{"id":827,"date":"2025-01-11T10:44:18","date_gmt":"2025-01-11T02:44:18","guid":{"rendered":"https:\/\/www.laixuexila.com\/?p=827"},"modified":"2025-01-11T11:15:28","modified_gmt":"2025-01-11T03:15:28","slug":"%e6%95%b0%e6%8d%ae%e7%a7%91%e5%ad%a6%e5%ae%b6%e5%92%8c%e5%88%86%e6%9e%90%e5%b8%88%e7%9a%84%e9%a6%96%e9%80%89%e7%bc%96%e7%a8%8b%e8%af%ad%e8%a8%80%ef%bc%9apython","status":"publish","type":"post","link":"https:\/\/www.laixuexila.com\/index.php\/2025\/01\/11\/%e6%95%b0%e6%8d%ae%e7%a7%91%e5%ad%a6%e5%ae%b6%e5%92%8c%e5%88%86%e6%9e%90%e5%b8%88%e7%9a%84%e9%a6%96%e9%80%89%e7%bc%96%e7%a8%8b%e8%af%ad%e8%a8%80%ef%bc%9apython\/","title":{"rendered":"\u6570\u636e\u79d1\u5b66\u5bb6\u4e0e\u5206\u6790\u5e08\u7684\u9996\u9009\u7f16\u7a0b\u8bed\u8a00\uff1aPython\u5de5\u5177\u3001\u6280\u672f\u4e0e\u5b9e\u8df5"},"content":{"rendered":"\n
\n

Python \u5728 \u6570\u636e\u79d1\u5b66<\/strong> \u548c \u5927\u6570\u636e\u5206\u6790<\/strong> \u9886\u57df\u7684\u5e94\u7528\u975e\u5e38\u5e7f\u6cdb\u3002\u7531\u4e8e Python \u8bed\u8a00\u7b80\u6d01\u6613\u61c2\u4e14\u62e5\u6709\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5e93\uff0c\u5b83\u6210\u4e3a\u4e86\u6570\u636e\u79d1\u5b66\u5bb6\u548c\u5206\u6790\u5e08\u7684\u9996\u9009\u7f16\u7a0b\u8bed\u8a00\u4e4b\u4e00\u3002\u4ece\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u53ef\u89c6\u5316\u5230\u9ad8\u7ea7\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\uff0cPython \u90fd\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5de5\u5177\u548c\u751f\u6001\u7cfb\u7edf\uff0c\u652f\u6301\u4ece\u5c0f\u89c4\u6a21\u6570\u636e\u5230\u5927\u89c4\u6a21\u6570\u636e\u7684\u5904\u7406\u548c\u5206\u6790\u3002<\/p>\n<\/blockquote>\n\n\n\n

\n\n\n\n

1. Python \u5728\u6570\u636e\u79d1\u5b66\u4e2d\u7684\u5e94\u7528<\/strong><\/h3>\n\n\n\n

\u6570\u636e\u79d1\u5b66\u662f\u5229\u7528\u6570\u636e\u5206\u6790\u3001\u7edf\u8ba1\u5b66\u548c\u8ba1\u7b97\u673a\u79d1\u5b66\u7684\u65b9\u6cd5\uff0c\u4ece\u6570\u636e\u4e2d\u63d0\u53d6\u6709\u4ef7\u503c\u7684\u77e5\u8bc6\u548c\u4fe1\u606f\u3002Python \u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5e93\u6765\u652f\u6301\u8fd9\u4e9b\u4efb\u52a1\uff0c\u6db5\u76d6\u4e86\u6570\u636e\u9884\u5904\u7406\u3001\u53ef\u89c6\u5316\u3001\u7edf\u8ba1\u5206\u6790\u3001\u673a\u5668\u5b66\u4e60\u7b49\u5404\u4e2a\u65b9\u9762\u3002<\/p>\n\n\n\n

1.1 \u6570\u636e\u9884\u5904\u7406<\/strong><\/h4>\n\n\n\n

\u6570\u636e\u9884\u5904\u7406\u662f\u6570\u636e\u79d1\u5b66\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u73af\u8282\uff0c\u5b83\u901a\u5e38\u5305\u62ec\u6570\u636e\u6e05\u6d17\u3001\u7f3a\u5931\u503c\u5904\u7406\u3001\u6570\u636e\u8f6c\u6362\u3001\u7279\u5f81\u5de5\u7a0b\u7b49\u3002<\/p>\n\n\n\n

\u5e38\u7528\u5de5\u5177<\/strong>\uff1a<\/p>\n\n\n\n

    \n
