{"id":996679,"date":"2024-12-27T09:17:01","date_gmt":"2024-12-27T01:17:01","guid":{"rendered":""},"modified":"2024-12-27T09:17:04","modified_gmt":"2024-12-27T01:17:04","slug":"python%e5%a6%82%e4%bd%95%e5%ae%9e%e7%8e%b0%e5%a4%a7%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/996679.html","title":{"rendered":"python\u5982\u4f55\u5b9e\u73b0\u5927\u6570\u636e"},"content":{"rendered":"

\"python\u5982\u4f55\u5b9e\u73b0\u5927\u6570\u636e\"<\/p>\n

Python\u5b9e\u73b0\u5927\u6570\u636e\u7684\u4e3b\u8981\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u3001\u901a\u8fc7Dask\u8fdb\u884c\u5e76\u884c\u8ba1\u7b97\u3001\u5229\u7528PySpark\u5904\u7406\u5206\u5e03\u5f0f\u6570\u636e\u3002\u8fd9\u4e9b\u5de5\u5177\u80fd\u591f\u6709\u6548\u5730\u5904\u7406\u548c\u5206\u6790\u5927\u89c4\u6a21\u6570\u636e\u96c6\u3002<\/strong><\/p>\n<\/p>\n

\u5728\u4f17\u591a\u5904\u7406\u5927\u6570\u636e\u7684\u5de5\u5177\u4e2d\uff0cPandas\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u4e14\u5bb9\u6613\u4e0a\u624b\u7684\u5e93\uff0c\u9002\u5408\u7528\u4e8e\u5904\u7406\u4e2d\u7b49\u89c4\u6a21\u7684\u6570\u636e\u96c6\u3002\u5bf9\u4e8e\u8d85\u8fc7\u5355\u53f0\u673a\u5668\u5185\u5b58\u7684\u5927\u6570\u636e\u96c6\uff0cDask\u80fd\u591f\u5e2e\u52a9\u4f60\u5c06Pandas\u7684\u64cd\u4f5c\u6269\u5c55\u5230\u591a\u4e2a\u5904\u7406\u5668\u6838\u6216\u96c6\u7fa4\u3002PySpark\u5219\u662f\u57fa\u4e8eApache Spark\u7684Python API\uff0c\u9002\u5408\u5904\u7406\u5927\u89c4\u6a21\u5206\u5e03\u5f0f\u6570\u636e\u96c6\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u5de5\u5177\u7684\u5e94\u7528\u548c\u7279\u70b9\u3002<\/p>\n<\/p>\n

\u4e00\u3001PANDAS\u8fdb\u884c\u6570\u636e\u5904\u7406<\/p>\n<\/p>\n

Pandas\u662fPython\u4e2d\u6700\u4e3a\u6d41\u884c\u7684\u6570\u636e\u5904\u7406\u5e93\u4e4b\u4e00\uff0c\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\u3002Pandas\u7684\u6838\u5fc3\u6570\u636e\u7ed3\u6784\u662fDataFrame\uff0c\u5b83\u7c7b\u4f3c\u4e8eExcel\u8868\u683c\u6216SQL\u8868\u683c\uff0c\u63d0\u4f9b\u4e30\u5bcc\u7684\u51fd\u6570\u548c\u65b9\u6cd5\u6765\u64cd\u4f5c\u6570\u636e\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    Pandas\u7684\u57fa\u672c\u4f7f\u7528<\/strong><\/p>\n<\/p>\n

    Pandas\u53ef\u4ee5\u901a\u8fc7CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u591a\u79cd\u683c\u5f0f\u8bfb\u53d6\u6570\u636e\uff0c\u4f7f\u7528read_csv()<\/code>\u3001read_excel()<\/code>\u7b49\u65b9\u6cd5\u5373\u53ef\u5b9e\u73b0\u6570\u636e\u5bfc\u5165\u3002Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u64cd\u4f5c\u65b9\u6cd5\uff0c\u5982\u7b5b\u9009\u3001\u6392\u5e8f\u3001\u5206\u7ec4\u3001\u5408\u5e76\u7b49\u3002<\/p>\n<\/p>\n

    \u4e3e\u4f8b\u6765\u8bf4\uff0c\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u7528\u6237\u4fe1\u606f\u7684CSV\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u8bfb\u53d6\u5e76\u7b80\u5355\u5206\u6790\u6570\u636e\uff1a<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
