<\/p>\n\n\n\n
                                      df['Column1'] = [10, 11, 12]\n<\/pre>\n\n\n\n
                                      \u6dfb\u52a0\u65b0\u5217\uff1a<\/strong>\u7ed9\u65b0\u5217\u8d4b\u503c\u3002<\/p>\n\n\n\n
                                      df['NewColumn'] = [100, 200, 300]<\/pre>\n\n\n\n
                                      \u6dfb\u52a0\u65b0\u884c\uff1a<\/strong>\u4f7f\u7528 loc\u3001append \u6216 concat \u65b9\u6cd5\u3002<\/p>\n\n\n\n
                                      # \u4f7f\u7528 loc \u4e3a\u7279\u5b9a\u7d22\u5f15\u6dfb\u52a0\u65b0\u884c\ndf.loc[3] = [13, 14, 15, 16]\n\n# \u4f7f\u7528 append \u6dfb\u52a0\u65b0\u884c\u5230\u672b\u5c3e\nnew_row = {'Column1': 13, 'Column2': 14, 'NewColumn': 16}\ndf = df.append(new_row, ignore_index=True)<\/pre>\n\n\n\n
                                      \u6ce8\u610f\uff1a<\/strong>append()<\/strong> \u65b9\u6cd5\u5728 pandas \u7248\u672c 1.4.0 \u4e2d\u5df2\u7ecf\u88ab\u6807\u8bb0\u4e3a\u5f03\u7528\uff0c\u5e76\u5c06\u5728\u672a\u6765\u7684\u7248\u672c\u4e2d\u88ab\u79fb\u9664\uff0c\u5b98\u65b9\u63a8\u8350\u4f7f\u7528 concat()<\/strong> \u4f5c\u4e3a\u66ff\u4ee3\u65b9\u6cd5\u6765\u8fdb\u884c\u6570\u636e\u7684\u5408\u5e76\u64cd\u4f5c\u3002<\/p>\n\n\n\n
                                      concat() \u65b9\u6cd5\u7528\u4e8e\u5408\u5e76\u4e24\u4e2a\u6216\u591a\u4e2a DataFrame\uff0c\u5f53\u4f60\u60f3\u8981\u6dfb\u52a0\u4e00\u884c\u5230\u53e6\u4e00\u4e2a DataFrame \u65f6\uff0c\u53ef\u4ee5\u5c06\u65b0\u884c\u4f5c\u4e3a\u4e00\u4e2a\u65b0\u7684 DataFrame\uff0c\u7136\u540e\u4f7f\u7528 concat()\uff1a<\/p>\n\n\n\n
                                      # \u4f7f\u7528concat\u6dfb\u52a0\u65b0\u884c\nnew_row = pd.DataFrame([[4, 7]], columns=['A', 'B'])  # \u521b\u5efa\u4e00\u4e2a\u53ea\u5305\u542b\u65b0\u884c\u7684DataFrame\ndf = pd.concat([df, new_row], ignore_index=True)  # \u5c06\u65b0\u884c\u6dfb\u52a0\u5230\u539f\u59cbDataFrame\n\nprint(df)<\/pre>\n\n\n\n
                                      \u3000\u3000<\/p>\n\n\n\n
                                      \ud83d\udd39\u5220\u9664 DataFrame \u5143\u7d20<\/strong><\/p>\n\n\n\n
                                      \u5220\u9664\u5217\uff1a<\/strong>\u4f7f\u7528 drop \u65b9\u6cd5\u3002<\/p>\n\n\n\n
                                      df_dropped = df.drop('Column1', axis=1)<\/pre>\n\n\n\n
                                      \u5220\u9664\u884c\uff1a<\/strong>\u540c\u6837\u4f7f\u7528 drop \u65b9\u6cd5\u3002<\/p>\n\n\n\n
                                      df_dropped = df.drop(0)  # \u5220\u9664\u7d22\u5f15\u4e3a 0 \u7684\u884c<\/pre>\n\n\n\n
                                      \u3000\u3000<\/p>\n\n\n\n
                                      \ud83d\udd39DataFrame \u7684\u7edf\u8ba1\u5206\u6790<\/strong><\/p>\n\n\n\n
                                      \u63cf\u8ff0\u6027\u7edf\u8ba1\uff1a<\/strong>\u4f7f\u7528 .describe()<\/strong> \u67e5\u770b\u6570\u503c\u5217\u7684\u7edf\u8ba1\u6458\u8981\u3002<\/p>\n\n\n\n
                                      df.describe()<\/pre>\n\n\n\n
                                      \u8ba1\u7b97\u7edf\u8ba1\u6570\u636e\uff1a<\/strong>\u4f7f\u7528\u805a\u5408\u51fd\u6570\u5982 .sum()\u3001.mean()\u3001.max()<\/strong> \u7b49\u3002<\/p>\n\n\n\n
                                      df['Column1'].sum()\ndf.mean()<\/pre>\n\n\n\n
                                      \u3000\u3000<\/p>\n\n\n\n
                                      \ud83d\udd39DataFrame \u7684\u6570\u636e\u7c7b\u578b<\/strong><\/p>\n\n\n\n
                                      \u67e5\u770b\u6570\u636e\u7c7b\u578b\uff1a\u4f7f\u7528 dtypes<\/strong> \u5c5e\u6027\u3002<\/p>\n\n\n\n
                                      df.dtypes<\/pre>\n\n\n\n
                                      \u8f6c\u6362\u6570\u636e\u7c7b\u578b\uff1a<\/strong>\u4f7f\u7528 astype<\/strong> \u65b9\u6cd5\u3002<\/p>\n\n\n\n