  • Pandas<\/strong>\uff1a\u6700\u5e38\u7528\u7684 Python \u6570\u636e\u5904\u7406\u5e93\uff0c\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u636e\u64cd\u4f5c\u548c\u6e05\u6d17\u529f\u80fd\uff0c\u652f\u6301\u8868\u683c\u6570\u636e\uff08\u5982 CSV\u3001Excel\uff09\u548c\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u5904\u7406\u3002<\/li>\n\n\n\n
  • NumPy<\/strong>\uff1a\u5904\u7406\u6570\u503c\u6570\u636e\uff0c\u7279\u522b\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u7684\u77e9\u9635\u8fd0\u7b97\u548c\u6570\u503c\u8ba1\u7b97\u3002<\/li>\n<\/ul>\n\n\n\n

    \u793a\u4f8b\uff1a\u5904\u7406\u7f3a\u5931\u6570\u636e<\/strong><\/p>\n\n\n\n

    import pandas as pd\n\n# \u8bfb\u53d6\u6570\u636e\ndf = pd.read_csv('data.csv')\n\n# \u586b\u5145\u7f3a\u5931\u503c\ndf.fillna(df.mean(), inplace=True)\n\n# \u5904\u7406\u91cd\u590d\u503c\ndf.drop_duplicates(inplace=True)\n\n# \u6570\u636e\u7c7b\u578b\u8f6c\u6362\ndf['column_name'] = df['column_name'].astype('int')<\/code><\/pre>\n\n\n\n
    1.2 \u6570\u636e\u53ef\u89c6\u5316<\/strong><\/h4>\n\n\n\n
    \u6570\u636e\u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u7684\u4e00\u4e2a\u91cd\u8981\u90e8\u5206\uff0c\u5b83\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u66f4\u76f4\u89c2\u5730\u7406\u89e3\u6570\u636e\u4e2d\u7684\u6a21\u5f0f\u548c\u8d8b\u52bf\u3002Python \u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u53ef\u89c6\u5316\u5e93\u6765\u5c55\u793a\u6570\u636e\u5206\u6790\u7ed3\u679c\u3002<\/p>\n\n\n\n
    \u5e38\u7528\u5de5\u5177<\/strong>\uff1a<\/p>\n\n\n\n
      \n
    • Matplotlib<\/strong>\uff1a\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u7ed8\u5236\u5404\u79cd\u9759\u6001\u3001\u52a8\u6001\u56fe\u5f62\u3002<\/li>\n\n\n\n
    • Seaborn<\/strong>\uff1a\u57fa\u4e8e Matplotlib \u7684\u7edf\u8ba1\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u7684\u63a5\u53e3\u548c\u66f4\u7cbe\u7f8e\u7684\u56fe\u5f62\u6837\u5f0f\u3002<\/li>\n\n\n\n
    • Plotly<\/strong>\uff1a\u7528\u4e8e\u751f\u6210\u4ea4\u4e92\u5f0f\u56fe\u5f62\uff0c\u652f\u6301\u66f4\u590d\u6742\u7684\u53ef\u89c6\u5316\u9700\u6c42\u3002<\/li>\n\n\n\n
    • Bokeh<\/strong>\uff1a\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u548c\u5b9e\u65f6\u6570\u636e\u53ef\u89c6\u5316\u7684\u5e93\uff0c\u9002\u7528\u4e8e\u5927\u6570\u636e\u91cf\u7684\u53ef\u89c6\u5316\u3002<\/li>\n<\/ul>\n\n\n\n
      \u793a\u4f8b\uff1a\u4f7f\u7528 Seaborn \u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/strong><\/p>\n\n\n\n
      import seaborn as sns\nimport matplotlib.pyplot as plt\n\n# \u52a0\u8f7d\u6570\u636e\u96c6\ndf = sns.load_dataset('tips')\n\n# \u7ed8\u5236\u6563\u70b9\u56fe\nsns.scatterplot(x='total_bill', y='tip', data=df)\n\n# \u7ed8\u5236\u7bb1\u7ebf\u56fe\nsns.boxplot(x='day', y='total_bill', data=df)\n\nplt.show()<\/code><\/pre>\n\n\n\n