    df = pd.read_csv('users.csv')<\/p>\n
    \u663e\u793a\u6570\u636e\u7684\u524d\u4e94\u884c<\/strong><\/h2>\n
    print(df.head())<\/p>\n
    \u6309\u7167\u5e74\u9f84\u8fdb\u884c\u6392\u5e8f<\/strong><\/h2>\n
    sorted_df = df.sort_values(by='age')<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n
  2. \n
    Pandas\u7684\u9ad8\u7ea7\u529f\u80fd<\/strong><\/p>\n<\/p>\n
    Pandas\u8fd8\u652f\u6301\u590d\u6742\u7684\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u529f\u80fd\uff0c\u5982\u900f\u89c6\u8868\u3001\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3001\u591a\u91cd\u7d22\u5f15\u7b49\u3002\u900f\u89c6\u8868\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u6c47\u603b\u548c\u805a\u5408\uff0c\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u5141\u8bb8\u5904\u7406\u65e5\u671f\u65f6\u95f4\u6570\u636e\uff0c\u800c\u591a\u91cd\u7d22\u5f15\u53ef\u4ee5\u8ba9\u7528\u6237\u5728\u591a\u7ef4\u6570\u636e\u4e0a\u8fdb\u884c\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
    \u6bd4\u5982\uff0c\u8981\u521b\u5efa\u4e00\u4e2a\u900f\u89c6\u8868\u6765\u67e5\u770b\u6bcf\u4e2a\u57ce\u5e02\u7684\u5e73\u5747\u5e74\u9f84\uff0c\u53ef\u4ee5\u8fd9\u6837\u505a\uff1a<\/p>\n<\/p>\n
    pivot_table = df.pivot_table(values='age', index='city', aggfunc='mean')<\/p>\n
    print(pivot_table)<\/p>\n
    <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001DASK\u8fdb\u884c\u5e76\u884c\u8ba1\u7b97<\/p>\n<\/p>\n
    Dask\u662f\u4e00\u4e2a\u7075\u6d3b\u7684\u5e76\u884c\u8ba1\u7b97\u5e93\uff0c\u5141\u8bb8\u5728\u4e0d\u6539\u53d8\u73b0\u6709\u4ee3\u7801\u7684\u60c5\u51b5\u4e0b\u6269\u5c55Pandas\u8ba1\u7b97\u5230\u591a\u4e2a\u5904\u7406\u5668\u6838\u6216\u96c6\u7fa4\u3002\u5b83\u901a\u8fc7\u5ef6\u8fdf\u8ba1\u7b97\u548c\u52a8\u6001\u4efb\u52a1\u8c03\u5ea6\u6765\u5b9e\u73b0\u9ad8\u6548\u7684\u5e76\u884c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
      \n
    1. \n
      Dask\u7684\u57fa\u672c\u6982\u5ff5<\/strong><\/p>\n<\/p>\n
      Dask\u7684\u6838\u5fc3\u6982\u5ff5\u662f\u4efb\u52a1\u56fe\uff08task graph\uff09\uff0c\u5b83\u4ee5\u6709\u5411\u65e0\u73af\u56fe\uff08DAG\uff09\u7684\u5f62\u5f0f\u8868\u793a\u8ba1\u7b97\u4efb\u52a1\u53ca\u5176\u4f9d\u8d56\u5173\u7cfb\u3002Dask\u901a\u8fc7\u8c03\u5ea6\u5668\u52a8\u6001\u89e3\u6790\u548c\u6267\u884c\u8fd9\u4e9b\u4efb\u52a1\uff0c\u4ece\u800c\u5b9e\u73b0\u5e76\u884c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
      Dask\u7684DataFrame API\u4e0ePandas\u517c\u5bb9\uff0c\u8fd9\u610f\u5473\u7740\u4f60\u53ef\u4ee5\u4f7f\u7528\u7c7b\u4f3cPandas\u7684\u8bed\u6cd5\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\uff0c\u800cDask\u4f1a\u8d1f\u8d23\u5904\u7406\u5e76\u884c\u5316\u3002<\/p>\n<\/p>\n<\/li>\n
    2. \n
      Dask\u7684\u5e94\u7528\u5b9e\u4f8b<\/strong><\/p>\n<\/p>\n