                                      df['Column1'] = df['Column1'].astype('float64')<\/pre>\n\n\n\n
                                      \u3000\u3000<\/p>\n\n\n\n
                                      \ud83d\udd39DataFrame \u7684\u5408\u5e76\u4e0e\u5206\u5272<\/strong><\/p>\n\n\n\n
                                      \u5408\u5e76\uff1a<\/strong>\u4f7f\u7528 concat<\/strong> \u6216 merge<\/strong> \u65b9\u6cd5\u3002<\/p>\n\n\n\n
                                      # \u7eb5\u5411\u5408\u5e76\npd.concat([df1, df2], ignore_index=True)\n\n# \u6a2a\u5411\u5408\u5e76\npd.merge(df1, df2, on='Column1')<\/pre>\n\n\n\n
                                      \u5206\u5272\uff1a<\/strong>\u4f7f\u7528 pivot\u3001melt<\/strong> \u6216\u81ea\u5b9a\u4e49\u51fd\u6570\u3002<\/p>\n\n\n\n
                                      # \u957f\u683c\u5f0f\u8f6c\u5bbd\u683c\u5f0f\ndf_pivot = df.pivot(index='Column1', columns='Column2', values='Column3')\n\n# \u5bbd\u683c\u5f0f\u8f6c\u957f\u683c\u5f0f\ndf_melt = df.melt(id_vars='Column1', value_vars=['Column2', 'Column3'])<\/pre>\n\n\n\n
                                      \u3000\u3000<\/p>\n\n\n\n
                                      \ud83d\udd39\u7d22\u5f15\u548c\u5207\u7247<\/strong><\/p>\n\n\n\n
                                      DataFrame \u652f\u6301\u5bf9\u884c\u548c\u5217\u8fdb\u884c\u7d22\u5f15\u548c\u5207\u7247\u64cd\u4f5c\u3002<\/p>\n\n\n\n
                                      # \u7d22\u5f15\u548c\u5207\u7247\nprint(df[['Name', 'Age']])  # \u63d0\u53d6\u591a\u5217\nprint(df[1:3])               # \u5207\u7247\u884c\nprint(df.loc[:, 'Name'])     # \u63d0\u53d6\u5355\u5217\nprint(df.loc[1:2, ['Name', 'Age']])  # \u6807\u7b7e\u7d22\u5f15\u63d0\u53d6\u6307\u5b9a\u884c\u5217\nprint(df.iloc[:, 1:])        # \u4f4d\u7f6e\u7d22\u5f15\u63d0\u53d6\u6307\u5b9a\u5217<\/pre>\n\n\n\n
                                      \u3000\u3000<\/p>\n\n\n\n
                                      \ud83d\udd39\u6ce8\u610f\u4e8b\u9879<\/strong><\/p>\n\n\n\n
                                        \n
                                      • DataFrame<\/code>\u00a0\u662f\u4e00\u79cd\u7075\u6d3b\u7684\u6570\u636e\u7ed3\u6784\uff0c\u53ef\u4ee5\u5bb9\u7eb3\u4e0d\u540c\u6570\u636e\u7c7b\u578b\u7684\u5217\u3002<\/li>\n\n\n\n
                                      • \u5217\u540d\u548c\u884c\u7d22\u5f15\u53ef\u4ee5\u662f\u5b57\u7b26\u4e32\u3001\u6574\u6570\u7b49\u3002<\/li>\n\n\n\n
                                      • DataFrame<\/code>\u00a0\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u8fdb\u884c\u6570\u636e\u9009\u62e9\u3001\u8fc7\u6ee4\u3001\u4fee\u6539\u548c\u5206\u6790\u3002<\/li>\n\n\n\n
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                                        \u524d\u8a00\u200b\ud83d\udd16 Pandas \u662f\u9488\u5bf9\u00a0Python\u00a0\u7f16\u7a0b\u8bed\u8a00\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u548c\u6570\u636e\u5206\u6790\u7684\u70ed\u95e8\u8f6f\u4ef6\u5e93\u3002 Pandas \u662f\u57fa […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[48],"tags":[],"_links":{"self":[{"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts\/20128"}],"collection":[{"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=20128"}],"version-history":[{"count":6,"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts\/20128\/revisions"}],"predecessor-version":[{"id":20155,"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts\/20128\/revisions\/20155"}],"wp:attachment":[{"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=20128"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=20128"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=20128"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}