数据科学家常犯的15个编码错误

{"type":"doc","content":[{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"本文作者介绍了数据科学家在编写代码时常犯的几个错误，并给出了自己对问题的看法以及相应的解决方案。希望文中的观点能给读者带来一些启发。"}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"编写应用于数据科学项目的Python代码，并按照自己的期望运行起来，可能没有什么困难。但是，如果你想让自己的代码对其他人（包括未来的自己）有高可读性，并且可重现及运行时维持高效率，可能就没那么容易了。我们可以通过减少开发中常见的不良做法来解决这个问题。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在我从事数据科学的职业生涯中，我逐渐意识到，通过应用软件工程的最佳实践，可以交付质量更高的项目。高质量的项目意味着极少的错误、可复现准确结果以及高效的代码执行效率。本文不会事无巨细地向你介绍这些最佳实践。相反，我总结了几点开发中最常见到的问题（也是我自己之前经常犯的错误），并有针对性地给出相应的解决方法及其相关学习资料。"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"1. 没有配置独立的开发环境"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"从某一方面来看，这可能不是编码问题，但我仍然坚持认为独立的运行环境是代码健康运行的保证。我认为要给每个项目配置独立的专用环境，这样才能保证代码的可重现性。项目代码未来可能会运行在你的电脑上，或者是你同事的电脑上，甚至有可能部署到生产环境中。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"如果你不清楚什么是依赖管理，那么最好先了解和学习下 "},{"type":"link","attrs":{"href":"https:\/\/docs.conda.io\/projects\/conda\/en\/latest\/user-guide\/tasks\/manage-environments.html","title":null,"type":null},"content":[{"type":"text","text":"Anaconda Virtual Environment"}]},{"type":"text","text":" 以及 "},{"type":"link","attrs":{"href":"https:\/\/realpython.com\/pipenv-guide\/","title":null,"type":null},"content":[{"type":"text","text":"Pipenv"}]},{"type":"text","text":"。我个人最常使用Anaconda，你可以点击"},{"type":"link","attrs":{"href":"https:\/\/towardsdatascience.com\/a-guide-to-conda-environments-bc6180fc533","title":null,"type":null},"content":[{"type":"text","text":"链接"}]},{"type":"text","text":"学习下入门教程。如果你想进阶或者进行工程化实践，那么可以考虑使用Docker。"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"2. 过度使用Jupyter Notebooks"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Notebooks非常适合用于教学以及初期项目研究，使用它可以快速完成一些小的棘手项目。尽管如此，它仍然不能算是一个好的IDE。工欲善其事必先利其器，好的IDE是数据科学家真正的武器，优秀的工具可以极大地提高你的工作效率。有很多大神指出过Notebooks的一些缺点，Joel Grus 曾经发表过一次"},{"type":"link","attrs":{"href":"https:\/\/www.youtube.com\/watch?v=7jiPeIFXb6U","title":null,"type":null},"content":[{"type":"text","text":"演讲"}]},{"type":"text","text":"，内容非常搞笑幽默，这里推荐给大家。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Notebooks非常适合项目前期的试验研究，而且可以非常方便地向他人展示研究成果，这一点非常不错。然而，当涉及到进行长周期、协作及可部署的项目时，它非常容易出错。这个时候，你最好使用一个专业的IDE，比如VScode、Pycharm、Spyder 等。在项目周期不超过一天的情况下，我也会时不时地使用一下Notebooks，这可能是我想到的唯一使用它的场景了。"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"3. 项目代码结构混乱"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"我见过不少人将项目的所有代码以及相关文件存储在一个目录里，这是一个十分不专业的做法。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"如下图所示，想象一下你要接手一个项目，你更喜欢哪种项目代码结构。图片中右面的项目代码结构绝对会让你和其他接盘侠疯掉的，因为这会让你花费数倍的时间来研究项目代码。毋庸置疑，左边的代码结构要比右边合理许多。所以，我们应该怎么构建项目结构呢？这里推荐给大家一个工具—— "},{"type":"link","attrs":{"href":"https:\/\/drivendata.github.io\/cookiecutter-data-science\/","title":null,"type":null},"content":[{"type":"text","text":"Cookiecutter"}]},{"type":"text","text":"，这是一个十分优秀的开源项目，它促进了数据科学项目代码结构的标准化，你可以从中学习一下。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.infoq.cn\/resource\/image\/bd\/0d\/bd3f998b32c762f7874718499e83a40d.png","alt":null,"title":"","style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":"","fromPaste":false,"pastePass":false}},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"4. 