      \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5927\u89c4\u6a21\u7684CSV\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528Dask\u8bfb\u53d6\u5e76\u5904\u7406\u6570\u636e\uff1a<\/p>\n<\/p>\n
      import dask.dataframe as dd<\/p>\n
      \u8bfb\u53d6\u5927\u89c4\u6a21CSV\u6587\u4ef6<\/strong><\/h2>\n
      ddf = dd.read_csv('large_users.csv')<\/p>\n
      \u8ba1\u7b97\u6bcf\u4e2a\u57ce\u5e02\u7684\u5e73\u5747\u5e74\u9f84<\/strong><\/h2>\n
      mean_age = ddf.groupby('city')['age'].mean().compute()<\/p>\n
      print(mean_age)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cread_csv()<\/code>\u65b9\u6cd5\u4f1a\u521b\u5efa\u4e00\u4e2aDask DataFrame\uff0ccompute()<\/code>\u65b9\u6cd5\u5219\u7528\u4e8e\u6267\u884c\u5e76\u884c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e09\u3001PYSPARK\u5904\u7406\u5206\u5e03\u5f0f\u6570\u636e<\/p>\n<\/p>\n
      PySpark\u662fApache Spark\u7684Python\u63a5\u53e3\uff0c\u9002\u5408\u5904\u7406\u5927\u89c4\u6a21\u5206\u5e03\u5f0f\u6570\u636e\u96c6\u3002Spark\u662f\u4e00\u79cd\u5feb\u901f\u3001\u901a\u7528\u7684\u5206\u5e03\u5f0f\u8ba1\u7b97\u7cfb\u7edf\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u529f\u80fd\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        Spark\u7684\u57fa\u672c\u67b6\u6784<\/strong><\/p>\n<\/p>\n
        Spark\u7684\u57fa\u672c\u67b6\u6784\u5305\u62ec\u9a71\u52a8\u7a0b\u5e8f\u3001\u96c6\u7fa4\u7ba1\u7406\u5668\u548c\u6267\u884c\u5668\u3002\u9a71\u52a8\u7a0b\u5e8f\u662f\u7528\u6237\u63d0\u4ea4Spark\u5e94\u7528\u7a0b\u5e8f\u7684\u5165\u53e3\uff0c\u96c6\u7fa4\u7ba1\u7406\u5668\u8d1f\u8d23\u7ba1\u7406\u96c6\u7fa4\u8d44\u6e90\uff0c\u800c\u6267\u884c\u5668\u5219\u5728\u96c6\u7fa4\u8282\u70b9\u4e0a\u6267\u884c\u4efb\u52a1\u3002<\/p>\n<\/p>\n
        Spark\u652f\u6301\u591a\u79cd\u6570\u636e\u6e90\u548c\u683c\u5f0f\uff0c\u5982HDFS\u3001Hive\u3001Cassandra\u7b49\uff0c\u540c\u65f6\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684API\uff0c\u652f\u6301SQL\u3001\u6d41\u5904\u7406\u3001\u673a\u5668\u5b66\u4e60<\/a>\u7b49\u529f\u80fd\u3002<\/p>\n<\/p>\n<\/li>\n
      2. \n
        PySpark\u7684\u5e94\u7528\u5b9e\u4f8b<\/strong><\/p>\n<\/p>\n
        \u4f7f\u7528PySpark\u8fdb\u884c\u6570\u636e\u5904\u7406\u901a\u5e38\u4ece\u521b\u5efaSpark\u4f1a\u8bdd\u5f00\u59cb\uff0c\u7136\u540e\u52a0\u8f7d\u6570\u636e\u5e76\u8fdb\u884c\u5904\u7406\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2aJSON\u6587\u4ef6\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u8bfb\u53d6\u5e76\u5206\u6790\u6570\u636e\uff1a<\/p>\n<\/p>\n
        from pyspark.sql import SparkSession<\/p>\n
        \u521b\u5efaSpark\u4f1a\u8bdd<\/strong><\/h2>\n
        spark = SparkSession.builder.appName('example').getOrCreate()<\/p>\n
        \u8bfb\u53d6JSON\u6587\u4ef6<\/strong><\/h2>\n
        df = spark.read.json('users.json')<\/p>\n
        \u8ba1\u7b97\u6bcf\u4e2a\u57ce\u5e02\u7684\u5e73\u5747\u5e74\u9f84<\/strong><\/h2>\n
