{"id":47029,"date":"2023-04-05T08:01:19","date_gmt":"2023-06-25T13:07:17","guid":{"rendered":"https:\/\/www.silicloud.com\/zh\/blog\/%e7%94%a8%e4%ba%8e%e6%95%b0%e6%8d%ae%e7%a7%91%e5%ad%a6%e5%ae%b6%e5%92%8c%e6%95%b0%e6%8d%ae%e5%b7%a5%e7%a8%8b%e5%b8%88%e7%9a%84python%e5%ba%93%e5%88%97%e8%a1%a8%e3%80%82\/"},"modified":"2024-04-29T22:01:07","modified_gmt":"2024-04-29T14:01:07","slug":"%e7%94%a8%e4%ba%8e%e6%95%b0%e6%8d%ae%e7%a7%91%e5%ad%a6%e5%ae%b6%e5%92%8c%e6%95%b0%e6%8d%ae%e5%b7%a5%e7%a8%8b%e5%b8%88%e7%9a%84python%e5%ba%93%e5%88%97%e8%a1%a8%e3%80%82","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/zh\/blog\/%e7%94%a8%e4%ba%8e%e6%95%b0%e6%8d%ae%e7%a7%91%e5%ad%a6%e5%ae%b6%e5%92%8c%e6%95%b0%e6%8d%ae%e5%b7%a5%e7%a8%8b%e5%b8%88%e7%9a%84python%e5%ba%93%e5%88%97%e8%a1%a8%e3%80%82\/","title":{"rendered":"\u7528\u4e8e\u6570\u636e\u79d1\u5b66\u5bb6\u548c\u6570\u636e\u5de5\u7a0b\u5e08\u7684Python\u5e93\u5217\u8868"},"content":{"rendered":"

\u4ecb\u7ecd\u4e00\u4e9b\u7528\u4e8e\u6570\u636e\u5206\u6790\u3001\u6570\u636e\u5904\u7406\u548c\u673a\u5668\u5b66\u4e60\u7b49\u65b9\u9762\u975e\u5e38\u65b9\u4fbf\u7684Python\u5e93\u3002<\/p>\n

\u4e3a\u4ec0\u4e48\u9009\u62e9 Python\uff1f<\/h2>\n

\u5982\u679c\u8fdb\u884c\u7edf\u8ba1\u6216\u673a\u5668\u5b66\u4e60\uff0c\u4e5f\u53ef\u4ee5\u9009\u62e9R\u3002R\u662f\u4e00\u79cd\u4f18\u79c0\u7684\u8bed\u8a00\uff0c\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u5904\u7406\u3001\u6c47\u603b\u548c\u7edf\u8ba1\u5904\u7406\uff0c\u5e76\u4e14\u4ec5\u51ed\u8bed\u8a00\u6807\u51c6\u529f\u80fd\u5c31\u53ef\u4ee5\u5b8c\u6210\u5f88\u591a\u5de5\u4f5c\u3002\u5b83\u8fd8\u62e5\u6709\u4e30\u5bcc\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u662f\u4e00\u4e2a\u5f3a\u6709\u529b\u7684\u9009\u62e9\u3002\u76f8\u6bd4\u8f83R\uff0cPython\u5728\u5468\u8fb9\u751f\u6001\u7cfb\u7edf\u7684\u5b8c\u5584\u4e0a\u66f4\u52a0\u4f18\u79c0\u3002Python\u7684\u751f\u6001\u7cfb\u7edf\u4e0d\u4ec5\u9650\u4e8e\u6570\u636e\u79d1\u5b66\u9886\u57df\uff0c\u8fd8\u53ef\u5c06\u7ecf\u8fc7NumPy\u548cPandas\u5904\u7406\u7684\u6570\u636e\u7528\u4e8e\u57fa\u4e8eDjango\u7684\u5b8c\u6574Web\u5e94\u7528\u7a0b\u5e8f\u4e2d\u3002<\/p>\n

\u5b89\u88c5\u5e93\u96c6\u5408<\/h2>\n

\u5927\u90e8\u5206\u5217\u4e3e\u7684\u5e93\u90fd\u53ef\u4ee5\u901a\u8fc7Anaconda\u4e00\u952e\u5b89\u88c5\u3002<\/p>\n