      1.3 \u7edf\u8ba1\u5206\u6790<\/strong><\/h4>\n\n\n\n
      \u6570\u636e\u79d1\u5b66\u901a\u5e38\u9700\u8981\u8fdb\u884c\u7edf\u8ba1\u63a8\u65ad\uff0cPython \u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7edf\u8ba1\u5206\u6790\u5e93\u6765\u5e2e\u52a9\u8fdb\u884c\u63cf\u8ff0\u6027\u7edf\u8ba1\u3001\u5047\u8bbe\u68c0\u9a8c\u3001\u56de\u5f52\u5206\u6790\u7b49\u3002<\/p>\n\n\n\n
      \u5e38\u7528\u5de5\u5177<\/strong>\uff1a<\/p>\n\n\n\n
        \n
      • SciPy<\/strong>\uff1a\u4e00\u4e2a\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5305\u542b\u4e86\u5927\u91cf\u7684\u6570\u5b66\u51fd\u6570\u3001\u4f18\u5316\u5de5\u5177\u548c\u7edf\u8ba1\u6d4b\u8bd5\u65b9\u6cd5\u3002<\/li>\n\n\n\n
      • Statsmodels<\/strong>\uff1a\u7528\u4e8e\u4f30\u8ba1\u548c\u7edf\u8ba1\u5206\u6790\u7684\u5e93\uff0c\u7279\u522b\u9002\u7528\u4e8e\u56de\u5f52\u6a21\u578b\u3001\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u7b49\u3002<\/li>\n<\/ul>\n\n\n\n
        \u793a\u4f8b\uff1a\u4f7f\u7528 Statsmodels \u8fdb\u884c\u56de\u5f52\u5206\u6790<\/strong><\/p>\n\n\n\n
        import statsmodels.api as sm\nimport pandas as pd\n\n# \u8bfb\u53d6\u6570\u636e\ndf = pd.read_csv('data.csv')\n\n# \u5b9a\u4e49\u81ea\u53d8\u91cf\uff08X\uff09\u548c\u56e0\u53d8\u91cf\uff08Y\uff09\nX = df[['feature1', 'feature2']]\nY = df['target']\n\n# \u6dfb\u52a0\u5e38\u6570\u9879\nX = sm.add_constant(X)\n\n# \u8fdb\u884c\u7ebf\u6027\u56de\u5f52\nmodel = sm.OLS(Y, X).fit()\n\n# \u67e5\u770b\u56de\u5f52\u7ed3\u679c\nprint(model.summary())<\/code><\/pre>\n\n\n\n
        \n\n\n\n
        2. Python \u5728\u5927\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e94\u7528<\/strong><\/h3>\n\n\n\n
        \u5927\u6570\u636e\u5206\u6790\u662f\u6307\u5904\u7406\u548c\u5206\u6790\u8d85\u5927\u89c4\u6a21\u7684\u6570\u636e\u96c6\uff0c\u901a\u5e38\u8981\u6c42\u4f7f\u7528\u5206\u5e03\u5f0f\u8ba1\u7b97\u548c\u9ad8\u6548\u7684\u6570\u636e\u5b58\u50a8\u6280\u672f\u3002Python \u867d\u7136\u662f\u5355\u673a\u7a0b\u5e8f\u8bed\u8a00\uff0c\u4f46\u5b83\u901a\u8fc7\u96c6\u6210\u5176\u4ed6\u5206\u5e03\u5f0f\u8ba1\u7b97\u5e73\u53f0\u548c\u5927\u6570\u636e\u5de5\u5177\uff0c\u80fd\u591f\u5728\u5927\u6570\u636e\u73af\u5883\u4e2d\u8fdb\u884c\u6709\u6548\u7684\u5206\u6790\u3002<\/p>\n\n\n\n
        2.1 \u96c6\u6210\u5927\u6570\u636e\u5de5\u5177\u4e0e\u5e73\u53f0<\/strong><\/h4>\n\n\n\n
        Python \u53ef\u4ee5\u4e0e\u5927\u6570\u636e\u5904\u7406\u6846\u67b6\u5982 Apache Spark<\/strong>\u3001Dask<\/strong>\u3001Hadoop<\/strong> \u7b49\u8fdb\u884c\u96c6\u6210\uff0c\u4f7f\u5f97\u5728\u5206\u5e03\u5f0f\u73af\u5883\u4e0b\u8fdb\u884c\u5927\u89c4\u6a21\u6570\u636e\u5206\u6790\u6210\u4e3a\u53ef\u80fd\u3002<\/p>\n\n\n\n
          \n
