July 2017
Intermediate to advanced
398 pages
11h 54m
Chinese
Content preview from Python数据处理
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302
｜
第
14
章
•
以特定的时间间隔重复失败的任务。
•
确保你的代码有很多的
try...except
代码块，让它能够处理错误。
•
在处理到机器、数据库或
API
的连接的代码周围，构建特殊的异常代码块。
•
定期维护和监控你为自动化使用的机器。
•
使用测试数据定期测试你的任务和自动化程序，确保它们正常执行。
•
注意脚本中出现的依赖、竞争条件和
API
规则，根据这些知识编写代码。
•
使用类似
requests
和
multiprocessing
的库，让困难的问题变得更简单，并尝试理解使
脚本运行困难的问题所在。
我们会使用这其中的一些技术和想法，研究如何最好地监控和自动化脚本。现在，让我们
讨论一下自动化中使用的工具，使数据处理更加容易和简单，并给出一些在哪里以及如何
使用这些工具的建议。
14.4
在哪里自动化
根据脚本需求的不同，决定在哪里运行它是重要的第一步。无论脚本在哪里第一次执行，
你都可以将它移到别的地方，但是这可能需要重写一些代码。在一开始，你可能需要它在
本地运行。在
本地
运行脚本或任务就是在你自己的计算机上运行。
远程
执行意味着在另外的机器上运行，可能是其他地方的服务器。一旦脚本运行成功并且
测试良好，你会想要移动它到远程。如果你管理或拥有服务器，或者为一个有服务器的组
织工作，这样迁移脚本到服务器上会相对简单。这允许你在自己的机器（笔记本电脑或台
式计算机）上工作，并且不用担心在什么时间关闭和打开它。在远程运行脚本也意味着对
你的网络服务提供商来说，你不是独立的。
如果无法访问服务器，但是有一台旧的不再使用的计算机或笔记本电脑，在必要的情况 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.