\u6570\u636e\u5904\u7406<\/h2>\n

NumPy -> \u6570\u503cPython<\/h3>\n

NumPy\u662f\u4e00\u4e2a\u7528\u4e8e\u9ad8\u6548\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u7684\u5e93\u3002\u8fd9\u91cc\u4ee5\u4e00\u7ef4\u6570\u7ec4\u4e3a\u4f8b\uff0c\u4f46\u4e5f\u652f\u6301\u591a\u7ef4\u6570\u7ec4\u3002\u5b83\u53ef\u4ee5\u5feb\u901f\u8fdb\u884c\u5411\u91cf\u548c\u77e9\u9635\u8ba1\u7b97\u3002<\/p>\n

In<\/span> [<\/span>1<\/span>]:<\/span> import<\/span> numpy<\/span> as<\/span> np<\/span> # NumPy \u306e\u30a4\u30f3\u30dd\u30fc\u30c8\r\n<\/span>\r\nIn<\/span> [<\/span>2<\/span>]:<\/span> arr<\/span> =<\/span> np<\/span>.<\/span>asarray<\/span>([<\/span>n<\/span> for<\/span> n<\/span> in<\/span> range<\/span>(<\/span>10<\/span>)])<\/span> # \u30d9\u30af\u30c8\u30eb\u306e\u4f5c\u6210\r\n<\/span>\r\nIn<\/span> [<\/span>3<\/span>]:<\/span> arr<\/span> # \u51fa\u529b\r\n<\/span>Out<\/span>[<\/span>3<\/span>]:<\/span> array<\/span>([<\/span>0<\/span>,<\/span> 1<\/span>,<\/span> 2<\/span>,<\/span> 3<\/span>,<\/span> 4<\/span>,<\/span> 5<\/span>,<\/span> 6<\/span>,<\/span> 7<\/span>,<\/span> 8<\/span>,<\/span> 9<\/span>])<\/span>\r\n\r\nIn<\/span> [<\/span>4<\/span>]:<\/span> arr<\/span> *<\/span> 10<\/span> # \u30c7\u30fc\u30bf\u306e\u52a0\u5de5\r\n<\/span>Out<\/span>[<\/span>4<\/span>]:<\/span> array<\/span>([<\/span> 0<\/span>,<\/span> 10<\/span>,<\/span> 20<\/span>,<\/span> 30<\/span>,<\/span> 40<\/span>,<\/span> 50<\/span>,<\/span> 60<\/span>,<\/span> 70<\/span>,<\/span> 80<\/span>,<\/span> 90<\/span>])<\/span>\r\n<\/code><\/pre>\n
NumPy \u662f\u4e00\u79cd\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u7684\u5e93\u3002<\/p>\n
\u5927\u718a\u732b<\/h3>\n
Pandas\u662f\u4e00\u79cd\u6269\u5c55\u4e86NumPy\u7684\u5e93\uff0c\u5177\u6709\u5728\u673a\u5668\u5b66\u4e60\u7684\u524d\u5904\u7406\u4e2d\u5fc5\u4e0d\u53ef\u5c11\u7684\u529f\u80fd\uff0c\u5982\u6570\u636e\u52a0\u8f7d\u548c\u7f3a\u5931\u503c\u5904\u7406\u3002\u5b83\u6709\u4e00\u4e2a\u540d\u4e3aDataFrame\u7684\u5bf9\u8c61\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5408\u5e76\u3002\u7c7b\u4f3c\u4e8eR\u4e2d\u7684data.frame\u3002<\/p>\n
In<\/span> [<\/span>1<\/span>]:<\/span> import<\/span> pandas<\/span> as<\/span> pd<\/span> # Pandas \u306e\u30a4\u30f3\u30dd\u30fc\u30c8\r\n<\/span>\r\nIn<\/span> [<\/span>2<\/span>]:<\/span> df<\/span> =<\/span> pd<\/span>.