{"id":953635,"date":"2024-12-27T01:45:44","date_gmt":"2024-12-26T17:45:44","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/953635.html"},"modified":"2024-12-27T01:45:46","modified_gmt":"2024-12-26T17:45:46","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e7%9a%84panda","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/953635.html","title":{"rendered":"\u5982\u4f55\u7528python\u7684panda"},"content":{"rendered":"

\"\u5982\u4f55\u7528python\u7684panda\"<\/p>\n

\u8981\u7528Python\u7684Pandas\u5e93\u8fdb\u884c\u6570\u636e\u5206\u6790\uff0c\u4f60\u9700\u8981\u638c\u63e1\u5982\u4f55\u5bfc\u5165\u6570\u636e\u3001\u6e05\u6d17\u6570\u636e\u3001\u5206\u6790\u6570\u636e\u548c\u53ef\u89c6\u5316\u6570\u636e\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u5982DataFrame\u548cSeries\uff0c\u5e2e\u52a9\u4f60\u8f7b\u677e\u5904\u7406\u548c\u5206\u6790\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u8be6\u7ec6\u4ecb\u7ecd\uff1a<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u5bfc\u5165\u6570\u636e<\/p>\n<\/p>\n

\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5206\u6790\u7684\u7b2c\u4e00\u6b65\u662f\u5bfc\u5165\u6570\u636e\u3002Pandas\u652f\u6301\u591a\u79cd\u683c\u5f0f\u7684\u6570\u636e\u5bfc\u5165\uff0c\u5305\u62ecCSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u3002\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u901a\u8fc7pandas.read_csv()<\/code>\u51fd\u6570\u6765\u8bfb\u53d6CSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    \u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/p>\n<\/p>\n

    Pandas\u7684read_csv()<\/code>\u51fd\u6570\u4f7f\u5f97\u8bfb\u53d6CSV\u6587\u4ef6\u975e\u5e38\u7b80\u5355\uff0c\u53ea\u9700\u63d0\u4f9b\u6587\u4ef6\u8def\u5f84\u5373\u53ef\u3002\u4f60\u8fd8\u53ef\u4ee5\u4f7f\u7528\u53c2\u6570\u8c03\u6574\u8bfb\u53d6\u65b9\u5f0f\uff0c\u4f8b\u5982\u6307\u5b9a\u5206\u9694\u7b26\u3001\u8df3\u8fc7\u884c\u3001\u9009\u62e9\u7279\u5b9a\u5217\u7b49\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    df = pd.read_csv('data.csv', sep=',', header=0)<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  2. \n
    \u8bfb\u53d6Excel\u6587\u4ef6<\/strong><\/p>\n<\/p>\n
    \u5982\u679c\u4f60\u7684\u6570\u636e\u5b58\u50a8\u5728Excel\u6587\u4ef6\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas.read_excel()<\/code>\u51fd\u6570\u3002\u8fd9\u4e2a\u51fd\u6570\u5141\u8bb8\u4f60\u6307\u5b9a\u5de5\u4f5c\u8868\u540d\u79f0\u3001\u6570\u636e\u7c7b\u578b\u7b49\u3002<\/p>\n<\/p>\n
    df = pd.read_excel('data.xlsx', sheet_name='Sheet1')<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001\u6e05\u6d17\u6570\u636e<\/p>\n<\/p>\n
    \u6570\u636e\u6e05\u6d17\u662f\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\u7684\u4e00\u4e2a\u5173\u952e\u6b65\u9aa4\u3002Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u65b9\u6cd5\u6765\u5e2e\u52a9\u4f60\u5904\u7406\u7f3a\u5931\u6570\u636e\u3001\u91cd\u590d\u6570\u636e\u4ee5\u53ca\u683c\u5f0f\u4e0d\u4e00\u81f4\u7684\u95ee\u9898\u3002<\/p>\n<\/p>\n
      \n
    1. \n
      \u5904\u7406\u7f3a\u5931\u6570\u636e<\/strong><\/p>\n<\/p>\n
