{"id":1197,"date":"2021-10-15T16:05:43","date_gmt":"2021-10-15T08:05:43","guid":{"rendered":"https:\/\/aif.amtbbs.org\/?p=1197"},"modified":"2021-10-15T16:05:43","modified_gmt":"2021-10-15T08:05:43","slug":"%e6%95%b0%e6%8d%ae%e7%a7%91%e5%ad%a6%e5%ae%b6%e5%b8%b8%e7%8a%af%e7%9a%8415%e4%b8%aa%e7%bc%96%e7%a0%81%e9%94%99%e8%af%af","status":"publish","type":"post","link":"https:\/\/aif.amtbbs.org\/index.php\/2021\/10\/15\/1197\/","title":{"rendered":"\u6570\u636e\u79d1\u5b66\u5bb6\u5e38\u72af\u768415\u4e2a\u7f16\u7801\u9519\u8bef"},"content":{"rendered":"
\"\u56fe\u7247\"<\/section>\n
\u4f5c\u8005 | Gerold Csendes<\/section>\n
\u8bd1\u8005 | \u738b\u5764\u7965<\/section>\n
\u7b56\u5212 | \u51cc\u654f<\/section>\n
\u672c\u6587\u4f5c\u8005\u4ecb\u7ecd\u4e86\u6570\u636e\u79d1\u5b66\u5bb6\u5728\u7f16\u5199\u4ee3\u7801\u65f6\u5e38\u72af\u7684\u51e0\u4e2a\u9519\u8bef\uff0c\u5e76\u7ed9\u51fa\u4e86\u81ea\u5df1\u5bf9\u95ee\u9898\u7684\u770b\u6cd5\u4ee5\u53ca\u76f8\u5e94\u7684\u89e3\u51b3\u65b9\u6848\u3002\u5e0c\u671b\u6587\u4e2d\u7684\u89c2\u70b9\u80fd\u7ed9\u8bfb\u8005\u5e26\u6765\u4e00\u4e9b\u542f\u53d1\u3002<\/section>\n

\u7f16\u5199\u5e94\u7528\u4e8e\u6570\u636e\u79d1\u5b66\u9879\u76ee\u7684 Python \u4ee3\u7801\uff0c\u5e76\u6309\u7167\u81ea\u5df1\u7684\u671f\u671b\u8fd0\u884c\u8d77\u6765\uff0c\u53ef\u80fd\u6ca1\u6709\u4ec0\u4e48\u56f0\u96be\u3002\u4f46\u662f\uff0c\u5982\u679c\u4f60\u60f3\u8ba9\u81ea\u5df1\u7684\u4ee3\u7801\u5bf9\u5176\u4ed6\u4eba\uff08\u5305\u62ec\u672a\u6765\u7684\u81ea\u5df1\uff09\u6709\u9ad8\u53ef\u8bfb\u6027\uff0c\u5e76\u4e14\u53ef\u91cd\u73b0\u53ca\u8fd0\u884c\u65f6\u7ef4\u6301\u9ad8\u6548\u7387\uff0c\u53ef\u80fd\u5c31\u6ca1\u90a3\u4e48\u5bb9\u6613\u4e86\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u51cf\u5c11\u5f00\u53d1\u4e2d\u5e38\u89c1\u7684\u4e0d\u826f\u505a\u6cd5\u6765\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u3002<\/p>\n

\u5728\u6211\u4ece\u4e8b\u6570\u636e\u79d1\u5b66\u7684\u804c\u4e1a\u751f\u6daf\u4e2d\uff0c\u6211\u9010\u6e10\u610f\u8bc6\u5230\uff0c\u901a\u8fc7\u5e94\u7528\u8f6f\u4ef6\u5de5\u7a0b\u7684\u6700\u4f73\u5b9e\u8df5\uff0c\u53ef\u4ee5\u4ea4\u4ed8\u8d28\u91cf\u66f4\u9ad8\u7684\u9879\u76ee\u3002\u9ad8\u8d28\u91cf\u7684\u9879\u76ee\u610f\u5473\u7740\u6781\u5c11\u7684\u9519\u8bef\u3001\u53ef\u590d\u73b0\u51c6\u786e\u7ed3\u679c\u4ee5\u53ca\u9ad8\u6548\u7684\u4ee3\u7801\u6267\u884c\u6548\u7387\u3002\u672c\u6587\u4e0d\u4f1a\u4e8b\u65e0\u5de8\u7ec6\u5730\u5411\u4f60\u4ecb\u7ecd\u8fd9\u4e9b\u6700\u4f73\u5b9e\u8df5\u3002\u76f8\u53cd\uff0c\u6211\u603b\u7ed3\u4e86\u51e0\u70b9\u5f00\u53d1\u4e2d\u6700\u5e38\u89c1\u5230\u7684\u95ee\u9898\uff08\u4e5f\u662f\u6211\u81ea\u5df1\u4e4b\u524d\u7ecf\u5e38\u72af\u7684\u9519\u8bef\uff09\uff0c\u5e76\u6709\u9488\u5bf9\u6027\u5730\u7ed9\u51fa\u76f8\u5e94\u7684\u89e3\u51b3\u65b9\u6cd5\u53ca\u5176\u76f8\u5173\u5b66\u4e60\u8d44\u6599\u3002<\/p>\n

1. \u6ca1\u6709\u914d\u7f6e\u72ec\u7acb\u7684\u5f00\u53d1\u73af\u5883<\/section>\n

\u4ece\u67d0\u4e00\u65b9\u9762\u6765\u770b\uff0c\u8fd9\u53ef\u80fd\u4e0d\u662f\u7f16\u7801\u95ee\u9898\uff0c\u4f46\u6211\u4ecd\u7136\u575a\u6301\u8ba4\u4e3a\u72ec\u7acb\u7684\u8fd0\u884c\u73af\u5883\u662f\u4ee3\u7801\u5065\u5eb7\u8fd0\u884c\u7684\u4fdd\u8bc1\u3002\u6211\u8ba4\u4e3a\u8981\u7ed9\u6bcf\u4e2a\u9879\u76ee\u914d\u7f6e\u72ec\u7acb\u7684\u4e13\u7528\u73af\u5883\uff0c\u8fd9\u6837\u624d\u80fd\u4fdd\u8bc1\u4ee3\u7801\u7684\u53ef\u91cd\u73b0\u6027\u3002\u9879\u76ee\u4ee3\u7801\u672a\u6765\u53ef\u80fd\u4f1a\u8fd0\u884c\u5728\u4f60\u7684\u7535\u8111\u4e0a\uff0c\u6216\u8005\u662f\u4f60\u540c\u4e8b\u7684\u7535\u8111\u4e0a\uff0c\u751a\u81f3\u6709\u53ef\u80fd\u90e8\u7f72\u5230\u751f\u4ea7\u73af\u5883\u4e2d\u3002<\/p>\n