<\/span>DataFrame<\/span>({<\/span> # \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u4f5c\u6210\r\n<\/span>   ...:<\/span> 'A'<\/span>:<\/span> [<\/span>n<\/span> for<\/span> n<\/span> in<\/span> range<\/span>(<\/span>5<\/span>)],<\/span>\r\n   ...:<\/span> 'B'<\/span>:<\/span> [<\/span>'male'<\/span>,<\/span> 'male'<\/span>,<\/span> 'female'<\/span>,<\/span> 'female'<\/span>,<\/span> 'male'<\/span>],<\/span>\r\n   ...:<\/span> 'C'<\/span>:<\/span> [<\/span>0.3<\/span>,<\/span> 0.4<\/span>,<\/span> 1.2<\/span>,<\/span> 100.5<\/span>,<\/span> -<\/span>20.0<\/span>]<\/span>\r\n   ...:<\/span> })<\/span>\r\n\r\nIn<\/span> [<\/span>3<\/span>]:<\/span> df<\/span>\r\nOut<\/span>[<\/span>3<\/span>]:<\/span> \r\n   A<\/span>       B<\/span>      C<\/span>\r\n0<\/span>  0<\/span>    male<\/span>    0.3<\/span>\r\n1<\/span>  1<\/span>    male<\/span>    0.4<\/span>\r\n2<\/span>  2<\/span>  female<\/span>    1.2<\/span>\r\n3<\/span>  3<\/span>  female<\/span>  100.5<\/span>\r\n4<\/span>  4<\/span>    male<\/span>  -<\/span>20.0<\/span>\r\n\r\nIn<\/span> [<\/span>4<\/span>]:<\/span> df<\/span>.<\/span>describe<\/span>()<\/span> # \u57fa\u672c\u7d71\u8a08\u91cf\u306e\u51fa\u529b\r\n<\/span>Out<\/span>[<\/span>4<\/span>]:<\/span> \r\n              A<\/span>           C<\/span>\r\ncount<\/span>  5.000000<\/span>    5.000000<\/span>\r\nmean<\/span>   2.000000<\/span>   16.480000<\/span>\r\nstd<\/span>    1.581139<\/span>   47.812101<\/span>\r\nmin<\/span>    0.000000<\/span>  -<\/span>20.000000<\/span>\r\n25<\/span>%<\/span>    1.000000<\/span>    0.300000<\/span>\r\n50<\/span>%<\/span>    2.000000<\/span>    0.400000<\/span>\r\n75<\/span>%<\/span>    3.000000<\/span>    1.200000<\/span>\r\nmax<\/span>    4.000000<\/span>  100.500000<\/span>\r\n\r\nIn<\/span> [<\/span>5<\/span>]:<\/span> df<\/span>[<\/span>df<\/span>[<\/span>'B'<\/span>]<\/span> ==<\/span> 'female'<\/span>]<\/span> # \u30b5\u30d6\u30bb\u30c3\u30c8\u306e\u547c\u3073\u51fa\u3057\r\n<\/span>Out<\/span>[<\/span>5<\/span>]:<\/span> \r\n   A<\/span>       B<\/span>      C<\/span>\r\n2<\/span>  2<\/span>  female<\/span>    1.2<\/span>\r\n3<\/span>  3<\/span>  female<\/span>  100.5<\/span>\r\n<\/code><\/pre>\n
Python\u6570\u636e\u5206\u6790\u5e93 \u2014 pandas\uff1aPython\u6570\u636e\u5206\u6790\u5e93<\/p>\n
\u62a5\u544a\uff0c\u53ef\u89c6\u5316<\/h2>\n
\u6731\u5e87\u7279<\/h3>\n
Jupyter Notebook\u662fPython\u7684\u6267\u884c\u73af\u5883\uff0c\u53ef\u4ee5\u7528\u4f5c\u6570\u636e\u63a2\u7d22\u3001\u6570\u636e\u5904\u7406\u548c\u7edf\u8ba1\u5206\u6790\u7684\u7f16\u7801\u73af\u5883\uff0c\u5b83\u80fd\u8bb0\u5f55\u4ee3\u7801\u7684\u5185\u5bb9\u548c\u8f93\u51fa\u7ed3\u679c\u3002\u6b64\u5916\uff0c\u8fd8\u53ef\u4ee5\u5c06\u5176\u8f93\u51fa\u4e3a\u62a5\u544a\u6216\u5e7b\u706f\u7247\u7684\u5f62\u5f0f\u3002<\/p>\n
\"\"<\/div>\n
\u9879\u76eeJupyter | \u9996\u9875<\/p>\n
\u56fe\u50cf\u7ed8\u5236\u3002<\/h3>\n