        • PySpark<\/strong>\uff1a\u662f Apache Spark \u7684 Python API\uff0c\u63d0\u4f9b\u4e86\u5bf9\u5927\u6570\u636e\u96c6\u7684\u5206\u5e03\u5f0f\u8ba1\u7b97\u652f\u6301\u3002<\/li>\n\n\n\n
        • Dask<\/strong>\uff1a\u4e00\u4e2a\u7075\u6d3b\u7684\u5e76\u884c\u8ba1\u7b97\u5e93\uff0c\u53ef\u4ee5\u5728\u591a\u6838\u673a\u5668\u548c\u96c6\u7fa4\u4e0a\u5e76\u884c\u8ba1\u7b97\uff0c\u652f\u6301\u5927\u89c4\u6a21\u6570\u636e\u96c6\u7684\u5904\u7406\u3002<\/li>\n\n\n\n
        • Hadoop<\/strong>\uff1a\u867d\u7136 Hadoop \u672c\u8eab\u4e3b\u8981\u4f7f\u7528 Java\uff0c\u4f46 Python \u53ef\u4ee5\u901a\u8fc7 PyArrow<\/strong> \u6216 HDFS<\/strong> \u5e93\u6765\u4e0e Hadoop \u751f\u6001\u7cfb\u7edf\u8fdb\u884c\u4ea4\u4e92\u3002<\/li>\n<\/ul>\n\n\n\n
          2.2 \u5927\u6570\u636e\u5904\u7406\u5de5\u5177<\/strong><\/h4>\n\n\n\n
          PySpark \u793a\u4f8b\uff1a\u4f7f\u7528 Spark \u8fdb\u884c\u5206\u5e03\u5f0f\u6570\u636e\u5904\u7406<\/strong><\/p>\n\n\n\n
          from pyspark.sql import SparkSession\n\n# \u521b\u5efa Spark \u4f1a\u8bdd\nspark = SparkSession.builder.appName('BigDataAnalysis').getOrCreate()\n\n# \u8bfb\u53d6\u6570\u636e\ndf = spark.read.csv('large_data.csv', header=True, inferSchema=True)\n\n# \u6570\u636e\u64cd\u4f5c\uff1a\u7b5b\u9009\ndf_filtered = df.filter(df['age'] > 30)\n\n# \u6570\u636e\u805a\u5408\uff1a\u8ba1\u7b97\u5e73\u5747\u503c\ndf_agg = df.groupBy('city').avg('salary')\n\n# \u663e\u793a\u7ed3\u679c\ndf_agg.show()<\/code><\/pre>\n\n\n\n
          2.3 \u5927\u6570\u636e\u5b58\u50a8\u4e0e\u67e5\u8be2<\/strong><\/h4>\n\n\n\n
          Python \u8fd8\u53ef\u4ee5\u4e0e\u5927\u6570\u636e\u5b58\u50a8\u7cfb\u7edf\u8fdb\u884c\u4ea4\u4e92\uff0c\u5e38\u89c1\u7684\u5b58\u50a8\u7cfb\u7edf\u5305\u62ec Hadoop HDFS<\/strong>\u3001Amazon S3<\/strong>\u3001Apache Parquet<\/strong> \u7b49\u3002\u901a\u8fc7 Python \u5e93\uff0c\u53ef\u4ee5\u5c06\u6570\u636e\u4ece\u5206\u5e03\u5f0f\u5b58\u50a8\u8bfb\u53d6\u5230\u5185\u5b58\u8fdb\u884c\u5206\u6790\u3002<\/p>\n\n\n\n
            \n
          • PyArrow<\/strong>\uff1a\u7528\u4e8e\u4e0e\u5927\u6570\u636e\u6587\u4ef6\u683c\u5f0f\uff08\u5982 Parquet\uff09\u8fdb\u884c\u4ea4\u4e92\u3002<\/li>\n\n\n\n
          • Boto3<\/strong>\uff1a\u4e0e Amazon S3 \u8fdb\u884c\u4ea4\u4e92\uff0c\u8bfb\u53d6\u548c\u5199\u5165\u5927\u6570\u636e\u6587\u4ef6\u3002<\/li>\n<\/ul>\n\n\n\n
            \u793a\u4f8b\uff1a\u4f7f\u7528 Boto3 \u4ece Amazon S3 \u8bfb\u53d6\u6570\u636e<\/strong><\/p>\n\n\n\n
            import boto3\nimport pandas as pd\n\n# \u521b\u5efa S3 \u5ba2\u6237\u7aef\ns3 = boto3.client('s3')\n\n# \u4e0b\u8f7d\u6570\u636e\ns3.download_file('my-bucket', 'path\/to\/data.csv', 'data.csv')\n\n# \u4f7f\u7528 Pandas \u8bfb\u53d6\u6570\u636e\ndf = pd.read_csv('data.csv')\nprint(df.head())<\/code><\/pre>\n\n\n\n