        df.groupBy('city').avg('age').show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cSparkSession<\/code>\u7528\u4e8e\u521b\u5efa\u548c\u7ba1\u7406Spark\u5e94\u7528\u7a0b\u5e8f\u7684\u6267\u884c\u73af\u5883\uff0cread.json()<\/code>\u65b9\u6cd5\u5219\u7528\u4e8e\u8bfb\u53d6JSON\u683c\u5f0f\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
        \u56db\u3001PYTHON\u4e0eHADOOP\u7684\u7ed3\u5408<\/p>\n<\/p>\n
        Hadoop\u662f\u53e6\u4e00\u4e2a\u5e38\u7528\u7684\u5927\u6570\u636e\u5904\u7406\u5e73\u53f0\uff0c\u5b83\u4ee5\u5206\u5e03\u5f0f\u5b58\u50a8\u548c\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6\u800c\u95fb\u540d\u3002Python\u53ef\u4ee5\u4e0eHadoop\u7ed3\u5408\uff0c\u901a\u8fc7Pydoop\u3001hdfs3\u7b49\u5e93\u4e0eHadoop\u751f\u6001\u7cfb\u7edf\u8fdb\u884c\u4ea4\u4e92\u3002<\/p>\n<\/p>\n
          \n
        1. \n
          Hadoop\u7684\u57fa\u672c\u6982\u5ff5<\/strong><\/p>\n<\/p>\n
          Hadoop\u7684\u6838\u5fc3\u7ec4\u4ef6\u5305\u62ecHDFS\uff08Hadoop\u5206\u5e03\u5f0f\u6587\u4ef6\u7cfb\u7edf\uff09\u548cMapReduce\u3002HDFS\u7528\u4e8e\u5b58\u50a8\u6570\u636e\uff0c\u800cMapReduce\u7528\u4e8e\u5904\u7406\u6570\u636e\u3002Hadoop\u8fd8\u63d0\u4f9b\u4e86YARN\u4f5c\u4e3a\u8d44\u6e90\u7ba1\u7406<\/a>\u548c\u4efb\u52a1\u8c03\u5ea6\u7684\u6846\u67b6\u3002<\/p>\n<\/p>\n<\/li>\n
        2. \n
          Python\u4e0eHadoop\u7684\u4ea4\u4e92<\/strong><\/p>\n<\/p>\n
          Python\u53ef\u4ee5\u901a\u8fc7Pydoop\u4e0eHDFS\u8fdb\u884c\u4ea4\u4e92\uff0cPydoop\u63d0\u4f9b\u4e86\u5bf9HDFS\u7684\u8bbf\u95ee\u63a5\u53e3\uff0c\u53ef\u4ee5\u8bfb\u53d6\u548c\u5199\u5165HDFS\u6587\u4ef6\u7cfb\u7edf\u3002\u4f8b\u5982\uff0c\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528Pydoop\u8bfb\u53d6HDFS\u4e0a\u7684\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
          import pydoop.hdfs as hdfs<\/p>\n
          \u8bfb\u53d6HDFS\u4e0a\u7684\u6587\u4ef6<\/strong><\/h2>\n
          with hdfs.open('\/path\/to\/hdfs\/file.txt') as f:<\/p>\n
              content = f.read()<\/p>\n
              print(content)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4f7f\u7528hdfs3\u5e93\u4e5f\u53ef\u4ee5\u5b9e\u73b0\u7c7b\u4f3c\u7684\u529f\u80fd\uff0c\u5b83\u652f\u6301\u4e0eHDFS3\u534f\u8bae\u8fdb\u884c\u4ea4\u4e92\uff0c\u9002\u7528\u4e8e\u9700\u8981\u9ad8\u6027\u80fd\u6570\u636e\u4f20\u8f93\u7684\u573a\u666f\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
          \u4e94\u3001PYTHON\u4e0e\u5927\u6570\u636e\u673a\u5668\u5b66\u4e60<\/p>\n<\/p>\n
          \u5728\u5927\u6570\u636e\u73af\u5883\u4e0b\uff0c\u673a\u5668\u5b66\u4e60\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u5e94\u7528\u573a\u666f\u3002Python\u63d0\u4f9b\u4e86\u8bf8\u591a\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u5982Scikit-learn\u3001TensorFlow\u3001Keras\u7b49\uff0c\u53ef\u4ee5\u7528\u4e8e\u6784\u5efa\u548c\u8bad\u7ec3\u673a\u5668\u5b66\u4e60\u6a21\u578b\u3002<\/p>\n<\/p>\n
