May 2024
Intermediate to advanced
357 pages
5h 3m
Chinese
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第6章 运用税务数据进行应用导向的数据分析（Python）
这一章包含以下主要内容。
- 准备高收入数据集的分析
- 导入并熟悉世界各国高收入数据集
- 分析并可视化美国的高收入数据集
- 进一步分析美国的高收入阶层
- 用Jinja2汇报结果
简介
到目前为止，我们主要采用了一种基于R语言的实用的数据分析方法。据此我们可以相对容易地回答关于某个数据集的问题、对数据建模，并且输出可视化结果。因此，对于快速建立模型原型并进行分析，R语言是不错的选择；它是一种为统计数据分析量身定制而用于该特定领域的语言。
在本书的下半部分，我们会介绍另一种分析的方法，它更加适合产品环境和应用。数据科学往往包含假设建立、数据的获取、清洗及转换、数据分析、模型建立、可视化与应用等一系列流程，而上述流程无论如何都不会是一个简单而线性的过程。此外，当数据分析需要以自动的方式大规模地重复运行时，很多新的考虑和需求自然也会应运而生。因此，很多的数据应用需要一个实用性很广的工具。这个工具需要保有快速建立模型原型的能力，在所有的系统中都能广泛采用，并且能为一系列的计算应用（包括网络应用、数据操作、科学计算等）提供全面的支持。基于上述要求，Python成为应用级数据分析工具的有力竞争者。
与R语言很类似，Python是一种直译式语言（有时也被称为脚本语言）。它不需要特殊的集成开发环境（IDE）或者软件编译工具，因此开发和建立模型原型的速度与R语言一样快。与R语言一样，它也使用了C语言共享对象来改善计算性能。此外，Python还是Linux、UNIX和Mac OS X的默认系统工具的一种，并且Windows下也得到支持。Python可以说是“自带电池”：它的标准程序库中广泛地包括了从多进程到压缩工具的功能模块。正是如此，作为一个灵活的计算工具，可以适用于任何问题领域。如果你需要标准程序库之外的程序库，Python与R语言一样也提供程序库管理工具来下载和安装其他的程序库。 ...
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