\u5982\u679c\u4f60\u4e0d\u6e05\u695a\u4ec0\u4e48\u662f\u4f9d\u8d56\u7ba1\u7406\uff0c\u90a3\u4e48\u6700\u597d\u5148\u4e86\u89e3\u548c\u5b66\u4e60\u4e0b Anaconda Virtual Environment \u4ee5\u53ca Pipenv\u3002\u6211\u4e2a\u4eba\u6700\u5e38\u4f7f\u7528 Anaconda\uff0c\u4f60\u53ef\u4ee5\u70b9\u51fb\u8fd9\u91cc (https:\/\/towardsdatascience.com\/a-guide-to-conda-environments-bc6180fc533) \u5b66\u4e60\u4e0b\u5165\u95e8\u6559\u7a0b\u3002\u5982\u679c\u4f60\u60f3\u8fdb\u9636\u6216\u8005\u8fdb\u884c\u5de5\u7a0b\u5316\u5b9e\u8df5\uff0c\u90a3\u4e48\u53ef\u4ee5\u8003\u8651\u4f7f\u7528 Docker\u3002<\/p>\n

2. \u8fc7\u5ea6\u4f7f\u7528 Jupyter Notebooks<\/section>\n

Notebooks \u975e\u5e38\u9002\u5408\u7528\u4e8e\u6559\u5b66\u4ee5\u53ca\u521d\u671f\u9879\u76ee\u7814\u7a76\uff0c\u4f7f\u7528\u5b83\u53ef\u4ee5\u5feb\u901f\u5b8c\u6210\u4e00\u4e9b\u5c0f\u7684\u68d8\u624b\u9879\u76ee\u3002\u5c3d\u7ba1\u5982\u6b64\uff0c\u5b83\u4ecd\u7136\u4e0d\u80fd\u7b97\u662f\u4e00\u4e2a\u597d\u7684 IDE\u3002\u5de5\u6b32\u5584\u5176\u4e8b\u5fc5\u5148\u5229\u5176\u5668\uff0c\u597d\u7684 IDE \u662f\u6570\u636e\u79d1\u5b66\u5bb6\u771f\u6b63\u7684\u6b66\u5668\uff0c\u4f18\u79c0\u7684\u5de5\u5177\u53ef\u4ee5\u6781\u5927\u5730\u63d0\u9ad8\u4f60\u7684\u5de5\u4f5c\u6548\u7387\u3002\u6709\u5f88\u591a\u5927\u795e\u6307\u51fa\u8fc7 Notebooks \u7684\u4e00\u4e9b\u7f3a\u70b9\uff0cJoel Grus \u66fe\u7ecf\u53d1\u8868\u8fc7\u4e00\u6b21\u6f14\u8bb2 (https:\/\/www.youtube.com\/watch?v=7jiPeIFXb6U)\uff0c\u5185\u5bb9\u975e\u5e38\u641e\u7b11\u5e7d\u9ed8\uff0c\u8fd9\u91cc\u63a8\u8350\u7ed9\u5927\u5bb6\u3002<\/p>\n

Notebooks \u975e\u5e38\u9002\u5408\u9879\u76ee\u524d\u671f\u7684\u8bd5\u9a8c\u7814\u7a76\uff0c\u800c\u4e14\u53ef\u4ee5\u975e\u5e38\u65b9\u4fbf\u5730\u5411\u4ed6\u4eba\u5c55\u793a\u7814\u7a76\u6210\u679c\uff0c\u8fd9\u4e00\u70b9\u975e\u5e38\u4e0d\u9519\u3002\u7136\u800c\uff0c\u5f53\u6d89\u53ca\u5230\u8fdb\u884c\u957f\u5468\u671f\u3001\u534f\u4f5c\u53ca\u53ef\u90e8\u7f72\u7684\u9879\u76ee\u65f6\uff0c\u5b83\u975e\u5e38\u5bb9\u6613\u51fa\u9519\u3002\u8fd9\u4e2a\u65f6\u5019\uff0c\u4f60\u6700\u597d\u4f7f\u7528\u4e00\u4e2a\u4e13\u4e1a\u7684 IDE\uff0c\u6bd4\u5982 VScode\u3001Pycharm\u3001Spyder \u7b49\u3002\u5728\u9879\u76ee\u5468\u671f\u4e0d\u8d85\u8fc7\u4e00\u5929\u7684\u60c5\u51b5\u4e0b\uff0c\u6211\u4e5f\u4f1a\u65f6\u4e0d\u65f6\u5730\u4f7f\u7528\u4e00\u4e0b Notebooks\uff0c\u8fd9\u53ef\u80fd\u662f\u6211\u60f3\u5230\u7684\u552f\u4e00\u4f7f\u7528\u5b83\u7684\u573a\u666f\u4e86\u3002<\/p>\n

3. \u9879\u76ee\u4ee3\u7801\u7ed3\u6784\u6df7\u4e71<\/section>\n

\u6211\u89c1\u8fc7\u4e0d\u5c11\u4eba\u5c06\u9879\u76ee\u7684\u6240\u6709\u4ee3\u7801\u4ee5\u53ca\u76f8\u5173\u6587\u4ef6\u5b58\u50a8\u5728\u4e00\u4e2a\u76ee\u5f55\u91cc\uff0c\u8fd9\u662f\u4e00\u4e2a\u5341\u5206\u4e0d\u4e13\u4e1a\u7684\u505a\u6cd5\u3002<\/p>\n

\u5982\u4e0b\u56fe\u6240\u793a\uff0c\u60f3\u8c61\u4e00\u4e0b\u4f60\u8981\u63a5\u624b\u4e00\u4e2a\u9879\u76ee\uff0c\u4f60\u66f4\u559c\u6b22\u54ea\u79cd\u9879\u76ee\u4ee3\u7801\u7ed3\u6784\u3002\u56fe\u7247\u4e2d\u53f3\u9762\u7684\u9879\u76ee\u4ee3\u7801\u7ed3\u6784\u7edd\u5bf9\u4f1a\u8ba9\u4f60\u548c\u5176\u4ed6\u63a5\u76d8\u4fa0\u75af\u6389\u7684\uff0c\u56e0\u4e3a\u8fd9\u4f1a\u8ba9\u4f60\u82b1\u8d39\u6570\u500d\u7684\u65f6\u95f4\u6765\u7814\u7a76\u9879\u76ee\u4ee3\u7801\u3002\u6bcb\u5eb8\u7f6e\u7591\uff0c\u5de6\u8fb9\u7684\u4ee3\u7801\u7ed3\u6784\u8981\u6bd4\u53f3\u8fb9\u5408\u7406\u8bb8\u591a\u3002\u6240\u4ee5\uff0c\u6211\u4eec\u5e94\u8be5\u600e\u4e48\u6784\u5efa\u9879\u76ee\u7ed3\u6784\u5462\uff1f\u8fd9\u91cc\u63a8\u8350\u7ed9\u5927\u5bb6\u4e00\u4e2a\u5de5\u5177\u2014\u2014 Cookiecutter(https:\/\/drivendata.github.io\/cookiecutter-data-science\/)\uff0c\u8fd9\u662f\u4e00\u4e2a\u5341\u5206\u4f18\u79c0\u7684\u5f00\u6e90\u9879\u76ee\uff0c\u5b83\u4fc3\u8fdb\u4e86\u6570\u636e\u79d1\u5b66\u9879\u76ee\u4ee3\u7801\u7ed3\u6784\u7684\u6807\u51c6\u5316\uff0c\u4f60\u53ef\u4ee5\u4ece\u4e2d\u5b66\u4e60\u4e00\u4e0b\u3002<\/p>\n