            \n
          1. \n
            Scikit-learn\u7684\u5e94\u7528<\/strong><\/p>\n<\/p>\n
            Scikit-learn\u662f\u4e00\u4e2a\u7b80\u5355\u6613\u7528\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u9002\u5408\u4e8e\u4e2d\u5c0f\u89c4\u6a21\u7684\u6570\u636e\u96c6\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u548c\u5de5\u5177\uff0c\u652f\u6301\u5206\u7c7b\u3001\u56de\u5f52\u3001\u805a\u7c7b\u3001\u964d\u7ef4\u7b49\u591a\u79cd\u4efb\u52a1\u3002<\/p>\n<\/p>\n
            \u6bd4\u5982\uff0c\u4f7f\u7528Scikit-learn\u6784\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/p>\n<\/p>\n
            from sklearn.linear_model import LinearRegression<\/p>\n
            from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
            import pandas as pd<\/p>\n
            \u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
            df = pd.read_csv('data.csv')<\/p>\n
            X = df[['feature1', 'feature2']]<\/p>\n
            y = df['target']<\/p>\n
            \u5206\u5272\u6570\u636e\u96c6<\/strong><\/h2>\n
            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)<\/p>\n
            \u521b\u5efa\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/strong><\/h2>\n
            model = LinearRegression()<\/p>\n
            model.fit(X_train, y_train)<\/p>\n
            \u9884\u6d4b<\/strong><\/h2>\n
            predictions = model.predict(X_test)<\/p>\n
            <\/code><\/pre>\n<\/p>\n<\/li>\n
          2. \n
            \u5206\u5e03\u5f0f\u673a\u5668\u5b66\u4e60<\/strong><\/p>\n<\/p>\n
            \u5bf9\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u4f7f\u7528\u5206\u5e03\u5f0f\u673a\u5668\u5b66\u4e60\u6846\u67b6\uff0c\u5982Spark MLlib\u3001TensorFlowOnSpark\u7b49\u3002Spark MLlib\u662fSpark\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u9002\u5408\u5904\u7406\u5206\u5e03\u5f0f\u6570\u636e\u96c6\uff0c\u652f\u6301\u591a\u79cd\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u548c\u5de5\u5177\u3002<\/p>\n<\/p>\n
            \u4f7f\u7528Spark MLlib\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u7684\u8fc7\u7a0b\u4e0eScikit-learn\u7c7b\u4f3c\uff0c\u4e5f\u9700\u8981\u6570\u636e\u52a0\u8f7d\u3001\u7279\u5f81\u63d0\u53d6\u3001\u6a21\u578b\u8bad\u7ec3\u548c\u8bc4\u4f30\u7b49\u6b65\u9aa4\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
            \u516d\u3001PYTHON\u5728\u5927\u6570\u636e\u53ef\u89c6\u5316\u4e2d\u7684\u5e94\u7528<\/p>\n<\/p>\n
            \u6570\u636e\u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u76f4\u89c2\u5730\u7406\u89e3\u6570\u636e\u3002Python\u63d0\u4f9b\u4e86\u591a\u79cd\u53ef\u89c6\u5316\u5e93\uff0c\u5982Matplotlib\u3001Seaborn\u3001Plotly\u7b49\uff0c\u9002\u5408\u4e8e\u521b\u5efa\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u548c\u56fe\u5f62\u3002<\/p>\n<\/p>\n
              \n
