July 2017
Intermediate to advanced
398 pages
11h 54m
Chinese
Content preview from Python数据处理
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数据探索和分析
｜
197
9.2.4
将结论写成文档
当你发现一些结论和更多想研究的问题之后，应该开始将你的工作成果写成文档。作为文
档和最终展示成果的一部分，你需要对使用的数据源和分析的数据数量了如指掌。在我们
的问题中，我们只研究了大约
90
个数据点，但它们代表了我们想要研究的部分。
你可能会发现你关注的数据集比预期的小。只要你清楚你使用的方法和使用小数据集的原
因，就不会把听众和报告引入歧途。在下一章，我们会更深入地探究如何报告发现，将我
们的想法和工作写成文档，把我们的想法和世界分享。
9.3
小结
在这一章，我们使用了一些新的
Python
库和技术，探索和分析了我们的数据集。你已经能
够导入数据、联结数据、分组数据，并且基于发现创造新的数据集。
现在你可以使用统计学方法来找到离群值，衡量数据之间的相关性。你可以通过分离有趣
的分组，并且深入数据探索之中，确定清晰的、可回答的问题来研究。如果你曾经使用过
IPython
和
%store
来保存变量，在下一章，我们会用这个命令做更多的交互。
现在你应该能够：
•
使用
agate
库评估你的数据；
•
确定哪些事，如果有的话，在数据中是至关重要的；
•
找到数据中的入手点或一部分的数据来做深入研究，得到结论；
•
通过分析和探索数据挑战你的假设。
本章中涉及的新概念和库总结在表
9-2
中。
表9-2：新的Python编程概念和库
概念/库 功能
agate
库 使数据分析变得简单，能够从
CSV
数据中读取数据，创建供分析
的表，运行基本的数据分析函数，在数据集上应用过滤器，洞察
数据 ...
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