      \u4f60\u53ef\u4ee5\u4f7f\u7528dropna()<\/code>\u51fd\u6570\u6765\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\uff0c\u6216\u8005\u4f7f\u7528fillna()<\/code>\u51fd\u6570\u7528\u7279\u5b9a\u503c\u586b\u5145\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
      df.dropna(inplace=True)<\/p>\n
      df.fillna(value=0, inplace=True)<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    2. \n
      \u5904\u7406\u91cd\u590d\u6570\u636e<\/strong><\/p>\n<\/p>\n
      \u4f7f\u7528drop_duplicates()<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u5220\u9664\u91cd\u590d\u884c\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u552f\u4e00\u6027\u3002<\/p>\n<\/p>\n
      df.drop_duplicates(inplace=True)<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n
    3. \n
      \u6570\u636e\u683c\u5f0f\u8f6c\u6362<\/strong><\/p>\n<\/p>\n
      Pandas\u5141\u8bb8\u4f60\u901a\u8fc7astype()<\/code>\u51fd\u6570\u8f6c\u6362\u6570\u636e\u7c7b\u578b\uff0c\u4f8b\u5982\u5c06\u5b57\u7b26\u4e32\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u683c\u5f0f\u3002<\/p>\n<\/p>\n
      df['date'] = pd.to_datetime(df['date'])<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e09\u3001\u5206\u6790\u6570\u636e<\/p>\n<\/p>\n
      \u4e00\u65e6\u6570\u636e\u88ab\u6e05\u6d17\uff0c\u4e0b\u4e00\u6b65\u5c31\u662f\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002Pandas\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u529f\u80fd\uff0c\u5305\u62ec\u5206\u7ec4\u3001\u805a\u5408\u3001\u7edf\u8ba1\u5206\u6790\u7b49\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u6570\u636e\u9009\u62e9\u548c\u8fc7\u6ee4<\/strong><\/p>\n<\/p>\n
        \u901a\u8fc7Pandas\u7684\u7d22\u5f15\u548c\u8fc7\u6ee4\u529f\u80fd\uff0c\u4f60\u53ef\u4ee5\u9009\u62e9\u7279\u5b9a\u884c\u548c\u5217\u8fdb\u884c\u5206\u6790\u3002\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u53ef\u4ee5\u8f7b\u677e\u5b9e\u73b0\u6761\u4ef6\u8fc7\u6ee4\u3002<\/p>\n<\/p>\n
        filtered_df = df[df['column'] > 10]<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      2. \n
        \u6570\u636e\u5206\u7ec4\u548c\u805a\u5408<\/strong><\/p>\n<\/p>\n
        groupby()<\/code>\u51fd\u6570\u5141\u8bb8\u4f60\u6839\u636e\u7279\u5b9a\u5217\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u8fdb\u884c\u805a\u5408\u64cd\u4f5c\uff0c\u5982\u6c42\u548c\u3001\u5e73\u5747\u503c\u7b49\u3002<\/p>\n<\/p>\n
        grouped = df.groupby('category').sum()<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n
      3. \n
        \u7edf\u8ba1\u5206\u6790<\/strong><\/p>\n<\/p>\n
        Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u7edf\u8ba1\u65b9\u6cd5\uff0c\u5982mean()<\/code>\u3001median()<\/code>\u3001std()<\/code>\u7b49\uff0c\u5e2e\u52a9\u4f60\u5feb\u901f\u83b7\u53d6\u6570\u636e\u7684\u7edf\u8ba1\u4fe1\u606f\u3002<\/p>\n<\/p>\n
        mean_value = df['column'].mean()<\/p>\n
        <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
        \u56db\u3001\u53ef\u89c6\u5316\u6570\u636e<\/p>\n<\/p>\n
        \u867d\u7136Pandas\u672c\u8eab\u4e0d\u5177\u5907\u5f3a\u5927\u7684\u53ef\u89c6\u5316\u529f\u80fd\uff0c\u4f46\u5b83\u4e0eMatplotlib\u548cSeaborn\u5e93\u65e0\u7f1d\u96c6\u6210\uff0c\u5e2e\u52a9\u4f60\u521b\u5efa\u5404\u79cd\u56fe\u8868\uff0c\u5982\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002<\/p>\n<\/p>\n
