{"id":997976,"date":"2024-12-27T09:28:47","date_gmt":"2024-12-27T01:28:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/997976.html"},"modified":"2024-12-27T09:28:51","modified_gmt":"2024-12-27T01:28:51","slug":"python%e7%bb%98%e5%9b%be%e5%a6%82%e4%bd%95%e5%af%bc%e5%85%a5%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/997976.html","title":{"rendered":"python\u7ed8\u56fe\u5982\u4f55\u5bfc\u5165\u6570\u636e"},"content":{"rendered":"

\"python\u7ed8\u56fe\u5982\u4f55\u5bfc\u5165\u6570\u636e\"<\/p>\n

\u4e00\u3001\u5bfc\u5165\u6570\u636e\u7684\u65b9\u6cd5\u6982\u8ff0 <\/p>\n<\/p>\n

\u5728Python\u4e2d\u7ed8\u56fe\u65f6\uff0c\u5bfc\u5165\u6570\u636e\u7684\u65b9\u5f0f\u591a\u79cd\u591a\u6837\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528Pandas\u5e93\u8bfb\u53d6CSV\u6587\u4ef6\u3001\u5229\u7528Numpy\u5e93\u52a0\u8f7d\u6587\u672c\u6570\u636e\u3001\u901a\u8fc7SQLAlchemy\u4ece\u6570\u636e\u5e93\u4e2d\u63d0\u53d6\u6570\u636e\u3001\u76f4\u63a5\u4f7f\u7528Python\u7684\u5185\u7f6e\u51fd\u6570\u8bfb\u53d6\u6587\u672c\u6587\u4ef6<\/strong>\u3002\u5176\u4e2d\uff0cPandas\u5e93\u56e0\u5176\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\u800c\u5907\u53d7\u63a8\u5d07\u3002\u901a\u8fc7Pandas\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u4e0d\u4ec5\u53ef\u4ee5\u8f7b\u677e\u5bfc\u5165\u6570\u636e\uff0c\u8fd8\u80fd\u5bf9\u6570\u636e\u8fdb\u884c\u5404\u79cd\u9884\u5904\u7406\u64cd\u4f5c\uff0c\u5982\u8fc7\u6ee4\u3001\u5206\u7ec4\u7b49\uff0c\u8fd9\u4e3a\u540e\u7eed\u7684\u7ed8\u56fe\u63d0\u4f9b\u4e86\u6781\u5927\u7684\u4fbf\u5229\u3002<\/p>\n<\/p>\n

Pandas\u662f\u4e00\u4e2a\u7528\u4e8e\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u7684\u5f3a\u5927\u5de5\u5177\uff0c\u5176\u4e3b\u8981\u7279\u6027\u4e4b\u4e00\u662fDataFrame\u5bf9\u8c61\u3002DataFrame\u662f\u4e00\u4e2a\u4e8c\u7ef4\u8868\u683c\uff0c\u7c7b\u4f3c\u4e8e\u7535\u5b50\u8868\u683c\u6216SQL\u8868\u3002\u901a\u8fc7Pandas\uff0c\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6\u548c\u5904\u7406\u5404\u79cd\u683c\u5f0f\u7684\u6570\u636e\u6587\u4ef6\uff0c\u5982CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u3002\u8fd9\u4f7f\u5f97Pandas\u6210\u4e3a\u6570\u636e\u79d1\u5b66\u548c\u5206\u6790\u9886\u57df\u7684\u4e00\u4e2a\u91cd\u8981\u5de5\u5177\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u4f7f\u7528Pandas\u8bfb\u53d6CSV\u6587\u4ef6 <\/p>\n<\/p>\n

Pandas\u5e93\u63d0\u4f9b\u4e86\u4e00\u4e2a\u975e\u5e38\u7b80\u5355\u7684\u65b9\u6cd5\u6765\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u5373\u901a\u8fc7pandas.read_csv()<\/code>\u51fd\u6570\u3002\u8fd9\u4e2a\u51fd\u6570\u4e0d\u4ec5\u53ef\u4ee5\u8bfb\u53d6CSV\u6587\u4ef6\uff0c\u8fd8\u53ef\u4ee5\u901a\u8fc7\u5404\u79cd\u53c2\u6570\u81ea\u5b9a\u4e49\u8bfb\u53d6\u65b9\u5f0f\uff0c\u5982\u6307\u5b9a\u5206\u9694\u7b26\u3001\u9009\u62e9\u7279\u5b9a\u5217\u3001\u89e3\u6790\u65e5\u671f\u7b49\u3002 <\/p>\n<\/p>\n