项目代码使用绝对路径而不是相对路径"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"你有在个人开源的项目中有看到过“请修复你的文件路径”的评论吗？这样的评论往往暗示了糟糕的代码设计。修复该问题一般包括两个步骤："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"与他人共享项目结构（参考本文第三条建议）"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"将你的IDE根目录\/工作目录设置为项目根目录，该目录通常是项目中最外层目录。"}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"第二点有时候不是那么简单，但是值得你花时间这么去做，这样别人就可以在不用修改代码的情况下成功运行你的代码。下面给出一个例子供大家参考。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"codeblock","attrs":{"lang":"python"},"content":[{"type":"text","text":"import pandas as pd\nimport numpy as np\nimport os\n#### BAD WAY ####\n# please change it to your file path\nexcel_path1 = \"C:\\\\Users\\\\gerold\\\\Desktop\\\\CEU\\trim1\\\\DataEng1\\\\Team_asgn\\\\CrimeOneYearofData_2006.xlsx\"\nexcel_path2 = \"C:\\\\Users\\\\gerold\\\\Desktop\\\\CEU\\trim1\\\\DataEng1\\\\Team_asgn\\\\CrimeOneYearofData_2007.xlsx\"\n# read in excel\nmydf = pd.read_excel(excel_path1)\nmyd2 = pd.read_excel(excel_path2)\n#### END BAD WAY ####\n#### GOOD WAY ####\n# first put your 2 excels into the data folder\n# set the working directory in your IDE to the root (Team_asgn)\nDATA_DIR = \"data\" # indicate magical constansts (maybe rather put it on the top of the script)\n# fix gruesome var namescrime06_filename = \"\nCrimeOneYearofData_2006.xlsx\"crime07_filename = \"\nCrimeOneYearofData_2007.xlsx\"\n# fix gruesome var names\ncrime06_df = pd.read_excel(os.path.join(DATA_DIR, crime06_filename))\ncrime07_df = pd.read_excel(os.path.join(DATA_DIR, crime07_filename))\n#### END GOOD WAY ####"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"5. 使用“幻数”"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"幻数是在代码中没有任何上下文的数字。代码中频繁大量地使用幻数，可能会遇到难以追踪的问题。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"下面的代码示例中，我们在乘法计算时简单地使用了一个未分配变量的数字，而且没有任何上下文来解释这个数字的含义。如果你以后不得不对其进行修改，就会面临十分尴尬的局面，因为你不知道该数字的具体含义。因此，对于此类常量，按照惯例在Python中使用大写命名。当然你也可以坚持不使用大写，但是将“常量”与“常规变量”区分开来，是一个不错的编程习惯。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"codeblock","attrs":{"lang":"python"},"content":[{"type":"text","text":"# assign revenues in $ to marketing campaigns\ncamp1_revenue = 50000\ncamp2_revenue = 100000\n#### BAD WAY ####\n# calc whic performed better\ncamps_revenue_diff = (camp2_revenue * 0.65) - camp1_revenue\n#### END BAD WAY ####\n#### GOOD WAY ####\nCAMP2_NORMALIZER = 0.65 # we need to normalize because the campaign ran in peak season\n# calc whic performed better\ncamps_revenue_diff = (camp2_revenue * CAMP2_NORMALIZER) - camp1_revenue\n#### END GOOD WAY ####"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"6. 不处理告警信息"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"估计很多人都有这样的习惯：对代码运行过程中产生的告警信息置之不理。我们对代码能够正常运行并能够输出期望的结果已经非常满意了，所以为什么要处理告警信息呢？确实，告警信息不是错误，但是这些告警信息可能会引起潜在的问题或者错误。尽管代码能运行成功，但出现这些告警信息实际上并不符合我们的预期。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在做数据分析时，我遇到的最常见的告警信息是Pandas 的 SettingwithCopyWarning 和 DeprecationWarning。"