          \n
        1. \n
          \u4f7f\u7528Matplotlib\u8fdb\u884c\u53ef\u89c6\u5316<\/strong><\/p>\n<\/p>\n
          Matplotlib\u662fPython\u6700\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u4e4b\u4e00\uff0cPandas\u63d0\u4f9b\u4e86plot()<\/code>\u63a5\u53e3\u4e0e\u5176\u96c6\u6210\u3002<\/p>\n<\/p>\n
          import matplotlib.pyplot as plt<\/p>\n
          df['column'].plot(kind='line')<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n
        2. \n
          \u4f7f\u7528Seaborn\u8fdb\u884c\u9ad8\u7ea7\u53ef\u89c6\u5316<\/strong><\/p>\n<\/p>\n
          Seaborn\u57fa\u4e8eMatplotlib\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u53ef\u89c6\u5316\u529f\u80fd\uff0c\u5982\u70ed\u529b\u56fe\u3001\u7bb1\u7ebf\u56fe\u7b49\u3002<\/p>\n<\/p>\n
          import seaborn as sns<\/p>\n
          sns.boxplot(x='category', y='value', data=df)<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
          \u4e94\u3001\u4fdd\u5b58\u5206\u6790\u7ed3\u679c<\/p>\n<\/p>\n
          \u5728\u5b8c\u6210\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u4e4b\u540e\uff0c\u4f60\u53ef\u80fd\u9700\u8981\u5c06\u7ed3\u679c\u4fdd\u5b58\u4ee5\u4fbf\u540e\u7eed\u4f7f\u7528\u3002Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u5bfc\u51fa\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
            \n
          1. \n
            \u5bfc\u51fa\u4e3aCSV\u6587\u4ef6<\/strong><\/p>\n<\/p>\n
            \u4f7f\u7528to_csv()<\/code>\u51fd\u6570\u53ef\u4ee5\u5c06DataFrame\u5bfc\u51fa\u4e3aCSV\u6587\u4ef6\u3002<\/p>\n<\/p>\n
            df.to_csv('output.csv', index=False)<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n
          2. \n
            \u5bfc\u51fa\u4e3aExcel\u6587\u4ef6<\/strong><\/p>\n<\/p>\n
            \u4f7f\u7528to_excel()<\/code>\u51fd\u6570\u53ef\u4ee5\u5c06DataFrame\u5bfc\u51fa\u4e3aExcel\u6587\u4ef6\u3002<\/p>\n<\/p>\n
            df.to_excel('output.xlsx', index=False)<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
            \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528Python\u7684Pandas\u5e93\u9ad8\u6548\u5730\u8fdb\u884c\u6570\u636e\u5206\u6790\u3002\u638c\u63e1\u8fd9\u4e9b\u57fa\u672c\u64cd\u4f5c\u540e\uff0c\u4f60\u53ef\u4ee5\u6df1\u5165\u5b66\u4e60\u66f4\u591a\u9ad8\u7ea7\u529f\u80fd\uff0c\u4ee5\u6ee1\u8db3\u66f4\u590d\u6742\u7684\u6570\u636e\u5206\u6790\u9700\u6c42\u3002<\/p>\n<\/p>\n
            \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
             \u5982\u4f55\u7528Python\u7684Pandas\u5e93\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\uff1f<\/strong>