    \n
  1. \n

    \u57fa\u672c\u8bfb\u53d6\u65b9\u6cd5<\/strong> <\/p>\n<\/p>\n

    \u4f7f\u7528Pandas\u8bfb\u53d6CSV\u6587\u4ef6\u7684\u57fa\u672c\u65b9\u6cd5\u5982\u4e0b\uff1a<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
    df = pd.read_csv('data.csv')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8fd9\u91cc\uff0c'data.csv'<\/code>\u662f\u6587\u4ef6\u7684\u8def\u5f84\uff0cdf<\/code>\u662f\u8bfb\u53d6\u540e\u7684DataFrame\u5bf9\u8c61\u3002\u8bfb\u53d6\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7df.head()<\/code>\u67e5\u770b\u6570\u636e\u7684\u524d\u51e0\u884c\u3002<\/p>\n<\/p>\n<\/li>\n
  2. \n
    \u81ea\u5b9a\u4e49\u8bfb\u53d6\u53c2\u6570<\/strong>  <\/p>\n<\/p>\n
    \u5728\u8bfb\u53d6CSV\u6587\u4ef6\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u5404\u79cd\u53c2\u6570\u81ea\u5b9a\u4e49\u8bfb\u53d6\u65b9\u5f0f\uff1a<\/p>\n<\/p>\n
      \n
    • sep<\/code>: \u6307\u5b9a\u5206\u9694\u7b26\uff0c\u9ed8\u8ba4\u662f\u9017\u53f7\u3002<\/li>\n
    • usecols<\/code>: \u6307\u5b9a\u9700\u8981\u8bfb\u53d6\u7684\u5217\u3002<\/li>\n
    • parse_dates<\/code>: \u6307\u5b9a\u9700\u8981\u89e3\u6790\u4e3a\u65e5\u671f\u7684\u5217\u3002<\/li>\n<\/ul>\n
      \u4f8b\u5982\uff1a<\/p>\n<\/p>\n
      df = pd.read_csv('data.csv', sep=';', usecols=['Name', 'Age'], parse_dates=['Date'])<\/p>\n
      <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e09\u3001\u5229\u7528Numpy\u52a0\u8f7d\u6587\u672c\u6570\u636e  <\/p>\n<\/p>\n
      Numpy\u5e93\u63d0\u4f9b\u4e86\u4e00\u79cd\u5feb\u901f\u8bfb\u53d6\u6587\u672c\u6570\u636e\u7684\u65b9\u6cd5\uff0c\u5c24\u5176\u9002\u5408\u6570\u503c\u6570\u636e\u3002\u901a\u8fc7numpy.loadtxt()<\/code>\u6216numpy.genfromtxt()<\/code>\u51fd\u6570\u53ef\u4ee5\u8bfb\u53d6\u6587\u672c\u6587\u4ef6\u3002  <\/p>\n<\/p>\n
        \n
      1. \n
        \u4f7f\u7528numpy.loadtxt()<\/strong>  <\/p>\n<\/p>\n
        loadtxt()<\/code>\u9002\u7528\u4e8e\u7ed3\u6784\u7b80\u5355\u4e14\u65e0\u7f3a\u5931\u503c\u7684\u6587\u672c\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
        import numpy as np<\/p>\n
        \u52a0\u8f7d\u6587\u672c\u6587\u4ef6<\/strong><\/h2>\n
        data = np.loadtxt('data.txt', delimiter=',')<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u8fd9\u91cc\uff0cdelimiter<\/code>\u6307\u5b9a\u6570\u636e\u7684\u5206\u9694\u7b26\u3002<\/p>\n<\/p>\n<\/li>\n
      2. \n
        \u4f7f\u7528numpy.genfromtxt()<\/strong>  <\/p>\n<\/p>\n
        genfromtxt()<\/code>\u9002\u7528\u4e8e\u6709\u7f3a\u5931\u503c\u7684\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
        data = np.genfromtxt('data.txt', delimiter=',', filling_values=0)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        filling_values<\/code>\u53c2\u6570\u7528\u4e8e\u6307\u5b9a\u7f3a\u5931\u503c\u7684\u66ff\u4ee3\u503c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