\"\u56fe\u7247\"<\/p>\n

4. \u9879\u76ee\u4ee3\u7801\u4f7f\u7528\u7edd\u5bf9\u8def\u5f84\u800c\u4e0d\u662f\u76f8\u5bf9\u8def\u5f84<\/section>\n

\u4f60\u6709\u5728\u4e2a\u4eba\u5f00\u6e90\u7684\u9879\u76ee\u4e2d\u6709\u770b\u5230\u8fc7\u201c\u8bf7\u4fee\u590d\u4f60\u7684\u6587\u4ef6\u8def\u5f84\u201d\u7684\u8bc4\u8bba\u5417\uff1f\u8fd9\u6837\u7684\u8bc4\u8bba\u5f80\u5f80\u6697\u793a\u4e86\u7cdf\u7cd5\u7684\u4ee3\u7801\u8bbe\u8ba1\u3002\u4fee\u590d\u8be5\u95ee\u9898\u4e00\u822c\u5305\u62ec\u4e24\u4e2a\u6b65\u9aa4\uff1a<\/p>\n

    \n
  • \u4e0e\u4ed6\u4eba\u5171\u4eab\u9879\u76ee\u7ed3\u6784\uff08\u53c2\u8003\u672c\u6587\u7b2c\u4e09\u6761\u5efa\u8bae\uff09<\/li>\n
  • \u5c06\u4f60\u7684 IDE \u6839\u76ee\u5f55 \/ \u5de5\u4f5c\u76ee\u5f55\u8bbe\u7f6e\u4e3a\u9879\u76ee\u6839\u76ee\u5f55\uff0c\u8be5\u76ee\u5f55\u901a\u5e38\u662f\u9879\u76ee\u4e2d\u6700\u5916\u5c42\u76ee\u5f55\u3002<\/li>\n<\/ul>\n

    \u7b2c\u4e8c\u70b9\u6709\u65f6\u5019\u4e0d\u662f\u90a3\u4e48\u7b80\u5355\uff0c\u4f46\u662f\u503c\u5f97\u4f60\u82b1\u65f6\u95f4\u8fd9\u4e48\u53bb\u505a\uff0c\u8fd9\u6837\u522b\u4eba\u5c31\u53ef\u4ee5\u5728\u4e0d\u7528\u4fee\u6539\u4ee3\u7801\u7684\u60c5\u51b5\u4e0b\u6210\u529f\u8fd0\u884c\u4f60\u7684\u4ee3\u7801\u3002\u4e0b\u9762\u7ed9\u51fa\u4e00\u4e2a\u4f8b\u5b50\u4f9b\u5927\u5bb6\u53c2\u8003\u3002<\/p>\n