            1. \n
              Matplotlib\u548cSeaborn<\/strong><\/p>\n<\/p>\n
              Matplotlib\u662fPython\u4e2d\u6700\u57fa\u7840\u7684\u53ef\u89c6\u5316\u5e93\uff0c\u9002\u5408\u4e8e\u521b\u5efa\u9759\u6001\u56fe\u8868\u3002Seaborn\u662f\u57fa\u4e8eMatplotlib\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u7684API\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002<\/p>\n<\/p>\n
              \u6bd4\u5982\uff0c\u4f7f\u7528Seaborn\u7ed8\u5236\u4e00\u4e2a\u67f1\u72b6\u56fe\uff1a<\/p>\n<\/p>\n
              import seaborn as sns<\/p>\n
              import matplotlib.pyplot as plt<\/p>\n
              \u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
              df = pd.read_csv('data.csv')<\/p>\n
              \u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\n
              sns.barplot(x='category', y='value', data=df)<\/p>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n<\/li>\n
            2. \n
              \u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316<\/strong><\/p>\n<\/p>\n
              \u5bf9\u4e8e\u9700\u8981\u4e0e\u7528\u6237\u4ea4\u4e92\u7684\u53ef\u89c6\u5316\u4efb\u52a1\uff0c\u53ef\u4ee5\u4f7f\u7528Plotly\u3001Bokeh\u7b49\u5e93\u3002Plotly\u652f\u6301\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\uff0c\u5e76\u4e14\u53ef\u4ee5\u5728Web\u5e94\u7528\u7a0b\u5e8f\u4e2d\u5c55\u793a\u3002<\/p>\n<\/p>\n
              \u4f7f\u7528Plotly\u521b\u5efa\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u6563\u70b9\u56fe\uff1a<\/p>\n<\/p>\n
              import plotly.express as px<\/p>\n
              \u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
              df = pd.read_csv('data.csv')<\/p>\n
              \u521b\u5efa\u6563\u70b9\u56fe<\/strong><\/h2>\n
              fig = px.scatter(df, x='feature1', y='feature2', color='category')<\/p>\n
              fig.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
              \u901a\u8fc7\u8fd9\u4e9b\u5de5\u5177\u548c\u5e93\uff0cPython\u80fd\u591f\u6709\u6548\u5730\u5904\u7406\u548c\u5206\u6790\u5927\u89c4\u6a21\u6570\u636e\u96c6\uff0c\u5b9e\u73b0\u4ece\u6570\u636e\u9884\u5904\u7406\u3001\u5206\u6790\u3001\u5efa\u6a21\u5230\u53ef\u89c6\u5316\u7684\u5168\u6d41\u7a0b\u5927\u6570\u636e\u89e3\u51b3\u65b9\u6848\u3002\u9009\u62e9\u5408\u9002\u7684\u5de5\u5177\u548c\u65b9\u6cd5\uff0cPython\u53ef\u4ee5\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u5e94\u5bf9\u5927\u6570\u636e\u5e26\u6765\u7684\u6311\u6218\u548c\u673a\u9047\u3002<\/p>\n<\/p>\n
              \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
               \u5982\u4f55\u4f7f\u7528Python\u5904\u7406\u5927\u6570\u636e\uff1f<\/strong>
              Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5e93\u548c\u6846\u67b6\u6765\u5904\u7406\u5927\u6570\u636e\uff0c\u4f8b\u5982Pandas\u3001Dask\u548cPySpark\u3002Pandas\u9002\u7528\u4e8e\u5c0f\u578b\u5230\u4e2d\u578b\u6570\u636e\u96c6\uff0c\u800cDask\u548cPySpark\u5219\u53ef\u4ee5\u5904\u7406\u66f4\u5927\u89c4\u6a21\u7684\u6570\u636e\u3002\u901a\u8fc7\u8fd9\u4e9b\u5de5\u5177\uff0c\u7528\u6237\u53ef\u4ee5\u6267\u884c\u6570\u636e\u6e05\u6d17\u3001\u5206\u6790\u548c\u53ef\u89c6\u5316\u7b49\u64cd\u4f5c\uff0c\u4ece\u800c\u9ad8\u6548\u5730\u7ba1\u7406\u548c\u5229\u7528\u5927\u6570\u636e\u3002<\/p>\n
              Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u9002\u5408\u5927\u6570\u636e\u5206\u6790\uff1f<\/strong>
              \u5728Python\u4e2d\uff0c\u6709\u591a\u4e2a\u5e93\u4e13\u95e8\u7528\u4e8e\u5927\u6570\u636e\u5206\u6790\u3002\u9664\u4e86Pandas\u548cDask\uff0c\u8fd8\u6709NumPy\u3001Vaex\u548cModin\u7b49\u3002NumPy\u9002\u5408\u6570\u503c\u8ba1\u7b97\uff0cVaex\u80fd\u591f\u5904\u7406\u8d85\u5927\u6570\u636e\u96c6\u800c\u4e0d\u6d88\u8017\u8fc7\u591a\u5185\u5b58\uff0cModin\u5219\u53ef\u4ee5\u52a0\u901fPandas\u64cd\u4f5c\u3002\u9009\u62e9\u5408\u9002\u7684\u5e93\u53ef\u4ee5\u63d0\u9ad8\u6570\u636e\u5904\u7406\u6548\u7387\uff0c\u964d\u4f4e\u5185\u5b58\u5360\u7528\u3002<\/p>\n
              \u5982\u4f55\u5728Python\u4e2d\u4f18\u5316\u5927\u6570\u636e\u5904\u7406\u7684\u6027\u80fd\uff1f<\/strong>
              \u4f18\u5316\u5927\u6570\u636e\u5904\u7406\u6027\u80fd\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u3002\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u751f\u6210\u5668\u6765\u8282\u7701\u5185\u5b58\uff0c\u5229\u7528\u5e76\u884c\u8ba1\u7b97\u52a0\u901f\u5904\u7406\uff0c\u6216\u662f\u5c06\u6570\u636e\u5206\u5757\u8bfb\u53d6\u3002\u6b64\u5916\uff0c\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u683c\u5f0f\uff08\u5982Parquet\u6216HDF5\uff09\u4e5f\u80fd\u663e\u8457\u63d0\u5347\u8bfb\u5199\u901f\u5ea6\u3002\u5408\u7406\u7684\u4ee3\u7801\u7ed3\u6784\u548c\u7b97\u6cd5\u9009\u62e9\u540c\u6837\u91cd\u8981\uff0c\u5b83\u4eec\u76f4\u63a5\u5f71\u54cd\u5904\u7406\u6548\u7387\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5b9e\u73b0\u5927\u6570\u636e\u7684\u4e3b\u8981\u65b9\u6cd5\u6709\uff1a\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u3001\u901a\u8fc7Dask\u8fdb\u884c\u5e76\u884c\u8ba1\u7b97\u3001\u5229\u7528PySpark […]","protected":false},"author":3,"featured_media":996687,"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\/996679"}],"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=996679"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/996679\/revisions"}],"predecessor-version":[{"id":996692,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/996679\/revisions\/996692"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/996687"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=996679"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=996679"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=996679"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}