        \u56db\u3001\u901a\u8fc7SQLAlchemy\u4ece\u6570\u636e\u5e93\u4e2d\u63d0\u53d6\u6570\u636e  <\/p>\n<\/p>\n
        SQLAlchemy\u662f\u4e00\u4e2aSQL\u5de5\u5177\u5305\u548c\u5bf9\u8c61\u5173\u7cfb\u6620\u5c04\u5668\uff08ORM\uff09\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u4ece\u6570\u636e\u5e93\u4e2d\u63d0\u53d6\u6570\u636e\u3002  <\/p>\n<\/p>\n
          \n
        1. \n
          \u5efa\u7acb\u6570\u636e\u5e93\u8fde\u63a5<\/strong>  <\/p>\n<\/p>\n
          \u4f7f\u7528SQLAlchemy\u8fde\u63a5\u6570\u636e\u5e93\uff1a<\/p>\n<\/p>\n
          from sqlalchemy import create_engine<\/p>\n
          \u521b\u5efa\u6570\u636e\u5e93\u5f15\u64ce<\/strong><\/h2>\n
          engine = create_engine('sqlite:\/\/\/data.db')<\/p>\n
          \u67e5\u8be2\u6570\u636e<\/strong><\/h2>\n
          df = pd.read_sql('SELECT * FROM table_name', con=engine)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u8fd9\u91cc\uff0c'sqlite:\/\/\/data.db'<\/code>\u662fSQLite\u6570\u636e\u5e93\u7684\u8fde\u63a5\u5b57\u7b26\u4e32\uff0ctable_name<\/code>\u662f\u6570\u636e\u5e93\u4e2d\u7684\u8868\u540d\u3002<\/p>\n<\/p>\n<\/li>\n
        2. \n
          \u67e5\u8be2\u548c\u5904\u7406\u6570\u636e<\/strong>  <\/p>\n<\/p>\n
          \u901a\u8fc7SQLAlchemy\u548cPandas\uff0c\u53ef\u4ee5\u8f7b\u677e\u67e5\u8be2\u548c\u5904\u7406\u6570\u636e\uff1a<\/p>\n<\/p>\n
          # \u67e5\u8be2\u7279\u5b9a\u5217\u7684\u6570\u636e<\/p>\n
          df = pd.read_sql('SELECT name, age FROM table_name WHERE age > 20', con=engine)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528Pandas\u7684\u5404\u79cd\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u8fdb\u4e00\u6b65\u5904\u7406\uff0c\u5982\u6392\u5e8f\u3001\u5206\u7ec4\u7b49\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
          \u4e94\u3001\u4f7f\u7528Python\u5185\u7f6e\u51fd\u6570\u8bfb\u53d6\u6587\u672c\u6587\u4ef6  <\/p>\n<\/p>\n
          Python\u7684\u5185\u7f6e\u51fd\u6570\u4e5f\u53ef\u4ee5\u7528\u4e8e\u8bfb\u53d6\u7b80\u5355\u7684\u6587\u672c\u6587\u4ef6\uff0c\u9002\u7528\u4e8e\u5c0f\u578b\u6570\u636e\u96c6\u6216\u7b80\u5355\u7684\u6587\u672c\u683c\u5f0f\u3002  <\/p>\n<\/p>\n
            \n
          1. \n
            \u8bfb\u53d6\u6574\u4e2a\u6587\u4ef6<\/strong>  <\/p>\n<\/p>\n
            \u4f7f\u7528open()<\/code>\u51fd\u6570\u8bfb\u53d6\u6574\u4e2a\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
            # \u6253\u5f00\u6587\u4ef6<\/p>\n
            with open('data.txt', 'r') as file:<\/p>\n
                data = file.read()<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u8bfb\u53d6\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528split()<\/code>\u65b9\u6cd5\u5c06\u6587\u672c\u8f6c\u6362\u4e3a\u5217\u8868\u3002<\/p>\n<\/p>\n<\/li>\n
          2. \n
            \u9010\u884c\u8bfb\u53d6\u6587\u4ef6<\/strong>  <\/p>\n<\/p>\n
            \u53ef\u4ee5\u9010\u884c\u8bfb\u53d6\u6587\u4ef6\uff0c\u9002\u7528\u4e8e\u5927\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