    import pandas as pd\r\nimport numpy as np\r\nimport os\r\n#### BAD WAY ####\r\n# please change it to your file path\r\nexcel_path1 = \"C:\\\\Users\\\\gerold\\\\Desktop\\\\CEU\\trim1\\\\DataEng1\\\\Team_asgn\\\\CrimeOneYearofData_2006.xlsx\"\r\nexcel_path2 = \"C:\\\\Users\\\\gerold\\\\Desktop\\\\CEU\\trim1\\\\DataEng1\\\\Team_asgn\\\\CrimeOneYearofData_2007.xlsx\"\r\n# read in excel\r\nmydf = pd.read_excel(excel_path1)\r\nmyd2 = pd.read_excel(excel_path2)\r\n#### END BAD WAY ####\r\n#### GOOD WAY ####\r\n# first put your 2 excels into the data folder\r\n# set the working directory in your IDE to the root (Team_asgn)\r\nDATA_DIR = \"data\" # indicate magical constansts (maybe rather put it on the top of the script)\r\n# fix gruesome var namescrime06_filename = \"\r\nCrimeOneYearofData_2006.xlsx\"crime07_filename = \"\r\nCrimeOneYearofData_2007.xlsx\"\r\n# fix gruesome var names\r\ncrime06_df = pd.read_excel(os.path.join(DATA_DIR, crime06_filename))\r\ncrime07_df = pd.read_excel(os.path.join(DATA_DIR, crime07_filename))\r\n#### END GOOD WAY ####\r\n<\/code><\/pre>\n
    5. \u4f7f\u7528\u201c\u5e7b\u6570\u201d<\/section>\n
    \u5e7b\u6570\u662f\u5728\u4ee3\u7801\u4e2d\u6ca1\u6709\u4efb\u4f55\u4e0a\u4e0b\u6587\u7684\u6570\u5b57\u3002\u4ee3\u7801\u4e2d\u9891\u7e41\u5927\u91cf\u5730\u4f7f\u7528\u5e7b\u6570\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u96be\u4ee5\u8ffd\u8e2a\u7684\u95ee\u9898\u3002<\/p>\n
    \u4e0b\u9762\u7684\u4ee3\u7801\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5728\u4e58\u6cd5\u8ba1\u7b97\u65f6\u7b80\u5355\u5730\u4f7f\u7528\u4e86\u4e00\u4e2a\u672a\u5206\u914d\u53d8\u91cf\u7684\u6570\u5b57\uff0c\u800c\u4e14\u6ca1\u6709\u4efb\u4f55\u4e0a\u4e0b\u6587\u6765\u89e3\u91ca\u8fd9\u4e2a\u6570\u5b57\u7684\u542b\u4e49\u3002\u5982\u679c\u4f60\u4ee5\u540e\u4e0d\u5f97\u4e0d\u5bf9\u5176\u8fdb\u884c\u4fee\u6539\uff0c\u5c31\u4f1a\u9762\u4e34\u5341\u5206\u5c34\u5c2c\u7684\u5c40\u9762\uff0c\u56e0\u4e3a\u4f60\u4e0d\u77e5\u9053\u8be5\u6570\u5b57\u7684\u5177\u4f53\u542b\u4e49\u3002\u56e0\u6b64\uff0c\u5bf9\u4e8e\u6b64\u7c7b\u5e38\u91cf\uff0c\u6309\u7167\u60ef\u4f8b\u5728 Python \u4e2d\u4f7f\u7528\u5927\u5199\u547d\u540d\u3002\u5f53\u7136\u4f60\u4e5f\u53ef\u4ee5\u575a\u6301\u4e0d\u4f7f\u7528\u5927\u5199\uff0c\u4f46\u662f\u5c06\u201c\u5e38\u91cf\u201d\u4e0e\u201c\u5e38\u89c4\u53d8\u91cf\u201d\u533a\u5206\u5f00\u6765\uff0c\u662f\u4e00\u4e2a\u4e0d\u9519\u7684\u7f16\u7a0b\u4e60\u60ef\u3002<\/p>\n
    # assign revenues in $ to marketing campaigns\r\ncamp1_revenue = 50000\r\ncamp2_revenue = 100000\r\n#### BAD WAY ####\r\n# calc whic performed better\r\ncamps_revenue_diff = (camp2_revenue * 0.65) - camp1_revenue\r\n#### END BAD WAY ####\r\n#### GOOD WAY ####\r\nCAMP2_NORMALIZER = 0.65 # we need to normalize because the campaign ran in peak season\r\n# calc whic performed better\r\ncamps_revenue_diff = (camp2_revenue * CAMP2_NORMALIZER) - camp1_revenue\r\n#### END GOOD WAY ####\r\n<\/code><\/pre>\n
    6. \u4e0d\u5904\u7406\u544a\u8b66\u4fe1\u606f<\/section>\n
    \u4f30\u8ba1\u5f88\u591a\u4eba\u90fd\u6709\u8fd9\u6837\u7684\u4e60\u60ef\uff1a\u5bf9\u4ee3\u7801\u8fd0\u884c\u8fc7\u7a0b\u4e2d\u4ea7\u751f\u7684\u544a\u8b66\u4fe1\u606f\u7f6e\u4e4b\u4e0d\u7406\u3002\u6211\u4eec\u5bf9\u4ee3\u7801\u80fd\u591f\u6b63\u5e38\u8fd0\u884c\u5e76\u80fd\u591f\u8f93\u51fa\u671f\u671b\u7684\u7ed3\u679c\u5df2\u7ecf\u975e\u5e38\u6ee1\u610f\u4e86\uff0c\u6240\u4ee5\u4e3a\u4ec0\u4e48\u8981\u5904\u7406\u544a\u8b66\u4fe1\u606f\u5462\uff1f\u786e\u5b9e\uff0c\u544a\u8b66\u4fe1\u606f\u4e0d\u662f\u9519\u8bef\uff0c\u4f46\u662f\u8fd9\u4e9b\u544a\u8b66\u4fe1\u606f\u53ef\u80fd\u4f1a\u5f15\u8d77\u6f5c\u5728\u7684\u95ee\u9898\u6216\u8005\u9519\u8bef\u3002\u5c3d\u7ba1\u4ee3\u7801\u80fd\u8fd0\u884c\u6210\u529f\uff0c\u4f46\u51fa\u73b0\u8fd9\u4e9b\u544a\u8b66\u4fe1\u606f\u5b9e\u9645\u4e0a\u5e76\u4e0d\u7b26\u5408\u6211\u4eec\u7684\u9884\u671f\u3002<\/p>\n