            2.4 \u9ad8\u6548\u7684\u8ba1\u7b97\u548c\u5185\u5b58\u7ba1\u7406<\/strong><\/h4>\n\n\n\n
            \u5904\u7406\u5927\u6570\u636e\u65f6\uff0c\u5185\u5b58\u548c\u8ba1\u7b97\u6548\u7387\u81f3\u5173\u91cd\u8981\u3002Python \u63d0\u4f9b\u4e86\u4e00\u4e9b\u4f18\u5316\u5de5\u5177\u6765\u63d0\u5347\u5927\u6570\u636e\u5206\u6790\u7684\u6027\u80fd\uff1a<\/p>\n\n\n\n
              \n
            • Dask<\/strong>\uff1a\u652f\u6301\u5927\u89c4\u6a21\u5e76\u884c\u8ba1\u7b97\uff0c\u80fd\u591f\u5904\u7406\u8d85\u51fa\u5185\u5b58\u7684\u6570\u636e\u96c6\u3002<\/li>\n\n\n\n
            • Vaex<\/strong>\uff1a\u4e13\u95e8\u4e3a\u5185\u5b58\u4e2d\u7684\u5927\u6570\u636e\u96c6\u8bbe\u8ba1\uff0c\u652f\u6301\u5185\u5b58\u5916\u8ba1\u7b97\u548c\u66f4\u9ad8\u6548\u7684\u6570\u636e\u64cd\u4f5c\u3002<\/li>\n\n\n\n
            • Numba<\/strong>\uff1a\u4e00\u4e2a\u5373\u65f6\u7f16\u8bd1\u5668\uff0c\u53ef\u4ee5\u52a0\u901f Python \u4ee3\u7801\uff0c\u5c24\u5176\u5728\u6570\u503c\u8ba1\u7b97\u65b9\u9762\u3002<\/li>\n<\/ul>\n\n\n\n
              \u793a\u4f8b\uff1a\u4f7f\u7528 Dask \u8fdb\u884c\u5927\u6570\u636e\u5206\u6790<\/strong><\/p>\n\n\n\n
              import dask.dataframe as dd\n\n# \u8bfb\u53d6\u5927\u6570\u636e\u6587\u4ef6\ndf = dd.read_csv('large_data\/*.csv')\n\n# \u6570\u636e\u64cd\u4f5c\ndf_filtered = df[df['age'] > 30]\n\n# \u8ba1\u7b97\u5e73\u5747\u503c\nresult = df_filtered.groupby('city')['salary'].mean().compute()\nprint(result)<\/code><\/pre>\n\n\n\n
              \n\n\n\n
              3. Python \u5728\u6570\u636e\u79d1\u5b66\u548c\u5927\u6570\u636e\u5206\u6790\u4e2d\u7684\u4f18\u52bf\u4e0e\u6311\u6218<\/strong><\/h3>\n\n\n\n
              3.1 \u4f18\u52bf<\/strong><\/h4>\n\n\n\n
                \n
              • \u6613\u7528\u6027<\/strong>\uff1aPython \u7684\u8bed\u6cd5\u7b80\u6d01\u76f4\u89c2\uff0c\u975e\u5e38\u9002\u5408\u5feb\u901f\u5f00\u53d1\u548c\u5206\u6790\u3002<\/li>\n\n\n\n
              • \u4e30\u5bcc\u7684\u5e93\u548c\u5de5\u5177<\/strong>\uff1aPython \u62e5\u6709\u5f3a\u5927\u7684\u751f\u6001\u7cfb\u7edf\uff0c\u6db5\u76d6\u4e86\u4ece\u6570\u636e\u9884\u5904\u7406\u3001\u673a\u5668\u5b66\u4e60\u5230\u5927\u6570\u636e\u5206\u6790\u7684\u6240\u6709\u5de5\u5177\u3002<\/li>\n\n\n\n
              • \u793e\u533a\u652f\u6301<\/strong>\uff1aPython \u62e5\u6709\u4e00\u4e2a\u6d3b\u8dc3\u7684\u5f00\u53d1\u8005\u793e\u533a\uff0c\u8bb8\u591a\u5f00\u6e90\u5de5\u5177\u548c\u5e93\u90fd\u53ef\u4ee5\u901a\u8fc7 Python \u5feb\u901f\u5b9e\u73b0\u3002<\/li>\n<\/ul>\n\n\n\n
                3.2 \u6311\u6218<\/strong><\/h4>\n\n\n\n
                  \n
                • \u6027\u80fd\u95ee\u9898<\/strong>\uff1a\u5bf9\u4e8e\u8d85\u5927\u89c4\u6a21\u6570\u636e\u96c6\uff0cPython \u672c\u8eab\u7684\u5355\u673a\u5904\u7406\u80fd\u529b\u6709\u9650\uff0c\u5f80\u5f80\u9700\u8981\u7ed3\u5408\u5176\u4ed6\u5927\u6570\u636e\u5904\u7406\u5de5\u5177\u3002<\/li>\n\n\n\n