            with open('data.txt', 'r') as file:<\/p>\n
                for line in file:<\/p>\n
                    print(line.strip())<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u9010\u884c\u8bfb\u53d6\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528strip()<\/code>\u65b9\u6cd5\u53bb\u9664\u884c\u672b\u7684\u6362\u884c\u7b26\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
            \u516d\u3001\u6570\u636e\u9884\u5904\u7406\u548c\u6e05\u6d17  <\/p>\n<\/p>\n
            \u5728\u5bfc\u5165\u6570\u636e\u540e\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u548c\u6e05\u6d17\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u8d28\u91cf\u548c\u4e00\u81f4\u6027\u3002  <\/p>\n<\/p>\n
              \n
            1. \n
              \u5904\u7406\u7f3a\u5931\u503c<\/strong>  <\/p>\n<\/p>\n
              \u7f3a\u5931\u503c\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e38\u89c1\u95ee\u9898\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u7684fillna()<\/code>\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\uff1a<\/p>\n<\/p>\n
              df.fillna(0, inplace=True)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u4f7f\u7528dropna()<\/code>\u65b9\u6cd5\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\u3002<\/p>\n<\/p>\n<\/li>\n
            2. \n
              \u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/strong>  <\/p>\n<\/p>\n
              \u5728\u5206\u6790\u6570\u636e\u65f6\uff0c\u786e\u4fdd\u6570\u636e\u7c7b\u578b\u7684\u4e00\u81f4\u6027\u975e\u5e38\u91cd\u8981\u3002\u53ef\u4ee5\u4f7f\u7528astype()<\/code>\u65b9\u6cd5\u8f6c\u6362\u6570\u636e\u7c7b\u578b\uff1a<\/p>\n<\/p>\n
              df['Age'] = df['Age'].astype(int)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u8f6c\u6362\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7dtypes<\/code>\u5c5e\u6027\u67e5\u770bDataFrame\u4e2d\u7684\u6570\u636e\u7c7b\u578b\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
              \u4e03\u3001\u6570\u636e\u53ef\u89c6\u5316  <\/p>\n<\/p>\n
              \u5728\u6570\u636e\u5bfc\u5165\u548c\u9884\u5904\u7406\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u7684\u5404\u79cd\u7ed8\u56fe\u5e93\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3002\u5e38\u7528\u7684\u7ed8\u56fe\u5e93\u5305\u62ecMatplotlib\u3001Seaborn\u3001Plotly\u7b49\u3002  <\/p>\n<\/p>\n
                \n
              1. \n
                Matplotlib\u5e93<\/strong>  <\/p>\n<\/p>\n
                Matplotlib\u662fPython\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u7ed8\u5236\u7b80\u5355\u7684\u56fe\u5f62\uff1a<\/p>\n<\/p>\n
                import matplotlib.pyplot as plt<\/p>\n
                \u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
                plt.plot(df['Date'], df['Value'])<\/p>\n
                plt.xlabel('Date')<\/p>\n