},{"type":"link","attrs":{"href":"https:\/\/www.youtube.com\/watch?v=4R4WsDJ-KVc","title":null,"type":null},"content":[{"type":"text","text":"DataSchool"}]},{"type":"text","text":" 的教学视频以简洁的方式解释了如何触发SettingwithCopyWarning。DeprecationWarning 告警说明 Pandas 已弃用某些方法，未来你的项目代码在使用更高版本时会有中断的风险。当然，还有一些其他的告警类型。依照我的经验，产生这些告警大部分是因为使用了工具类非原本设计的调用方式。所以，了解函数的源代码总是有帮助的，这样就可以避免大多数的异常告警了。"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"7. 不使用类型注解"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"这也是我最近学到的一种做法，因为我已经体会到了使用类型注解带来的好处。类型注解（或类型提示）简单来讲就是为变量指定数据类型。基本上，使用IDE自带的代码扩展提示就可以完成代码变量的注解。使用代码注解，可以让你的代码更易于自己和他人阅读。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"为了证明这一点，我摘取了Daniel Starner在"},{"type":"link","attrs":{"href":"https:\/\/dev.to\/dstarner\/using-pythons-type-annotations-4cfe","title":null,"type":null},"content":[{"type":"text","text":"dev.to"}]},{"type":"text","text":"博客中的代码片段来举个例子。如下代码所示，在没有类型提示的情况下，mystery_combine() 使用整数或字符串作为输入并相应地返回整数或字符串作为结果。对于开发人员来讲，该方法的描述有点模棱两可。如果使用了类型注解，就可以清晰的表达函数意图，避免产生误解，同时会给其他开发人员以及未来的自己带来一些便利。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"codeblock","attrs":{"lang":"python"},"content":[{"type":"text","text":"# code taken from https:\/\/dev.to\/dstarner\/using-pythons-type-annotations-4cfe\n# Our original function\ndef mystery_combine(a, b, times):\nreturn (a + b) * times\nprint(mystery_combine(2, 3, 4))\n# 20\nprint(mystery_combine('Hello ', 'World! ', 4))\n# Hello World! Hello World! Hello World! Hello World!\n# show your intents explicitly by indicating types of your argument and returned value\ndef mystery_combine(a: str, b: str, times: int) -> str:\nreturn (a + b) * times"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"此外，使用类型注解可以在无需运行代码的情况下，静态地检查代码是否存在错误。下图的示例展示了没有按函数类型注解指定对应参数，静态检查给出了相应的错误提示。静态检查是在运行项目之前进行代码预检查的一种十分有用的方法。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.infoq.cn\/resource\/image\/f2\/2f\/f2b715b3ce4f432604bbb29524457b2f.png","alt":null,"title":"","style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":"","fromPaste":false,"pastePass":false}},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"8. 不习惯使用列表推导表达式"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"列表推导表达式是Python非常强大的特性之一。使用列表推导表达式，可以让for循环更加易于阅读，更符合Python的习惯表达，而且执行效率会更高。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"下面的一段示例代码尝试读取目录中的CSV文件。在这种情况下你可能会说，不使用列表推导式也挺优雅呀，没有什么不妥。但是，如果目录里有其他格式的文件，比如JSON文件，此时，使用列表表达式的便捷性和可读性会提升一个档次，而且，代码也更容易维护。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"codeblock","attrs":{"lang":"python"},"content":[{"type":"text","text":"import pandas as pd\nimport os\nDATA_PATH = \"data\"\nfilename_list = os.listdir(DATA_PATH)\n#### BAD WAY ####\n# read in bunch of csv-s from a dir\ncsv_list = []\nfor fileaname in filename_list:\ncsv_list.append(pd.read_csv(os.path.join(DATA_PATH, filename)))\n#### END BAD WAY ####\n#### GOOD WAY ####\ncsv_list = [pd.read_csv(os.path.join(DATA_PATH, filename)) for filename in filename_list]\n# what about if not only .csv-s are present? easy to tackle this with list comprehensions\ncsv_list = [\npd.read_csv(os.path.join(DATA_PATH, filename)) for filename in filename_list if filename.endswith(\".csv\")]\n#### END GOOD WAY ####"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"9. pandas代码可读性差"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"方法链调用是 Pandas 中的一个很棒的特性，但是如果你坚持在一行中表达所有内容，代码的可读性会变差。