    \u5728\u505a\u6570\u636e\u5206\u6790\u65f6\uff0c\u6211\u9047\u5230\u7684\u6700\u5e38\u89c1\u7684\u544a\u8b66\u4fe1\u606f\u662f Pandas \u7684 SettingwithCopyWarning \u548c DeprecationWarning\u3002DataSchool(https:\/\/www.youtube.com\/watch?v=4R4WsDJ-KVc) \u7684\u6559\u5b66\u89c6\u9891\u4ee5\u7b80\u6d01\u7684\u65b9\u5f0f\u89e3\u91ca\u4e86\u5982\u4f55\u89e6\u53d1 SettingwithCopyWarning\u3002DeprecationWarning \u544a\u8b66\u8bf4\u660e Pandas \u5df2\u5f03\u7528\u67d0\u4e9b\u65b9\u6cd5\uff0c\u672a\u6765\u4f60\u7684\u9879\u76ee\u4ee3\u7801\u5728\u4f7f\u7528\u66f4\u9ad8\u7248\u672c\u65f6\u4f1a\u6709\u4e2d\u65ad\u7684\u98ce\u9669\u3002\u5f53\u7136\uff0c\u8fd8\u6709\u4e00\u4e9b\u5176\u4ed6\u7684\u544a\u8b66\u7c7b\u578b\u3002\u4f9d\u7167\u6211\u7684\u7ecf\u9a8c\uff0c\u4ea7\u751f\u8fd9\u4e9b\u544a\u8b66\u5927\u90e8\u5206\u662f\u56e0\u4e3a\u4f7f\u7528\u4e86\u5de5\u5177\u7c7b\u975e\u539f\u672c\u8bbe\u8ba1\u7684\u8c03\u7528\u65b9\u5f0f\u3002\u6240\u4ee5\uff0c\u4e86\u89e3\u51fd\u6570\u7684\u6e90\u4ee3\u7801\u603b\u662f\u6709\u5e2e\u52a9\u7684\uff0c\u8fd9\u6837\u5c31\u53ef\u4ee5\u907f\u514d\u5927\u591a\u6570\u7684\u5f02\u5e38\u544a\u8b66\u4e86\u3002<\/p>\n
    7. \u4e0d\u4f7f\u7528\u7c7b\u578b\u6ce8\u89e3<\/section>\n
    \u8fd9\u4e5f\u662f\u6211\u6700\u8fd1\u5b66\u5230\u7684\u4e00\u79cd\u505a\u6cd5\uff0c\u56e0\u4e3a\u6211\u5df2\u7ecf\u4f53\u4f1a\u5230\u4e86\u4f7f\u7528\u7c7b\u578b\u6ce8\u89e3\u5e26\u6765\u7684\u597d\u5904\u3002\u7c7b\u578b\u6ce8\u89e3\uff08\u6216\u7c7b\u578b\u63d0\u793a\uff09\u7b80\u5355\u6765\u8bb2\u5c31\u662f\u4e3a\u53d8\u91cf\u6307\u5b9a\u6570\u636e\u7c7b\u578b\u3002\u57fa\u672c\u4e0a\uff0c\u4f7f\u7528 IDE \u81ea\u5e26\u7684\u4ee3\u7801\u6269\u5c55\u63d0\u793a\u5c31\u53ef\u4ee5\u5b8c\u6210\u4ee3\u7801\u53d8\u91cf\u7684\u6ce8\u89e3\u3002\u4f7f\u7528\u4ee3\u7801\u6ce8\u89e3\uff0c\u53ef\u4ee5\u8ba9\u4f60\u7684\u4ee3\u7801\u66f4\u6613\u4e8e\u81ea\u5df1\u548c\u4ed6\u4eba\u9605\u8bfb\u3002<\/p>\n
    \u4e3a\u4e86\u8bc1\u660e\u8fd9\u4e00\u70b9\uff0c\u6211\u6458\u53d6\u4e86 Daniel Starner \u5728 dev.to(https:\/\/dev.to\/dstarner\/using-pythons-type-annotations-4cfe) \u535a\u5ba2\u4e2d\u7684\u4ee3\u7801\u7247\u6bb5\u6765\u4e3e\u4e2a\u4f8b\u5b50\u3002\u5982\u4e0b\u4ee3\u7801\u6240\u793a\uff0c\u5728\u6ca1\u6709\u7c7b\u578b\u63d0\u793a\u7684\u60c5\u51b5\u4e0b\uff0cmystery_combine() \u4f7f\u7528\u6574\u6570\u6216\u5b57\u7b26\u4e32\u4f5c\u4e3a\u8f93\u5165\u5e76\u76f8\u5e94\u5730\u8fd4\u56de\u6574\u6570\u6216\u5b57\u7b26\u4e32\u4f5c\u4e3a\u7ed3\u679c\u3002\u5bf9\u4e8e\u5f00\u53d1\u4eba\u5458\u6765\u8bb2\uff0c\u8be5\u65b9\u6cd5\u7684\u63cf\u8ff0\u6709\u70b9\u6a21\u68f1\u4e24\u53ef\u3002\u5982\u679c\u4f7f\u7528\u4e86\u7c7b\u578b\u6ce8\u89e3\uff0c\u5c31\u53ef\u4ee5\u6e05\u6670\u7684\u8868\u8fbe\u51fd\u6570\u610f\u56fe\uff0c\u907f\u514d\u4ea7\u751f\u8bef\u89e3\uff0c\u540c\u65f6\u4f1a\u7ed9\u5176\u4ed6\u5f00\u53d1\u4eba\u5458\u4ee5\u53ca\u672a\u6765\u7684\u81ea\u5df1\u5e26\u6765\u4e00\u4e9b\u4fbf\u5229\u3002<\/p>\n
    # code taken from https:\/\/dev.to\/dstarner\/using-pythons-type-annotations-4cfe\r\n# Our original function\r\ndef mystery_combine(a, b, times):\r\nreturn (a + b) * times\r\nprint(mystery_combine(2, 3, 4))\r\n# 20\r\nprint(mystery_combine('Hello ', 'World! ', 4))\r\n# Hello World! Hello World! Hello World! Hello World!\r\n# show your intents explicitly by indicating types of your argument and returned value\r\ndef mystery_combine(a: str, b: str, times: int) -> str:\r\nreturn (a + b) * times\r\n<\/code><\/pre>\n
    \u6b64\u5916\uff0c\u4f7f\u7528\u7c7b\u578b\u6ce8\u89e3\u53ef\u4ee5\u5728\u65e0\u9700\u8fd0\u884c\u4ee3\u7801\u7684\u60c5\u51b5\u4e0b\uff0c\u9759\u6001\u5730\u68c0\u67e5\u4ee3\u7801\u662f\u5426\u5b58\u5728\u9519\u8bef\u3002\u4e0b\u56fe\u7684\u793a\u4f8b\u5c55\u793a\u4e86\u6ca1\u6709\u6309\u51fd\u6570\u7c7b\u578b\u6ce8\u89e3\u6307\u5b9a\u5bf9\u5e94\u53c2\u6570\uff0c\u9759\u6001\u68c0\u67e5\u7ed9\u51fa\u4e86\u76f8\u5e94\u7684\u9519\u8bef\u63d0\u793a\u3002\u9759\u6001\u68c0\u67e5\u662f\u5728\u8fd0\u884c\u9879\u76ee\u4e4b\u524d\u8fdb\u884c\u4ee3\u7801\u9884\u68c0\u67e5\u7684\u4e00\u79cd\u5341\u5206\u6709\u7528\u7684\u65b9\u6cd5\u3002<\/p>\n
    \"\u56fe\u7247\"<\/p>\n
    8. \u4e0d\u4e60\u60ef\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u8868\u8fbe\u5f0f<\/section>\n
    \u5217\u8868\u63a8\u5bfc\u8868\u8fbe\u5f0f\u662f Python \u975e\u5e38\u5f3a\u5927\u7684\u7279\u6027\u4e4b\u4e00\u3002\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u8868\u8fbe\u5f0f\uff0c\u53ef\u4ee5\u8ba9 for \u5faa\u73af\u66f4\u52a0\u6613\u4e8e\u9605\u8bfb\uff0c\u66f4\u7b26\u5408 Python \u7684\u4e60\u60ef\u8868\u8fbe\uff0c\u800c\u4e14\u6267\u884c\u6548\u7387\u4f1a\u66f4\u9ad8\u3002<\/p>\n
    \u4e0b\u9762\u7684\u4e00\u6bb5\u793a\u4f8b\u4ee3\u7801\u5c1d\u8bd5\u8bfb\u53d6\u76ee\u5f55\u4e2d\u7684 CSV \u6587\u4ef6\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\u4f60\u53ef\u80fd\u4f1a\u8bf4\uff0c\u4e0d\u4f7f\u7528\u5217\u8868\u63a8\u5bfc\u5f0f\u4e5f\u633a\u4f18\u96c5\u5440\uff0c\u6ca1\u6709\u4ec0\u4e48\u4e0d\u59a5\u3002\u4f46\u662f\uff0c\u5982\u679c\u76ee\u5f55\u91cc\u6709\u5176\u4ed6\u683c\u5f0f\u7684\u6587\u4ef6\uff0c\u6bd4\u5982 JSON \u6587\u4ef6\uff0c\u6b64\u65f6\uff0c\u4f7f\u7528\u5217\u8868\u8868\u8fbe\u5f0f\u7684\u4fbf\u6377\u6027\u548c\u53ef\u8bfb\u6027\u4f1a\u63d0\u5347\u4e00\u4e2a\u6863\u6b21\uff0c\u800c\u4e14\uff0c\u4ee3\u7801\u4e5f\u66f4\u5bb9\u6613\u7ef4\u62a4\u3002<\/p>\n