matplotlib\u662f\u4e00\u4e2a\u7528\u4e8e\u7ed8\u5236\u56fe\u8868\u7684\u5e93\u3002\u5b83\u652f\u6301\u6761\u5f62\u56fe\u3001\u6563\u70b9\u56fe\u3001\u76f4\u65b9\u56fe\u7b49\u5404\u79cd\u7c7b\u578b\u7684\u56fe\u8868\u3002<\/p>\n
Matplotlib\uff08\u7ed8\u56fe\u5e93\uff09\uff1aPython\u7ed8\u56fe \u2014\u2014 Matplotlib 2.0.2 \u6587\u6863<\/p>\n
Plotly\u662f\u4e00\u79cd\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\u3002<\/h3>\n
plotly\u53ef\u4ee5\u7ed8\u5236\u6bd4matplotlib\u66f4\u4e30\u5bcc\u4e14\u5177\u6709\u4e92\u52a8\u6027\u7684\u56fe\u5f62\u3002\u60a8\u8fd8\u53ef\u4ee5\u5728plot.ly\u4e0a\u5171\u4eab\u6240\u521b\u5efa\u7684\u56fe\u5f62\u3002<\/p>\n
\"Plot<\/div>\n
Python\u7ed8\u56fe\u5e93\uff0cPlotly<\/p>\n
\u6d88\u606f\u4f20\u9012\u3001\u6d41\u5f0f\u5904\u7406<\/h2>\n
\u5361\u592b\u5361-Python<\/h3>\n
Kafka-Python\u662f\u4e00\u4e2a\u540d\u526f\u5176\u5b9e\u7684Python\u5ba2\u6237\u7aef\uff0c\u7528\u4e8e\u8fde\u63a5Apache Kafka\u3002<\/p>\n
from<\/span> kafka<\/span> import<\/span> KafkaConsumer<\/span>\r\nimport<\/span> json<\/span>\r\n\r\nconsumer<\/span> =<\/span> KafkaConsumer<\/span>(<\/span>'topic'<\/span>,<\/span> bootstrap_servers<\/span>=<\/span>'localhost:9092'<\/span>)<\/span>\r\n\r\nfor<\/span> msg<\/span> in<\/span> consumer<\/span>:<\/span>\r\n    data<\/span> =<\/span> json<\/span>.<\/span>loads<\/span>(<\/span>msg<\/span>.<\/span>value<\/span>.<\/span>decode<\/span>())<\/span>\r\n    print<\/span>(<\/span>data<\/span>)<\/span>\r\n<\/code><\/pre>\n
PySpark can be paraphrased in Chinese as “PySpark \u53ef\u4ee5”.<\/h3>\n
Spark\u548cKafka\u5df2\u7ecf\u6210\u4e3a\u5fc5\u4e0d\u53ef\u5c11\u7684\u5927\u6570\u636e\u5de5\u5177\u3002\u5b58\u5728\u7740\u4e00\u4e2a\u540d\u4e3aMLlib\u7684\u673a\u5668\u5b66\u4e60\u5e93\u3002<\/p>\n
Python\u7f16\u7a0b\u6307\u5357- Spark 0.9.0\u6587\u6863<\/p>\n
\u673a\u5668\u5b66\u4e60<\/h2>\n
scikit-learn \u673a\u5668\u5b66\u4e60\u5e93<\/h3>\n
scikit-learn\u662f\u4e00\u500b\u6a5f\u5668\u5b78\u7fd2\u7684\u51fd\u5f0f\u5eab\u3002\u5b83\u4e0d\u50c5\u652f\u6301\u6d41\u884c\u7684\u795e\u7d93\u7db2\u7d61\uff0c\u9084\u53ef\u4ee5\u4f7f\u7528\u5176\u4ed6\u7b97\u6cd5\u3002\u6b64\u5916\uff0c\u5b83\u9084\u63d0\u4f9b\u4e86\u8655\u7406\u6a5f\u5668\u5b78\u7fd2\u6240\u9700\u7684\u8a13\u7df4\u6578\u64da\u548c\u9a57\u8b49\u6578\u64da\u7684\u529f\u80fd\uff0c\u5982\u5206\u5272\u548c\u4ea4\u53c9\u9a57\u8b49\u4ee5\u53ca\u7db2\u683c\u641c\u7d22\u7b49\u3002\u9019\u662f\u4e00\u500b\u975e\u5e38\u5be6\u7528\u7684\u51fd\u5f0f\u5eab\uff0c\u53ef\u4ee5\u89e3\u6c7a\u4e00\u4e9b\u68d8\u624b\u7684\u554f\u984c\u3002\u5982\u679c\u4f60\u60f3\u5b78\u7fd2\u6a5f\u5668\u5b78\u7fd2\u51fd\u5f0f\u5eab\uff0c\u9996\u5148\u5c31\u5f9e\u9019\u500b\u958b\u59cb\u5427\u3002<\/p>\n