                plt.ylabel('Value')<\/p>\n
                plt.title('Time Series')<\/p>\n
                plt.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n<\/li>\n
              2. \n
                Seaborn\u5e93<\/strong>  <\/p>\n<\/p>\n
                Seaborn\u662f\u5728Matplotlib\u57fa\u7840\u4e0a\u6784\u5efa\u7684\u9ad8\u7ea7\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u7ed8\u5236\u7f8e\u89c2\u4e14\u590d\u6742\u7684\u56fe\u5f62\uff1a<\/p>\n<\/p>\n
                import seaborn as sns<\/p>\n
                \u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
                sns.boxplot(x='Category', y='Value', data=df)<\/p>\n
                plt.title('Box Plot')<\/p>\n
                plt.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n<\/li>\n
              3. \n
                Plotly\u5e93<\/strong>  <\/p>\n<\/p>\n
                Plotly\u662f\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u7ed8\u56fe\u5e93\uff0c\u9002\u5408\u7ed8\u5236\u4ea4\u4e92\u5f0f\u56fe\u5f62\uff1a<\/p>\n<\/p>\n
                import plotly.express as px<\/p>\n
                \u7ed8\u5236\u4ea4\u4e92\u5f0f\u6298\u7ebf\u56fe<\/strong><\/h2>\n
                fig = px.line(df, x='Date', y='Value', title='Interactive Time Series')<\/p>\n
                fig.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
                \u516b\u3001\u603b\u7ed3  <\/p>\n<\/p>\n
                \u5728Python\u4e2d\uff0c\u5bfc\u5165\u6570\u636e\u662f\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u7684\u7b2c\u4e00\u6b65\u3002\u901a\u8fc7\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u5bfc\u5165\u65b9\u6cd5\uff0c\u5982Pandas\u3001Numpy\u3001SQLAlchemy\u7b49\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u8bfb\u53d6\u548c\u5904\u7406\u6570\u636e\u3002\u4e3a\u4e86\u786e\u4fdd\u6570\u636e\u7684\u8d28\u91cf\u548c\u4e00\u81f4\u6027\uff0c\u5728\u5bfc\u5165\u6570\u636e\u540e\u901a\u5e38\u9700\u8981\u8fdb\u884c\u6570\u636e\u9884\u5904\u7406\u548c\u6e05\u6d17\u3002\u8fd9\u4e9b\u6b65\u9aa4\u4e3a\u540e\u7eed\u7684\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u5960\u5b9a\u4e86\u575a\u5b9e\u7684\u57fa\u7840\u3002\u65e0\u8bba\u662f\u4f7f\u7528Matplotlib\u3001Seaborn\u8fd8\u662fPlotly\uff0c\u90fd\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u7ed8\u56fe\u5e93\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u5448\u73b0\u6570\u636e\u7684\u7279\u5f81\u548c\u8d8b\u52bf\u3002<\/p>\n<\/p>\n
                \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                 \u5982\u4f55\u5728Python\u4e2d\u5bfc\u5165CSV\u6587\u4ef6\u8fdb\u884c\u7ed8\u56fe\uff1f<\/strong>
                \u5728Python\u4e2d\uff0c\u5bfc\u5165CSV\u6587\u4ef6\u53ef\u4ee5\u4f7f\u7528pandas\u5e93\u3002\u9996\u5148\uff0c\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5pandas\u5e93\u3002\u4f7f\u7528pd.read_csv('\u6587\u4ef6\u8def\u5f84.csv')<\/code>\u53ef\u4ee5\u5c06\u6570\u636e\u5bfc\u5165\u4e3aDataFrame\u5bf9\u8c61\u3002\u4e4b\u540e\uff0c\u53ef\u4ee5\u5229\u7528matplotlib\u6216seaborn\u7b49\u5e93\u8fdb\u884c\u7ed8\u56fe\uff0c\u4ee3\u7801\u793a\u4f8b\uff1a  <\/p>\n