                • \u5e76\u884c\u8ba1\u7b97<\/strong>\uff1a\u867d\u7136 Python \u53ef\u4ee5\u4e0e\u5206\u5e03\u5f0f\u8ba1\u7b97\u6846\u67b6\uff08\u5982 Spark \u548c Dask\uff09\u7ed3\u5408\uff0c\u4f46\u7531\u4e8e \u5168\u5c40\u89e3\u91ca\u5668\u9501\uff08GIL\uff09<\/strong> \u7684\u5b58\u5728\uff0c\u5355\u673a\u7684\u5e76\u884c\u8ba1\u7b97\u80fd\u529b\u53d7\u5230\u4e00\u5b9a\u9650\u5236\u3002<\/li>\n<\/ul>\n\n\n\n
                  \n\n\n\n
                  \u603b\u7ed3<\/h3>\n\n\n\n
                  Python \u5728\u6570\u636e\u79d1\u5b66\u548c\u5927\u6570\u636e\u5206\u6790\u4e2d\u5177\u6709\u5f3a\u5927\u7684\u5e94\u7528\u6f5c\u529b\uff0c\u4ece\u6570\u636e\u9884\u5904\u7406\u3001\u53ef\u89c6\u5316\u3001\u7edf\u8ba1\u5206\u6790\u5230\u5927\u6570\u636e\u5904\u7406\u548c\u5206\u5e03\u5f0f\u8ba1\u7b97\uff0c\u5b83\u51e0\u4e4e\u6db5\u76d6\u4e86\u6240\u6709\u6570\u636e\u5206\u6790\u4efb\u52a1\u7684\u9700\u6c42\u3002\u901a\u8fc7\u96c6\u6210\u5404\u7c7b\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\uff08\u5982 Pandas\u3001NumPy\uff09\u3001\u673a\u5668\u5b66\u4e60\u5e93\uff08\u5982 Scikit-learn\u3001TensorFlow\uff09\u4ee5\u53ca\u5927\u6570\u636e\u5904\u7406\u6846\u67b6\uff08\u5982 PySpark\u3001Dask\uff09\uff0cPython \u80fd\u591f\u6709\u6548\u5730\u89e3\u51b3\u4e0d\u540c\u89c4\u6a21\u7684\u6570\u636e\u5206\u6790\u4efb\u52a1\u3002\u5bf9\u4e8e\u4ece\u4e8b\u6570\u636e\u79d1\u5b66\u548c\u5927\u6570\u636e\u5206\u6790\u7684\u5f00\u53d1\u8005\u6765\u8bf4\uff0cPython \u5df2\u7ecf\u6210\u4e3a\u4e0d\u53ef\u6216\u7f3a\u7684\u5de5\u5177\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
                  Python \u5728 \u6570\u636e\u79d1\u5b66 \u548c \u5927\u6570\u636e\u5206\u6790 \u9886\u57df\u7684\u5e94\u7528\u975e\u5e38\u5e7f\u6cdb\u3002\u7531\u4e8e Python \u8bed\u8a00\u7b80\u6d01\u6613\u61c2\u4e14\u62e5\u6709\u5f3a\u5927\u7684\u6570 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[54],"tags":[],"class_list":["post-827","post","type-post","status-publish","format-standard","hentry","category-python"],"_links":{"self":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts\/827","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/comments?post=827"}],"version-history":[{"count":2,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts\/827\/revisions"}],"predecessor-version":[{"id":840,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts\/827\/revisions\/840"}],"wp:attachment":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/media?parent=827"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/categories?post=827"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/tags?post=827"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}