有一个技巧可以让你对表达式进行分解。如下的代码所示，可以将整个表达式放入括号中，然后表达式的每个组成部分可以单独使用一行，这样处理后的代码看起来就清爽多了。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"codeblock","attrs":{"lang":"python"},"content":[{"type":"text","text":"# lets aggregate click and time spent to its mean in a Q\nvar_list = [\"clicks\", \"time_spent\"]\nvar_list_Q = [varname + \"_Q\" for varname in var_list]\n#### BAD WAY ####\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)))\n#### BAD WAY ####\n#### GOOD WAY ####\ndf_Q = (\n df\n .groupby(\"id\")\n .rolling(window=3, min_periods=1, on=\"yearmonth\")[var_list]\n .mean()\n .reset_index()\n .rename(columns=dict(zip(var_list, var_list_Q))))\n#### END GOOD WAY ####"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"10. 排斥使用Python自带的date工具"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在Python中使用日期模块确实不是特别友好，因为它的语法比较奇怪，而且让人难以理解并记忆。我经常看到很多人像处理数字一样处理日期对象，这种做法实在不够优雅。虽然很多时候这么做能够跑通代码，但是这样非常容易出错，而且维护起来非常困难。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"以下面的实例代码为例，它的功能是实现以 %Y%m 格式列出两个日期之间的所有月份。如果你借助datetime工具实现，代码可读性和可维护性得到了提高。实话讲，即使是现在，我在处理日期问题时仍然依赖谷歌搜索，这很正常，习惯就好了。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"codeblock","attrs":{"lang":"python"},"content":[{"type":"text","text":"import datetime\nfrom dateutil.relativedelta import relativedelta\n# task: get months between two dates in YM format\n#### BAD WAY ####\nstart_num = 201910\nend_num = 202012\nres_list = []\niter_num = start_num\nwhile iter_num < end_num:\nif abs(iter_num) % 100 > 12:\n iter_num += 88\n res_list.append(iter_num)\n iter_num += 1\nelse:\n res_list.append(iter_num)\n iter_num += 1\nres_list.append(iter_num)\n#### END BAD WAY ####\n#### GOOD WAY ####\n# initialize datetimes\nstart_datetime = datetime.datetime(2019, 10, 1)\nend_datetime = datetime.datetime(2020, 12, 1)\n# find months between end and astart date\nr = relativedelta(end_datetime, start_datetime)\nmonths_between = r.months + (12*r.years)\nmyres = [\n start_datetime + relativedelta(months=_)\nfor _ in range(1, months_between + 1)]\n# format dates\nmyres = [element.strftime(\"%Y%m\") for element in myres]\n#### END GOOD WAY ####"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"11. 变量命名不规范"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在循环中给变量使用i，j，k，df等非描述性字符进行命名，会使代码的可读性降低，尤其是循环中的逻辑处理较为复杂的时候。代码中变量命名短小精悍，往往容易混淆项目开发人员，这一点相信大家深有体会。不要担心使用较长的变量名，也不要吝啬使用下划线“_”对变量进行命名。推荐给大家一篇有关"},{"type":"link","attrs":{"href":"https:\/\/towardsdatascience.com\/data-scientists-your-variable-names-are-awful-heres-how-to-fix-them-89053d2855be","title":null,"type":null},"content":[{"type":"text","text":"变量命名"}]},{"type":"text","text":"的高质量博客，一定会对你有所启发。"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"12. 不对代码进行模块化重构"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"模块化意味着将冗长且复杂的代码分解成简单的模块，以执行细粒度的、特定的任务。不要只为项目创建一个冗长的执行脚本。在代码入口文件开头定义大量的类或函数是不推荐的做法，因为这样做代码很难阅读和维护。相反，要根据代码功能创建相应的模块（包）。