    import pandas as pd\r\nimport os\r\nDATA_PATH = \"data\"\r\nfilename_list = os.listdir(DATA_PATH)\r\n#### BAD WAY ####\r\n# read in bunch of csv-s from a dir\r\ncsv_list = []\r\nfor fileaname in filename_list:\r\ncsv_list.append(pd.read_csv(os.path.join(DATA_PATH, filename)))\r\n#### END BAD WAY ####\r\n#### GOOD WAY ####\r\ncsv_list = [pd.read_csv(os.path.join(DATA_PATH, filename)) for filename in filename_list]\r\n# what about if not only .csv-s are present? easy to tackle this with list comprehensions\r\ncsv_list = [\r\npd.read_csv(os.path.join(DATA_PATH, filename)) for filename in filename_list if filename.endswith(\".csv\")]\r\n#### END GOOD WAY ####\r\n<\/code><\/pre>\n
    9. pandas \u4ee3\u7801\u53ef\u8bfb\u6027\u5dee<\/section>\n
    \u65b9\u6cd5\u94fe\u8c03\u7528\u662f Pandas \u4e2d\u7684\u4e00\u4e2a\u5f88\u68d2\u7684\u7279\u6027\uff0c\u4f46\u662f\u5982\u679c\u4f60\u575a\u6301\u5728\u4e00\u884c\u4e2d\u8868\u8fbe\u6240\u6709\u5185\u5bb9\uff0c\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u4f1a\u53d8\u5dee\u3002\u6709\u4e00\u4e2a\u6280\u5de7\u53ef\u4ee5\u8ba9\u4f60\u5bf9\u8868\u8fbe\u5f0f\u8fdb\u884c\u5206\u89e3\u3002\u5982\u4e0b\u7684\u4ee3\u7801\u6240\u793a\uff0c\u53ef\u4ee5\u5c06\u6574\u4e2a\u8868\u8fbe\u5f0f\u653e\u5165\u62ec\u53f7\u4e2d\uff0c\u7136\u540e\u8868\u8fbe\u5f0f\u7684\u6bcf\u4e2a\u7ec4\u6210\u90e8\u5206\u53ef\u4ee5\u5355\u72ec\u4f7f\u7528\u4e00\u884c\uff0c\u8fd9\u6837\u5904\u7406\u540e\u7684\u4ee3\u7801\u770b\u8d77\u6765\u5c31\u6e05\u723d\u591a\u4e86\u3002<\/p>\n
    # lets aggregate click and time spent to its mean in a Q\r\nvar_list = [\"clicks\", \"time_spent\"]\r\nvar_list_Q = [varname + \"_Q\" for varname in var_list]\r\n#### BAD WAY ####\r\ndf_Q = df.groupby(\"id\").rolling(window=3, min_periods=1, on=\"yearmonth\")[var_list].mean().reset_index().rename(columns=dict(zip(var_list, var_list_Q)))\r\n#### BAD WAY ####\r\n#### GOOD WAY ####\r\ndf_Q = (\r\n df\r\n .groupby(\"id\")\r\n .rolling(window=3, min_periods=1, on=\"yearmonth\")[var_list]\r\n .mean()\r\n .reset_index()\r\n .rename(columns=dict(zip(var_list, var_list_Q))))\r\n#### END GOOD WAY ####\r\n<\/code><\/pre>\n
    10. \u6392\u65a5\u4f7f\u7528 Python \u81ea\u5e26\u7684 date \u5de5\u5177<\/section>\n
    \u5728 Python \u4e2d\u4f7f\u7528\u65e5\u671f\u6a21\u5757\u786e\u5b9e\u4e0d\u662f\u7279\u522b\u53cb\u597d\uff0c\u56e0\u4e3a\u5b83\u7684\u8bed\u6cd5\u6bd4\u8f83\u5947\u602a\uff0c\u800c\u4e14\u8ba9\u4eba\u96be\u4ee5\u7406\u89e3\u5e76\u8bb0\u5fc6\u3002\u6211\u7ecf\u5e38\u770b\u5230\u5f88\u591a\u4eba\u50cf\u5904\u7406\u6570\u5b57\u4e00\u6837\u5904\u7406\u65e5\u671f\u5bf9\u8c61\uff0c\u8fd9\u79cd\u505a\u6cd5\u5b9e\u5728\u4e0d\u591f\u4f18\u96c5\u3002\u867d\u7136\u5f88\u591a\u65f6\u5019\u8fd9\u4e48\u505a\u80fd\u591f\u8dd1\u901a\u4ee3\u7801\uff0c\u4f46\u662f\u8fd9\u6837\u975e\u5e38\u5bb9\u6613\u51fa\u9519\uff0c\u800c\u4e14\u7ef4\u62a4\u8d77\u6765\u975e\u5e38\u56f0\u96be\u3002<\/p>\n
    \u4ee5\u4e0b\u9762\u7684\u5b9e\u4f8b\u4ee3\u7801\u4e3a\u4f8b\uff0c\u5b83\u7684\u529f\u80fd\u662f\u5b9e\u73b0\u4ee5 %Y%m \u683c\u5f0f\u5217\u51fa\u4e24\u4e2a\u65e5\u671f\u4e4b\u95f4\u7684\u6240\u6709\u6708\u4efd\u3002\u5982\u679c\u4f60\u501f\u52a9 datetime \u5de5\u5177\u5b9e\u73b0\uff0c\u4ee3\u7801\u53ef\u8bfb\u6027\u548c\u53ef\u7ef4\u62a4\u6027\u5f97\u5230\u4e86\u63d0\u9ad8\u3002\u5b9e\u8bdd\u8bb2\uff0c\u5373\u4f7f\u662f\u73b0\u5728\uff0c\u6211\u5728\u5904\u7406\u65e5\u671f\u95ee\u9898\u65f6\u4ecd\u7136\u4f9d\u8d56\u8c37\u6b4c\u641c\u7d22\uff0c\u8fd9\u5f88\u6b63\u5e38\uff0c\u4e60\u60ef\u5c31\u597d\u4e86\u3002<\/p>\n
    import datetime\r\nfrom dateutil.relativedelta import relativedelta\r\n# task: get months between two dates in YM format\r\n#### BAD WAY ####\r\nstart_num = 201910\r\nend_num = 202012\r\nres_list = []\r\niter_num = start_num\r\nwhile iter_num < end_num:\r\nif abs(iter_num) % 100 > 12:\r\n iter_num += 88\r\n res_list.append(iter_num)\r\n iter_num += 1\r\nelse:\r\n res_list.append(iter_num)\r\n iter_num += 1\r\nres_list.append(iter_num)\r\n#### END BAD WAY ####\r\n#### GOOD WAY ####\r\n# initialize datetimes\r\nstart_datetime = datetime.datetime(2019, 10, 1)\r\nend_datetime = datetime.datetime(2020, 12, 1)\r\n# find months between end and astart date\r\nr = relativedelta(end_datetime, start_datetime)\r\nmonths_between = r.months + (12*r.years)\r\nmyres = [\r\n start_datetime + relativedelta(months=_)\r\nfor _ in range(1, months_between + 1)]\r\n# format dates\r\nmyres = [element.strftime(\"%Y%m\") for element in myres]\r\n#### END GOOD WAY ####\r\n<\/code><\/pre>\n