                import pandas as pd\nimport matplotlib.pyplot as plt\n\ndata = pd.read_csv('data.csv')\nplt.plot(data['x'], data['y'])\nplt.show()\n<\/code><\/pre>\n
                \u53ef\u4ee5\u4f7f\u7528\u54ea\u4e9b\u6587\u4ef6\u683c\u5f0f\u5bfc\u5165\u6570\u636e\u8fdb\u884cPython\u7ed8\u56fe\uff1f<\/strong>
                \u9664\u4e86CSV\u6587\u4ef6\uff0cPython\u8fd8\u652f\u6301\u591a\u79cd\u6587\u4ef6\u683c\u5f0f\u7684\u5bfc\u5165\uff0c\u4f8b\u5982Excel\u6587\u4ef6\uff08.xlsx\uff09\uff0c\u4f7f\u7528pandas\u7684pd.read_excel('\u6587\u4ef6\u8def\u5f84.xlsx')<\/code>\u8fdb\u884c\u5bfc\u5165\u3002\u6b64\u5916\uff0cJSON\u6587\u4ef6\u3001SQL\u6570\u636e\u5e93\u3001\u751a\u81f3\u76f4\u63a5\u4ece\u7f51\u9875\u6293\u53d6\u7684\u6570\u636e\u4e5f\u53ef\u4ee5\u901a\u8fc7\u76f8\u5e94\u7684\u5e93\u8fdb\u884c\u5904\u7406\uff0c\u6700\u7ec8\u7528\u4e8e\u7ed8\u56fe\u3002<\/p>\n
                \u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u7f3a\u5931\u6570\u636e\u4ee5\u786e\u4fdd\u7ed8\u56fe\u7684\u51c6\u786e\u6027\uff1f<\/strong>
                \u7f3a\u5931\u6570\u636e\u53ef\u80fd\u4f1a\u5f71\u54cd\u7ed8\u56fe\u7684\u6548\u679c\u3002\u5728\u4f7f\u7528pandas\u5bfc\u5165\u6570\u636e\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528data.dropna()<\/code>\u6765\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\uff0c\u6216\u8005\u4f7f\u7528data.fillna(0)<\/code>\u5c06\u7f3a\u5931\u503c\u586b\u5145\u4e3a0\u3002\u9488\u5bf9\u7279\u5b9a\u9700\u6c42\uff0c\u9009\u62e9\u9002\u5408\u7684\u65b9\u6cd5\u8fdb\u884c\u6570\u636e\u6e05\u7406\uff0c\u4ee5\u786e\u4fdd\u7ed8\u56fe\u7ed3\u679c\u7684\u51c6\u786e\u6027\u548c\u53ef\u8bfb\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4e00\u3001\u5bfc\u5165\u6570\u636e\u7684\u65b9\u6cd5\u6982\u8ff0 \u5728Python\u4e2d\u7ed8\u56fe\u65f6\uff0c\u5bfc\u5165\u6570\u636e\u7684\u65b9\u5f0f\u591a\u79cd\u591a\u6837\uff0c\u5e38\u7528\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528Pandas\u5e93\u8bfb\u53d6 […]","protected":false},"author":3,"featured_media":997992,"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\/997976"}],"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=997976"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/997976\/revisions"}],"predecessor-version":[{"id":997993,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/997976\/revisions\/997993"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/997992"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=997976"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=997976"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=997976"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}