这方面的详细内容，可以参考这篇博客 "},{"type":"link","attrs":{"href":"https:\/\/realpython.com\/courses\/python-modules-packages\/","title":null,"type":null},"content":[{"type":"text","text":"Python Modules and Packages"}]},{"type":"text","text":" 。"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"13. 没有遵循PEP约定"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"当我刚开始使用Python编写项目代码的时候，写出的代码十分丑陋，难以阅读。并且自己还努力地制定属于自己的设计原则，好让自己的代码看起来没有那么糟糕。想出这些原则花费了我不少时间，但是我并没有一直坚持这些原则，回想起来，受限于自己在Python的经验，很多自己设计原则没有那么合理。最终，我发现了"},{"type":"link","attrs":{"href":"https:\/\/www.python.org\/dev\/peps\/","title":null,"type":null},"content":[{"type":"text","text":"PEP"}]},{"type":"text","text":"设计原则，它是Python的官方约定指南。我很喜欢PEP提出的约定，因为它可以标准化我的代码，从而使协作编程更加方便。顺便说一下，有些特殊情况下我确实没有按照PEP规则来做，但在绝大多数情况下，我会按照PEP规范来写代码。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"几乎所有的IDE都支持linter扩展，下图展示了linter的工作原理，它可以指出代码中存在的问题。如果你仍然感觉不够直观，你可以查看具体的PEP索引提示，如括号中提示所示。如果你想查看有哪些可用的linter，可以参考"},{"type":"link","attrs":{"href":"https:\/\/realpython.com\/python-code-quality\/#linters","title":null,"type":null},"content":[{"type":"text","text":"realpythong.org"}]},{"type":"text","text":" 网站上的学习资源。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.infoq.cn\/resource\/image\/2d\/bc\/2d65f64fb7945c9d152f77fc6a7690bc.png","alt":null,"title":"","style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":"","fromPaste":false,"pastePass":false}},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"14. 从不使用编码助手"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"如果你想大幅提高写代码的效率，那么就开始使用编码助手吧。该工具可以巧妙地帮助你自动完成代码、添加描述文档以及给你的代码提供修改建议。我最喜欢使用的编码提示工具是由微软开发的pylance，它支持在VScode中使用。Kite是另一个比较流行的编码助手，同样非常好用，许多编辑器都支持使用。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"代码提示工具的使用效果视频可以点击"},{"type":"link","attrs":{"href":"https:\/\/thumbs.gfycat.com\/BaggyNiceLemur-mobile.mp4","title":null,"type":null},"content":[{"type":"text","text":"此处"}]},{"type":"text","text":"进行查看。"}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"15. 缺少信息安全意识"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"将重要信息（密码、密钥）推送到公共 GitHub 仓库是一个普遍存在的安全问题。如果你想了解这个问题的严重性，请查看 "},{"type":"link","attrs":{"href":"https:\/\/qz.com\/674520\/companies-are-sharing-their-secret-access-codes-on-github-and-they-may-not-even-know-it\/","title":null,"type":null},"content":[{"type":"text","text":"qz"}]},{"type":"text","text":" 上的这篇文章。互联网上有专门的爬虫机器人等待着你犯这样的错误。从我的经历来看，安全这一课题几乎从来没有在数据科学的相关课程中提到过。所以，你需要自己来填充这方面知识的空白。我建议首先去了解一下操作系统的环境变量相关知识，"},{"type":"link","attrs":{"href":"https:\/\/dev.to\/biplov\/handling-passwords-and-secret-keys-using-environment-variables-2ei0","title":null,"type":null},"content":[{"type":"text","text":"dev.to"}]},{"type":"text","text":"的这篇文章就是一个很好的开始，强烈推荐大家阅读学习。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"作者介绍"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Gerold Csendes，现就职于EPAM，数据科学家，机器学习工程师。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"原文链接"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"link","attrs":{"href":"https:\/\/www.kdnuggets.com\/2021\/03\/15-common-mistakes-python.html","title":null,"type":null},"content":[{"type":"text","text":"15 common mistakes data scientists make in Python (and how to fix them)"}]}]}]}