            Pandas\u5e93\u5728\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u65f6\u8868\u73b0\u51fa\u8272\u3002\u9996\u5148\uff0c\u53ef\u4ee5\u5229\u7528Pandas\u7684read_csv()<\/code>\u51fd\u6570\u8bfb\u53d6\u5927\u578bCSV\u6587\u4ef6\uff0c\u5e76\u4f7f\u7528\u53c2\u6570\u5982chunksize<\/code>\u6765\u5206\u5757\u8bfb\u53d6\u6570\u636e\u3002\u8fd9\u79cd\u65b9\u5f0f\u53ef\u4ee5\u6709\u6548\u51cf\u5c11\u5185\u5b58\u5360\u7528\u3002\u5176\u6b21\uff0c\u4f7f\u7528DataFrame<\/code>\u5bf9\u8c61\u7684\u8fc7\u6ee4\u548c\u5206\u7ec4\u529f\u80fd\u53ef\u4ee5\u5feb\u901f\u5904\u7406\u6570\u636e\uff0c\u907f\u514d\u4e00\u6b21\u6027\u52a0\u8f7d\u6574\u4e2a\u6570\u636e\u96c6\u3002\u6700\u540e\uff0c\u5229\u7528Pandas\u5185\u7f6e\u7684\u9ad8\u6548\u7b97\u6cd5\u8fdb\u884c\u6570\u636e\u5206\u6790\u548c\u64cd\u4f5c\uff0c\u80fd\u591f\u52a0\u901f\u5904\u7406\u901f\u5ea6\u3002<\/p>\n
            Pandas\u5e93\u4e2d\u5e38\u7528\u7684\u6570\u636e\u6e05\u6d17\u65b9\u6cd5\u6709\u54ea\u4e9b\uff1f<\/strong>
            \u5728Pandas\u4e2d\uff0c\u6570\u636e\u6e05\u6d17\u662f\u4e00\u4e2a\u91cd\u8981\u6b65\u9aa4\u3002\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ecdropna()<\/code>\u53bb\u9664\u7f3a\u5931\u503c\uff0cfillna()<\/code>\u586b\u8865\u7f3a\u5931\u6570\u636e\uff0c\u4ee5\u53careplace()<\/code>\u66ff\u6362\u7279\u5b9a\u503c\u3002\u6b64\u5916\uff0castype()<\/code>\u53ef\u4ee5\u7528\u4e8e\u66f4\u6539\u6570\u636e\u7c7b\u578b\uff0c\u786e\u4fdd\u6570\u636e\u7684\u4e00\u81f4\u6027\u3002\u4f7f\u7528duplicated()<\/code>\u548cdrop_duplicates()<\/code>\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u548c\u5220\u9664\u91cd\u590d\u6570\u636e\uff0c\u786e\u4fdd\u6570\u636e\u96c6\u7684\u552f\u4e00\u6027\u3002<\/p>\n
            \u5982\u4f55\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\uff1f<\/strong>
            Pandas\u5e93\u867d\u7136\u4e3b\u8981\u7528\u4e8e\u6570\u636e\u5904\u7406\uff0c\u4f46\u4e5f\u53ef\u4ee5\u4e0eMatplotlib\u548cSeaborn\u7b49\u53ef\u89c6\u5316\u5e93\u7ed3\u5408\u4f7f\u7528\u3002\u901a\u8fc7DataFrame.plot()<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5feb\u901f\u751f\u6210\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u548c\u6563\u70b9\u56fe\u7b49\u57fa\u672c\u56fe\u8868\u3002\u5bf9\u4e8e\u66f4\u590d\u6742\u7684\u53ef\u89c6\u5316\uff0c\u53ef\u4ee5\u5c06Pandas\u6570\u636e\u6846\u4f20\u9012\u7ed9Seaborn\u7684\u7ed8\u56fe\u51fd\u6570\uff0c\u4ee5\u4fbf\u521b\u5efa\u66f4\u5177\u4fe1\u606f\u91cf\u548c\u7f8e\u89c2\u7684\u56fe\u8868\u3002\u8fd9\u79cd\u65b9\u6cd5\u4f7f\u5f97\u6570\u636e\u5206\u6790\u548c\u7ed3\u679c\u5448\u73b0\u66f4\u52a0\u76f4\u89c2\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u8981\u7528Python\u7684Pandas\u5e93\u8fdb\u884c\u6570\u636e\u5206\u6790\uff0c\u4f60\u9700\u8981\u638c\u63e1\u5982\u4f55\u5bfc\u5165\u6570\u636e\u3001\u6e05\u6d17\u6570\u636e\u3001\u5206\u6790\u6570\u636e\u548c\u53ef\u89c6\u5316\u6570\u636e\u3002Pand […]","protected":false},"author":3,"featured_media":953640,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/953635"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=953635"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/953635\/revisions"}],"predecessor-version":[{"id":953643,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/953635\/revisions\/953643"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/953640"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=953635"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=953635"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=953635"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}