November 2023
Intermediate to advanced
512 pages
11h 53m
Chinese
Content preview from 利用 Python 进行数据分析：原书第 3 版
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数据分析案例
|
411
现在，将三个表合并，并按分类进行分组：
13.3 1880
—
2010
年间全美婴儿姓名
美国社会保障管理局（
SSA
）提供了一份从
1880
年到现在的婴儿名字频率数据。
Hadley
Wickham
（多款流行
R
包的作者）经常用这份数据来演示
R
的数据处理功能。
我们要做一些数据规整才能加载这个数据集，完成规整后会得到一个如下的
DataFrame
：
412
|
第
13
章
你可以用这个数据集做很多事，例如：
·
根据给定名字，对该名字随时间的比例进行可视化。
·
确定某个名字的相对排名。
·
判断各年度最流行的名字，或者流行程度增长或减少最多的名字。
·
分析名字趋势：元音、辅音、长度、总体多样性、拼写变化、首尾字母等。
·
分析外源性趋势：圣经中的名字、名人、人口结构变化等。
利用本书介绍的工具能轻松地完成这些分析工作，我会讲解其中的一些。
在写作本书时，美国社会保障管理局将该数据库按年度制成了多个数据文件，其中给
出了每个性别
/
名字组合的出生总数。读者可以下载原始档案（
http://www.ssa.gov/oact/
babynames/limits.html
）。
如果读者读到此处时，这个页面已经不见了，可以用搜索引擎进行网络查找。下载“国
家数据”文件
names.zip
并将其解压，路径中含有一组文件 ...
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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.