    11. \u53d8\u91cf\u547d\u540d\u4e0d\u89c4\u8303<\/section>\n
    \u5728\u5faa\u73af\u4e2d\u7ed9\u53d8\u91cf\u4f7f\u7528 i\uff0cj\uff0ck\uff0cdf \u7b49\u975e\u63cf\u8ff0\u6027\u5b57\u7b26\u8fdb\u884c\u547d\u540d\uff0c\u4f1a\u4f7f\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u964d\u4f4e\uff0c\u5c24\u5176\u662f\u5faa\u73af\u4e2d\u7684\u903b\u8f91\u5904\u7406\u8f83\u4e3a\u590d\u6742\u7684\u65f6\u5019\u3002\u4ee3\u7801\u4e2d\u53d8\u91cf\u547d\u540d\u77ed\u5c0f\u7cbe\u608d\uff0c\u5f80\u5f80\u5bb9\u6613\u6df7\u6dc6\u9879\u76ee\u5f00\u53d1\u4eba\u5458\uff0c\u8fd9\u4e00\u70b9\u76f8\u4fe1\u5927\u5bb6\u6df1\u6709\u4f53\u4f1a\u3002\u4e0d\u8981\u62c5\u5fc3\u4f7f\u7528\u8f83\u957f\u7684\u53d8\u91cf\u540d\uff0c\u4e5f\u4e0d\u8981\u541d\u556c\u4f7f\u7528\u4e0b\u5212\u7ebf\u201c_\u201d\u5bf9\u53d8\u91cf\u8fdb\u884c\u547d\u540d\u3002\u63a8\u8350\u7ed9\u5927\u5bb6\u4e00\u7bc7\u6709\u5173\u53d8\u91cf\u547d\u540d (https:\/\/towardsdatascience.com\/data-scientists-your-variable-names-are-awful-heres-how-to-fix-them-89053d2855be) \u7684\u9ad8\u8d28\u91cf\u535a\u5ba2\uff0c\u4e00\u5b9a\u4f1a\u5bf9\u4f60\u6709\u6240\u542f\u53d1\u3002<\/p>\n
    12. \u4e0d\u5bf9\u4ee3\u7801\u8fdb\u884c\u6a21\u5757\u5316\u91cd\u6784<\/section>\n
    \u6a21\u5757\u5316\u610f\u5473\u7740\u5c06\u5197\u957f\u4e14\u590d\u6742\u7684\u4ee3\u7801\u5206\u89e3\u6210\u7b80\u5355\u7684\u6a21\u5757\uff0c\u4ee5\u6267\u884c\u7ec6\u7c92\u5ea6\u7684\u3001\u7279\u5b9a\u7684\u4efb\u52a1\u3002\u4e0d\u8981\u53ea\u4e3a\u9879\u76ee\u521b\u5efa\u4e00\u4e2a\u5197\u957f\u7684\u6267\u884c\u811a\u672c\u3002\u5728\u4ee3\u7801\u5165\u53e3\u6587\u4ef6\u5f00\u5934\u5b9a\u4e49\u5927\u91cf\u7684\u7c7b\u6216\u51fd\u6570\u662f\u4e0d\u63a8\u8350\u7684\u505a\u6cd5\uff0c\u56e0\u4e3a\u8fd9\u6837\u505a\u4ee3\u7801\u5f88\u96be\u9605\u8bfb\u548c\u7ef4\u62a4\u3002\u76f8\u53cd\uff0c\u8981\u6839\u636e\u4ee3\u7801\u529f\u80fd\u521b\u5efa\u76f8\u5e94\u7684\u6a21\u5757\uff08\u5305\uff09\u3002\u8fd9\u65b9\u9762\u7684\u8be6\u7ec6\u5185\u5bb9\uff0c\u53ef\u4ee5\u53c2\u8003\u8fd9\u7bc7\u535a\u5ba2 Python Modules and Packages(https:\/\/realpython.com\/courses\/python-modules-packages\/) \u3002<\/p>\n
    13. \u6ca1\u6709\u9075\u5faa PEP \u7ea6\u5b9a<\/section>\n
    \u5f53\u6211\u521a\u5f00\u59cb\u4f7f\u7528 Python \u7f16\u5199\u9879\u76ee\u4ee3\u7801\u7684\u65f6\u5019\uff0c\u5199\u51fa\u7684\u4ee3\u7801\u5341\u5206\u4e11\u964b\uff0c\u96be\u4ee5\u9605\u8bfb\u3002\u5e76\u4e14\u81ea\u5df1\u8fd8\u52aa\u529b\u5730\u5236\u5b9a\u5c5e\u4e8e\u81ea\u5df1\u7684\u8bbe\u8ba1\u539f\u5219\uff0c\u597d\u8ba9\u81ea\u5df1\u7684\u4ee3\u7801\u770b\u8d77\u6765\u6ca1\u6709\u90a3\u4e48\u7cdf\u7cd5\u3002\u60f3\u51fa\u8fd9\u4e9b\u539f\u5219\u82b1\u8d39\u4e86\u6211\u4e0d\u5c11\u65f6\u95f4\uff0c\u4f46\u662f\u6211\u5e76\u6ca1\u6709\u4e00\u76f4\u575a\u6301\u8fd9\u4e9b\u539f\u5219\uff0c\u56de\u60f3\u8d77\u6765\uff0c\u53d7\u9650\u4e8e\u81ea\u5df1\u5728 Python \u7684\u7ecf\u9a8c\uff0c\u5f88\u591a\u81ea\u5df1\u8bbe\u8ba1\u539f\u5219\u6ca1\u6709\u90a3\u4e48\u5408\u7406\u3002\u6700\u7ec8\uff0c\u6211\u53d1\u73b0\u4e86 PEP(https:\/\/www.python.org\/dev\/peps\/) \u8bbe\u8ba1\u539f\u5219\uff0c\u5b83\u662f Python \u7684\u5b98\u65b9\u7ea6\u5b9a\u6307\u5357\u3002\u6211\u5f88\u559c\u6b22 PEP \u63d0\u51fa\u7684\u7ea6\u5b9a\uff0c\u56e0\u4e3a\u5b83\u53ef\u4ee5\u6807\u51c6\u5316\u6211\u7684\u4ee3\u7801\uff0c\u4ece\u800c\u4f7f\u534f\u4f5c\u7f16\u7a0b\u66f4\u52a0\u65b9\u4fbf\u3002\u987a\u4fbf\u8bf4\u4e00\u4e0b\uff0c\u6709\u4e9b\u7279\u6b8a\u60c5\u51b5\u4e0b\u6211\u786e\u5b9e\u6ca1\u6709\u6309\u7167 PEP \u89c4\u5219\u6765\u505a\uff0c\u4f46\u5728\u7edd\u5927\u591a\u6570\u60c5\u51b5\u4e0b\uff0c\u6211\u4f1a\u6309\u7167 PEP \u89c4\u8303\u6765\u5199\u4ee3\u7801\u3002<\/p>\n