scikit-learn\uff1aPython\u4e2d\u7684\u673a\u5668\u5b66\u4e60 – scikit-learn 0.18.2\u6587\u6863<\/p>\n
TensorFlow: TensorFlow<\/h3>\n
\u4f60\u662f\u5426\u719f\u6089\u6df1\u5ea6\u5b66\u4e60\u5e93\uff1f<\/p>\n
TensorFlow<\/p>\n
TensorFlow\uff08\u5f20\u91cf\u6d41\uff09<\/p>\n
Keras: \u51ef\u62c9\u65af<\/h3>\n
Keras\u662f\u5bf9TensorFlow\u3001CNTK\u548cTheano\u7b49\u6846\u67b6\u7684\u5c01\u88c5\u3002<\/p>\n
Keras \u6587\u6863<\/p>\n
\u63a8\u8350\u7684\u4e66\u7c4d<\/h2>\n
\u5965\u30e9\u30a4\u30ea\u30fc\u30b8\u30e3\u30d1\u30f3 – \u30c7\u30fc\u30bf\u5206\u6790\u306e\u521d\u6b69\u3092Python\u3067\u5b66\u3076<\/h3>\n
\u8fd9\u672c\u4e66\u662f\u7531Pandas\u7684\u4f5c\u8005\u6240\u5199\u7684\u3002\u4f60\u53ef\u4ee5\u5b66\u4e60\u5230Pandas\u7684\u4f7f\u7528\u65b9\u6cd5\u548c\u6570\u636e\u5206\u6790\u6280\u5de7\u3002\u5b83\u8fd8\u6db5\u76d6\u4e86NumPy\u3001matplotlib\u7b49\u5468\u8fb9\u5e93\u3002<\/p>\n
O’Reilly\u65e5\u672c – Python\u6a5f\u68b0\u5b66\u7fd2\u5165\u9580<\/h3>\n
\u8fd9\u672c\u4e66\u662f\u7531scikit-learn\u7684\u4f5c\u8005\u7f16\u5199\u7684\u3002\u53ef\u4ee5\u5b66\u4e60\u5982\u4f55\u4f7f\u7528scikit-learn\u548c\u673a\u5668\u5b66\u4e60\u6240\u9700\u7684\u5de5\u7a0b\u77e5\u8bc6\u3002<\/p>\n
\u76f4\u63a5\u7ffb\u8bd1\u4e3a\u4e2d\u6587\uff1a\u201c\u8df3\u51fa Python\u201d<\/h2>\n
\u5982\u679c\u4f60\u4e0d\u4ec5\u4ec5\u6ee1\u8db3\u4e8e\u5728Pandas\u4e2d\u64cd\u7eb5\u6570\u636e\u6216\u8c03\u6574\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u90a3\u4e48\u4f60\u53ef\u80fd\u9700\u8981\u8df3\u51faPython\u7684\u751f\u6001\u7cfb\u7edf\u3002\u6570\u636e\u7684\u4e16\u754c\u662f\u6df1\u4e0d\u53ef\u6d4b\u4e14\u5e7f\u9614\u7684\uff0c\u5de5\u7a0b\u5e08\u4eec\u9700\u8981\u6db5\u76d6\u66f4\u5e7f\u6cdb\u7684\u8303\u56f4\u6765\u652f\u6301\u6570\u636e\u79d1\u5b66\u5bb6\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u638c\u63e1\u5206\u5e03\u5f0f\u5904\u7406\u57fa\u7840\u8bbe\u65bd\uff08\u5982Hadoop\u3001Spark\u3001Apex\uff09\u4ee5\u53ca\u5168\u6258\u7ba1\u6570\u636e\u4ed3\u5e93\uff08\u5982BigQuery\u3001TreasureData\uff09\u7b49\uff0c\u5c06\u4f1a\u6269\u5927\u4f60\u7684\u6d3b\u8dc3\u573a\u666f\u3002<\/p>\n
    \n
  • \n
      \u30b9\u30c8\u30ea\u30fc\u30e0\u51e6\u7406\u3067\u6587\u7ae0\u5185\u306b\u3042\u308b\u5358\u8a9e\u306e\u51fa\u73fe\u983b\u5ea6\u3092\u30ab\u30a6\u30f3\u30c8\u3059\u308b(Apache Apex) – \u99c4\u6587\u578b<\/ul>\n<\/li>\n<\/ul>\n
       <\/p>\n
        fluentd\u30b3\u30f3\u30c6\u30ca\u3092Docker\u3067\u7acb\u3066\u3066Rails\u306e\u30ed\u30b0\u3092TreasureData by IDCF\u306b\u4fdd\u5b58\u3059\u308b – \u99c4\u6587\u578b<\/ul>\n","protected":false},"excerpt":{"rendered":"
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