    \u51e0\u4e4e\u6240\u6709\u7684 IDE \u90fd\u652f\u6301 linter \u6269\u5c55\uff0c\u4e0b\u56fe\u5c55\u793a\u4e86 linter \u7684\u5de5\u4f5c\u539f\u7406\uff0c\u5b83\u53ef\u4ee5\u6307\u51fa\u4ee3\u7801\u4e2d\u5b58\u5728\u7684\u95ee\u9898\u3002\u5982\u679c\u4f60\u4ecd\u7136\u611f\u89c9\u4e0d\u591f\u76f4\u89c2\uff0c\u4f60\u53ef\u4ee5\u67e5\u770b\u5177\u4f53\u7684 PEP \u7d22\u5f15\u63d0\u793a\uff0c\u5982\u62ec\u53f7\u4e2d\u63d0\u793a\u6240\u793a\u3002\u5982\u679c\u4f60\u60f3\u67e5\u770b\u6709\u54ea\u4e9b\u53ef\u7528\u7684 linter\uff0c\u53ef\u4ee5\u53c2\u8003 realpythong.org(https:\/\/realpython.com\/python-code-quality\/#linters) \u7f51\u7ad9\u4e0a\u7684\u5b66\u4e60\u8d44\u6e90\u3002<\/p>\n
    \"\u56fe\u7247\"<\/p>\n
    14. \u4ece\u4e0d\u4f7f\u7528\u7f16\u7801\u52a9\u624b<\/section>\n
    \u5982\u679c\u4f60\u60f3\u5927\u5e45\u63d0\u9ad8\u5199\u4ee3\u7801\u7684\u6548\u7387\uff0c\u90a3\u4e48\u5c31\u5f00\u59cb\u4f7f\u7528\u7f16\u7801\u52a9\u624b\u5427\u3002\u8be5\u5de5\u5177\u53ef\u4ee5\u5de7\u5999\u5730\u5e2e\u52a9\u4f60\u81ea\u52a8\u5b8c\u6210\u4ee3\u7801\u3001\u6dfb\u52a0\u63cf\u8ff0\u6587\u6863\u4ee5\u53ca\u7ed9\u4f60\u7684\u4ee3\u7801\u63d0\u4f9b\u4fee\u6539\u5efa\u8bae\u3002\u6211\u6700\u559c\u6b22\u4f7f\u7528\u7684\u7f16\u7801\u63d0\u793a\u5de5\u5177\u662f\u7531\u5fae\u8f6f\u5f00\u53d1\u7684 pylance\uff0c\u5b83\u652f\u6301\u5728 VScode \u4e2d\u4f7f\u7528\u3002Kite \u662f\u53e6\u4e00\u4e2a\u6bd4\u8f83\u6d41\u884c\u7684\u7f16\u7801\u52a9\u624b\uff0c\u540c\u6837\u975e\u5e38\u597d\u7528\uff0c\u8bb8\u591a\u7f16\u8f91\u5668\u90fd\u652f\u6301\u4f7f\u7528\u3002<\/p>\n
    \u4ee3\u7801\u63d0\u793a\u5de5\u5177\u7684\u4f7f\u7528\u6548\u679c\u89c6\u9891\u53ef\u4ee5\u70b9\u51fb\u6b64\u5904 (https:\/\/thumbs.gfycat.com\/BaggyNiceLemur-mobile.mp4) \u8fdb\u884c\u67e5\u770b\u3002<\/p>\n
    15. \u7f3a\u5c11\u4fe1\u606f\u5b89\u5168\u610f\u8bc6<\/section>\n
    \u5c06\u91cd\u8981\u4fe1\u606f\uff08\u5bc6\u7801\u3001\u5bc6\u94a5\uff09\u63a8\u9001\u5230\u516c\u5171 GitHub \u4ed3\u5e93\u662f\u4e00\u4e2a\u666e\u904d\u5b58\u5728\u7684\u5b89\u5168\u95ee\u9898\u3002\u5982\u679c\u4f60\u60f3\u4e86\u89e3\u8fd9\u4e2a\u95ee\u9898\u7684\u4e25\u91cd\u6027\uff0c\u8bf7\u67e5\u770b qz(https:\/\/qz.com\/674520\/companies-are-sharing-their-secret-access-codes-on-github-and-they-may-not-even-know-it\/) \u4e0a\u7684\u8fd9\u7bc7\u6587\u7ae0\u3002\u4e92\u8054\u7f51\u4e0a\u6709\u4e13\u95e8\u7684\u722c\u866b\u673a\u5668\u4eba\u7b49\u5f85\u7740\u4f60\u72af\u8fd9\u6837\u7684\u9519\u8bef\u3002\u4ece\u6211\u7684\u7ecf\u5386\u6765\u770b\uff0c\u5b89\u5168\u8fd9\u4e00\u8bfe\u9898\u51e0\u4e4e\u4ece\u6765\u6ca1\u6709\u5728\u6570\u636e\u79d1\u5b66\u7684\u76f8\u5173\u8bfe\u7a0b\u4e2d\u63d0\u5230\u8fc7\u3002\u6240\u4ee5\uff0c\u4f60\u9700\u8981\u81ea\u5df1\u6765\u586b\u5145\u8fd9\u65b9\u9762\u77e5\u8bc6\u7684\u7a7a\u767d\u3002\u6211\u5efa\u8bae\u9996\u5148\u53bb\u4e86\u89e3\u4e00\u4e0b\u64cd\u4f5c\u7cfb\u7edf\u7684\u73af\u5883\u53d8\u91cf\u76f8\u5173\u77e5\u8bc6\uff0cdev.to(https:\/\/dev.to\/biplov\/handling-passwords-and-secret-keys-using-environment-variables-2ei0) \u7684\u8fd9\u7bc7\u6587\u7ae0\u5c31\u662f\u4e00\u4e2a\u5f88\u597d\u7684\u5f00\u59cb\uff0c\u5f3a\u70c8\u63a8\u8350\u5927\u5bb6\u9605\u8bfb\u5b66\u4e60\u3002<\/p>\n
    \u4f5c\u8005\u4ecb\u7ecd<\/strong><\/p>\n
    Gerold Csendes\uff0c\u73b0\u5c31\u804c\u4e8e EPAM\uff0c\u6570\u636e\u79d1\u5b66\u5bb6\uff0c\u673a\u5668\u5b66\u4e60\u5de5\u7a0b\u5e08\u3002<\/p>\n
    <\/div>\n
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    <\/div>\n","protected":false},"excerpt":{"rendered":"
    \u4f5c\u8005 | Gerold Csendes \u8bd1\u8005 | \u738b\u5764\u7965 \u7b56\u5212 | \u51cc\u654f \u672c\u6587\u4f5c\u8005\u4ecb\u7ecd\u4e86\u6570\u636e\u79d1\u5b66\u5bb6\u5728\u7f16\u5199\u4ee3\u7801\u65f6 […]<\/p>\n
    <\/div>\n
    <\/g><\/svg><\/i> \"Loading\"<\/p>\n
    <\/div>\n","protected":false},"author":1,"featured_media":1198,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-1197","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai"],"_links":{"self":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/1197","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/comments?post=1197"}],"version-history":[{"count":1,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/1197\/revisions"}],"predecessor-version":[{"id":1201,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/posts\/1197\/revisions\/1201"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/media\/1198"}],"wp:attachment":[{"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/media?parent=1197"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/categories?post=1197"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aif.amtbbs.org\/index.php\/wp-json\/wp\/v2\/tags?post=1197"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}